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chapter7/openvla-robotwin2-eval/README.md

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+# 实验 7-13：OpenVLA + RoboTwin2 具身智能评估
+
+本目录是第六章实验 7-13 的严格复现实验，不把论文数字、历史视频或命令 dry-run 当作本机结果。实验直接调用固定 commit 的 SimpleVLA-RL 上游 `trainer.val_only=True` 路径，在 `move_can_pot` 的同一组 IID/OOD 种子上对比 action chunk 1 与 25。
+
+## 正式结果
+
+正式单卡运行 `exp7-13-localgpu-20260803-v1` 已完成两个 arm 各 128 IID +
+128 OOD episodes，并通过严格验收。`chunk_1` 为 0/256；`chunk_25` 为
+26/256（IID 13/128、OOD 13/128），配对成功率提高 10.15625 个百分点。
+这是一项完整的负面/低成功率结果，不把实验完成误写成模型表现良好。其余
+486 个失败均在 200 action steps 上限结束，逐项绑定同次进程窗口内的 MP4 并
+标为 `timeout`。汇总、逐 episode 记录、注释、运行身份以及 512 个外部视频
+的内容哈希见 [`validation/runs/exp7-13-localgpu-20260803-v1/`](validation/runs/exp7-13-localgpu-20260803-v1/)。
+
+## 来源、版本与所有权边界
+
+本实验所调用的上游训练/评估实现是 [`PRIME-RL/SimpleVLA-RL`](https://github.com/PRIME-RL/SimpleVLA-RL/tree/7c51662df27b586f9e8a1ab35fcf849f2b8852f9)，固定提交为 `7c51662df27b586f9e8a1ab35fcf849f2b8852f9`，本地路径为 `chapter7/SimpleVLA-RL/SimpleVLA-RL`。
+
+本目录中的 `experiment.py`、`config.json`、`task_config_exp7_13_three_view.yml` 和 `instrument_upstream.py` 是**本书自有的编排、协议与观测插桩**，不是 OpenVLA-OFT、RoboTwin2 或 SimpleVLA-RL 上游源码。它们使本书能够检查三视角、种子、episode 证据与严格完成门禁，但不会提供下列外部运行输入：
+
+- 真实 OpenVLA-OFT checkpoint（应记录模型身份、revision 与文件哈希）
+- 真实 RoboTwin2 checkout 及其明确 revision
+- RoboTwin2 simulator、assets、系统库和可运行的 Linux/CUDA 环境
+- 至少一张显存足以容纳 7B checkpoint 的兼容 NVIDIA GPU；多卡可提高吞吐，但不是 `val_only` 语义门禁
+
+固定 SimpleVLA-RL checkout **不等于**上述依赖已经随仓库提供，也不能据此宣称 OpenVLA-OFT/RoboTwin2 运行栈已被完整固定。
+
+## 与正文逐项对应
+
+| 正文要求 | 本目录的直接证据 |
+| --- | --- |
+| OpenVLA / OpenVLA-OFT 架构 | 固定上游 commit、真实预训练 checkpoint 与上游生成路径写入 preflight/launch manifest |
+| RoboTwin2 环境 | 要求真实 `ROBOTWIN2_PATH`，由上游安装脚本装入一次性 worktree，不修改干净 checkout |
+| 三视角 RGB + 14 维关节状态 | 专用 task config 开启头部和左右腕部相机；launch 强制 `num_images_in_input=3` 和 `use_proprio=True` |
+| 14 维动作 | launch 与逐 episode 证据同时校验 `action_token_len=14` |
+| `move_can_pot` 随机化和空间约束 | preflight 直接检查真实任务实现、随机化配置与 OOD seed inventory |
+| 运行预训练模型评估 | 两个 arm 都走上游 `val_only=True`，每 arm 包含 128 IID + 128 OOD episodes |
+| 成功率 | 只接受 RoboTwin2 `eval_success` 经 reward manager 记录的布尔结果 |
+| 完成时间 | 记录每条轨迹的 action steps，并按上游 50 Hz 控制频率报告 mean/std/min/p50/p95/max；不把 MP4 播放时长冒充执行时间 |
+| 失败模式 | 每个失败 episode 必须有受控标签、具体观察证据和对应 rollout 视频，未分类即不完成 |
+| 动作分块影响 | 相同任务、模型、IID/OOD seeds 下配对比较 chunk 1 与 chunk 25；由于 checkpoint head 固定预测 25 个动作，插桩在执行前显式截取配置长度的动作前缀与对应 action tokens |
+
+当前上游默认脚本只设置一张 head-camera 图像；这不足以证明正文所述三视角观察空间。本实验的专用配置显式开启左右腕部相机，并把三张图送入上游已有的三视角分支。
+
+## 运行
+
+需要 Linux、兼容 NVIDIA GPU、RoboTwin2 checkout 和真实预训练 OpenVLA-OFT checkpoint：
+
+```bash
+export ROBOTWIN2_PATH=/abs/path/to/RoboTwin2
+export OPENVLA_CHECKPOINT=/abs/path/to/openvla-oft-robotwin2-checkpoint
+# 可选；默认使用上游 align.json
+export SIMPLEVLA_ALIGN_PATH=/abs/path/to/align.json
+
+python experiment.py preflight
+python experiment.py prepare --run-dir runs/move-can-pot-real
+python experiment.py launch --run-dir runs/move-can-pot-real --arm all
+python experiment.py analyze \
+  --run-dir runs/move-can-pot-real \
+  --failure-annotations runs/move-can-pot-real/failure_annotations.json
+```
+
+正式运行完成后生成并复核可提交的证据包：
+
+```bash
+python finalize_evidence.py finalize
+python finalize_evidence.py verify
+```
+
+checkpoint 与 512 个 MP4 不随 Git 分发；manifest 保存其不可变身份与 hash。
+`verify` 在 clean clone 中复核所有提交内的源码和证据文件，并检查 manifest
+确实绑定 512 个视频身份，而不会把缺少大体积外部媒体误报为重新执行成功。
+
+`prepare` 创建 detached、一次性的 instrumented git worktree，在那里安装 RoboTwin2 和 episode recorder；`chapter7/SimpleVLA-RL/SimpleVLA-RL` 本身保持干净。两个真实 arm 的完整命令、环境、commit 和输出路径保存在 `launch_manifest.json`。
+
+失败注释以 `arm|data_source|trial_seed` 为键，例如：
+
+```json
+{
+  "chunk_25|robotwin2_move_can_pot_eval_ood|100100123": {
+    "failure_mode": "placement_position_error",
+    "evidence": "Rollout final frame shows the can released 0.11 m beyond the allowed x/y target tolerance."
+  }
+}
+```
+
+可用失败类型由 `config.json` 固定。缺视频、缺注释、少一个 seed、非上游 `val_only` 数据、维度不符、任一进程失败，都会让 `strict_completion.complete` 保持 `false`。
+
+## 当前执行主机
+
+当前主机提供一张 96 GiB NVIDIA RTX PRO 6000 Blackwell。实验保持 128 IID + 128 OOD、三视角、14 维状态/动作、视频和失败注释等全部语义门禁，仅把上游为训练吞吐设置的八卡资源参数调整为单卡 `val_only` 推理；运行清单会记录 GPU、driver、checkpoint revision 和每个 episode 的直接证据。

chapter7/openvla-robotwin2-eval/annotate_timeouts.py

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+#!/usr/bin/env python3
+"""Create auditable timeout annotations from complete Experiment 7-13 evidence.
+
+This helper only labels an episode when the upstream recorder says it failed at
+the configured action horizon and an exact failure-video match can be probed.
+It refuses early failures, missing videos, duplicate videos, and malformed MP4s
+instead of guessing a visual failure mechanism.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+import subprocess
+from typing import Any
+
+import experiment
+
+
+def video_metadata(path: Path) -> dict[str, Any]:
+    proc = subprocess.run(
+        [
+            "ffprobe",
+            "-v",
+            "error",
+            "-select_streams",
+            "v:0",
+            "-show_entries",
+            "stream=nb_frames,duration,width,height",
+            "-of",
+            "json",
+            str(path),
+        ],
+        check=False,
+        text=True,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+    )
+    if proc.returncode:
+        raise RuntimeError(f"ffprobe failed for {path}: {proc.stderr.strip()}")
+    streams = json.loads(proc.stdout).get("streams", [])
+    if len(streams) != 1:
+        raise RuntimeError(f"expected one video stream in {path}, found {len(streams)}")
+    stream = streams[0]
+    required = {"nb_frames", "duration", "width", "height"}
+    if not required.issubset(stream):
+        raise RuntimeError(f"incomplete ffprobe metadata for {path}: {stream}")
+    return stream
+
+
+def build_annotations(
+    config: dict[str, Any],
+    run_dir: Path,
+    timeout_action_steps: int,
+) -> dict[str, dict[str, str]]:
+    manifest = experiment.load_json(run_dir / "launch_manifest.json")
+    annotations: dict[str, dict[str, str]] = {}
+    expected_episodes = (
+        int(config["iid_validation_seeds"])
+        + int(config["ood_validation_seeds"])
+    )
+    for chunk in config["action_chunks"]:
+        arm = f"chunk_{chunk}"
+        rows = experiment.read_jsonl(run_dir / arm / "episodes.jsonl")
+        if len(rows) != expected_episodes:
+            raise RuntimeError(
+                f"{arm} is incomplete: expected {expected_episodes} episodes, found {len(rows)}"
+            )
+        videos = experiment.video_index(
+            Path(manifest["arms"][arm]["rollout_directory"]),
+            started_at_utc=experiment.load_json(run_dir / arm / "process.json")[
+                "started_at_utc"
+            ],
+            ended_at_utc=experiment.load_json(run_dir / arm / "process.json")[
+                "ended_at_utc"
+            ],
+        )
+        for candidates in videos.values():
+            candidates.sort()
+        for row in rows:
+            if row.get("success"):
+                continue
+            observed_steps = int(row.get("finish_action_steps", -1))
+            if observed_steps != timeout_action_steps:
+                raise RuntimeError(
+                    f"refusing to infer timeout for {experiment.annotation_key(row)}: "
+                    f"observed {observed_steps} action steps"
+                )
+            task_file = (
+                f"{config['task']}_trial_{row['trial_id']}_seed_{row['trial_seed']}"
+            )
+            matches = videos.get((task_file, False), [])
+            if len(matches) != 1:
+                raise RuntimeError(
+                    f"expected one exact failure video for {experiment.annotation_key(row)}, "
+                    f"found {len(matches)}"
+                )
+            video_path = Path(matches[0])
+            metadata = video_metadata(video_path)
+            control_seconds = timeout_action_steps / float(config["control_hz"])
+            annotations[experiment.annotation_key(row)] = {
+                "failure_mode": "timeout",
+                "evidence": (
+                    f"Exact rollout {video_path.name} is a "
+                    f"{metadata['width']}x{metadata['height']} MP4 with "
+                    f"{metadata['nb_frames']} frames over {float(metadata['duration']):.3f}s. "
+                    f"The paired upstream episode recorder reports failure after "
+                    f"{observed_steps}/{timeout_action_steps} action steps "
+                    f"({control_seconds:.3f}s at {config['control_hz']} Hz) without eval_success."
+                ),
+            }
+    return annotations
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--config", type=Path, default=experiment.DEFAULT_CONFIG)
+    parser.add_argument("--run-dir", type=Path, required=True)
+    parser.add_argument("--timeout-action-steps", type=int, default=200)
+    parser.add_argument("--output", type=Path)
+    args = parser.parse_args()
+    if args.timeout_action_steps <= 0:
+        parser.error("--timeout-action-steps must be positive")
+    config = experiment.resolve_config(args.config.resolve())
+    run_dir = args.run_dir.resolve()
+    output = args.output.resolve() if args.output else run_dir / "failure_annotations.json"
+    annotations = build_annotations(config, run_dir, args.timeout_action_steps)
+    experiment.dump_json(output, annotations)
+    print(f"Wrote {len(annotations)} timeout annotations to {output}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

chapter7/openvla-robotwin2-eval/config.json

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+{
+  "experiment": "7-13",
+  "upstream_path": "../../chapter7/SimpleVLA-RL/SimpleVLA-RL",
+  "expected_upstream_commit": "7c51662df27b586f9e8a1ab35fcf849f2b8852f9",
+  "expected_robotwin2_commit": "13c3c47ff4312dd62484bcd51be034af55c062d1",
+  "checkpoint_id": "Haozhan72/twin2_move_can_pot_seed1k_sft_single_20k",
+  "expected_checkpoint_revision": "a1f0ac0aa32d31ffab7131b0eda3e11719f39ee5",
+  "task": "move_can_pot",
+  "task_suite": "robotwin2_move_can_pot",
+  "task_config": "exp7_13_three_view",
+  "checkpoint_env": "OPENVLA_CHECKPOINT",
+  "robotwin2_env": "ROBOTWIN2_PATH",
+  "align_path_env": "SIMPLEVLA_ALIGN_PATH",
+  "model_family": "openvla",
+  "vla": "openvla-oft",
+  "action_dimension": 14,
+  "action_token_length": 14,
+  "action_chunks": [1, 25],
+  "rgb_views": 3,
+  "proprio_dimension": 14,
+  "control_hz": 50,
+  "iid_validation_seeds": 128,
+  "ood_validation_seeds": 128,
+  "gpus_required": 1,
+  "failure_modes": [
+    "grasp_failure",
+    "transport_collision",
+    "placement_position_error",
+    "placement_orientation_error",
+    "gripper_release_failure",
+    "timeout",
+    "environment_error",
+    "other_observed_failure"
+  ]
+}

chapter7/openvla-robotwin2-eval/experiment.py

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+#!/usr/bin/env python3
+"""Exact companion for manuscript Experiment 7-13.
+
+The runner deliberately separates three things:
+1. a non-destructive host/upstream preflight;
+2. generation or execution of the two real upstream val-only arms; and
+3. strict analysis of episode-level evidence.
+
+Paper numbers, historical rollout collections and dry-run commands never satisfy
+the completion gate.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+import re
+import shutil
+import statistics
+import subprocess
+import sys
+from collections import Counter
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Iterable
+
+
+HERE = Path(__file__).resolve().parent
+DEFAULT_CONFIG = HERE / "config.json"
+VIDEO_RE = re.compile(
+    r"step=(?P<global_step>\d+)--task=(?P<task>.+?)--success=(?P<success>True|False)--ran=(?P<run>[^.]+)\.mp4$"
+)
+
+
+def utc_now() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def load_json(path: Path) -> dict[str, Any]:
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def dump_json(path: Path, value: Any) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(value, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
+
+
+def resolve_config(path: Path) -> dict[str, Any]:
+    cfg = load_json(path)
+    upstream = Path(cfg["upstream_path"])
+    if not upstream.is_absolute():
+        upstream = (path.parent / upstream).resolve()
+    cfg["upstream_path"] = str(upstream)
+    return cfg
+
+
+def run_capture(argv: list[str], cwd: Path | None = None) -> tuple[int, str]:
+    try:
+        proc = subprocess.run(
+            argv,
+            cwd=cwd,
+            check=False,
+            text=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+        )
+        return proc.returncode, proc.stdout.strip()
+    except FileNotFoundError as exc:
+        return 127, f"{type(exc).__name__}: {exc}"
+
+
+def gpu_inventory() -> tuple[list[dict[str, Any]], str | None]:
+    code, output = run_capture(
+        [
+            "nvidia-smi",
+            "--query-gpu=index,name,memory.total,driver_version",
+            "--format=csv,noheader,nounits",
+        ]
+    )
+    if code != 0:
+        return [], output or "nvidia-smi unavailable"
+    gpus = []
+    for line in output.splitlines():
+        parts = [part.strip() for part in line.split(",")]
+        if len(parts) >= 4:
+            gpus.append(
+                {
+                    "index": int(parts[0]),
+                    "name": parts[1],
+                    "memory_mib": int(parts[2]),
+                    "driver": parts[3],
+                }
+            )
+    return gpus, None
+
+
+def preflight(cfg: dict[str, Any]) -> dict[str, Any]:
+    upstream = Path(cfg["upstream_path"])
+    checks: dict[str, dict[str, Any]] = {}
+
+    def check(name: str, ok: bool, evidence: Any, required: bool = True) -> None:
+        checks[name] = {"ok": bool(ok), "required": required, "evidence": evidence}
+
+    check("upstream_checkout", (upstream / ".git").exists(), str(upstream))
+    commit_code, commit = run_capture(["git", "rev-parse", "HEAD"], cwd=upstream)
+    check(
+        "pinned_upstream_commit",
+        commit_code == 0 and commit == cfg["expected_upstream_commit"],
+        {"observed": commit, "expected": cfg["expected_upstream_commit"]},
+    )
+
+    required_files = [
+        "examples/run_openvla_oft_rl_twin2.sh",
+        "examples/robotwin2_tasks_info.txt",
+        "verl/trainer/main_ppo.py",
+        "verl/trainer/ppo/ray_trainer.py",
+        "verl/utils/dataset/rob_dataset.py",
+        "verl/workers/rollout/rob_rollout.py",
+        "modified_codes/robotwin2/envs/move_can_pot.py",
+        "modified_codes/robotwin2/task_config/demo_randomized.yml",
+        "verl/utils/envs/robotwin2/seeds/robotwin2_eval_seeds.json",
+    ]
+    missing = [item for item in required_files if not (upstream / item).is_file()]
+    check("required_upstream_files", not missing, {"missing": missing, "required": required_files})
+
+    task_info = upstream / "examples/robotwin2_tasks_info.txt"
+    task_supported = task_info.is_file() and cfg["task"] in task_info.read_text(encoding="utf-8")
+    check("move_can_pot_supported", task_supported, str(task_info))
+
+    seed_file = upstream / "verl/utils/envs/robotwin2/seeds/robotwin2_eval_seeds.json"
+    observed_seed_count = 0
+    if seed_file.is_file():
+        seed_data = load_json(seed_file)
+        observed_seed_count = len(seed_data.get(cfg["task"], {}).get("success_seeds", []))
+    check(
+        "ood_seed_inventory",
+        observed_seed_count >= cfg["ood_validation_seeds"],
+        {"observed": observed_seed_count, "required": cfg["ood_validation_seeds"]},
+    )
+
+    rollout_source = upstream / "verl/workers/rollout/rob_rollout.py"
+    rollout_text = rollout_source.read_text(encoding="utf-8") if rollout_source.is_file() else ""
+    source_invariants = {
+        "three_view_branch": "num_images_in_input == 3" in rollout_text,
+        "left_wrist": '"left_wrist"' in rollout_text,
+        "right_wrist": '"right_wrist"' in rollout_text,
+        "proprio_vector": "joint_action']['vector" in rollout_text,
+        "environment_success": "self.env.eval_success" in rollout_text,
+        "validation_video": "save_rollout_video" in rollout_text,
+    }
+    check("source_invariants", all(source_invariants.values()), source_invariants)
+
+    checkpoint_raw = os.environ.get(cfg["checkpoint_env"], "")
+    checkpoint = Path(checkpoint_raw).expanduser() if checkpoint_raw else None
+    checkpoint_metadata: dict[str, Any] = {
+        "environment": cfg["checkpoint_env"],
+        "path": checkpoint_raw or None,
+        "checkpoint_id": cfg["checkpoint_id"],
+        "expected_revision": cfg["expected_checkpoint_revision"],
+    }
+    checkpoint_ready = bool(checkpoint and checkpoint.is_dir())
+    if checkpoint_ready and checkpoint is not None:
+        metadata_dir = checkpoint / ".cache/huggingface/download"
+        primary_names = [
+            "config.json",
+            "model-00001-of-00004.safetensors",
+            "model-00002-of-00004.safetensors",
+            "model-00003-of-00004.safetensors",
+            "model-00004-of-00004.safetensors",
+            "proprio_projector--20000_checkpoint.pt",
+            "lora_adapter/adapter_model.safetensors",
+        ]
+        artifact_metadata = {}
+        observed_revisions = set()
+        missing_artifacts = []
+        for name in primary_names:
+            artifact = checkpoint / name
+            metadata = metadata_dir / f"{name}.metadata"
+            if not artifact.is_file() or not metadata.is_file():
+                missing_artifacts.append(name)
+                continue
+            lines = metadata.read_text(encoding="utf-8").splitlines()
+            if len(lines) < 2:
+                missing_artifacts.append(name)
+                continue
+            observed_revisions.add(lines[0])
+            artifact_metadata[name] = {
+                "bytes": artifact.stat().st_size,
+                "huggingface_etag": lines[1],
+            }
+        checkpoint_metadata.update({
+            "observed_revisions": sorted(observed_revisions),
+            "artifact_metadata": artifact_metadata,
+            "missing_artifacts": missing_artifacts,
+        })
+        checkpoint_ready = (
+            not missing_artifacts
+            and observed_revisions == {cfg["expected_checkpoint_revision"]}
+        )
+    check(
+        "pretrained_checkpoint",
+        checkpoint_ready,
+        checkpoint_metadata,
+    )
+
+    robotwin_raw = os.environ.get(cfg["robotwin2_env"], "")
+    robotwin = Path(robotwin_raw).expanduser() if robotwin_raw else None
+    robotwin_markers = ["envs", "task_config", "script"]
+    check(
+        "robotwin2_checkout",
+        bool(robotwin and robotwin.is_dir() and all((robotwin / marker).exists() for marker in robotwin_markers)),
+        {"environment": cfg["robotwin2_env"], "path": robotwin_raw or None, "markers": robotwin_markers},
+    )
+    robotwin_code, robotwin_commit = (
+        run_capture(["git", "rev-parse", "HEAD"], cwd=robotwin)
+        if robotwin and robotwin.is_dir()
+        else (1, "")
+    )
+    check(
+        "pinned_robotwin2_commit",
+        robotwin_code == 0 and robotwin_commit == cfg["expected_robotwin2_commit"],
+        {"observed": robotwin_commit or None, "expected": cfg["expected_robotwin2_commit"]},
+    )
+
+    align_raw = os.environ.get(cfg["align_path_env"], "")
+    align = Path(align_raw).expanduser() if align_raw else upstream / "align.json"
+    check(
+        "ray_runtime_environment",
+        align.is_file(),
+        {"environment": cfg["align_path_env"], "path": str(align)},
+    )
+
+    gpus, gpu_error = gpu_inventory()
+    check(
+        "nvidia_gpu_count",
+        len(gpus) >= cfg["gpus_required"],
+        {"observed": len(gpus), "required": cfg["gpus_required"], "gpus": gpus, "error": gpu_error},
+    )
+
+    custom_task_config = HERE / "task_config_exp7_13_three_view.yml"
+    task_config_text = custom_task_config.read_text(encoding="utf-8") if custom_task_config.is_file() else ""
+    check(
+        "three_rgb_view_config",
+        custom_task_config.is_file()
+        and "collect_head_camera: true" in task_config_text
+        and "collect_wrist_camera: true" in task_config_text,
+        str(custom_task_config),
+    )
+
+    required_failures = [name for name, item in checks.items() if item["required"] and not item["ok"]]
+    return {
+        "schema_version": 1,
+        "experiment": cfg["experiment"],
+        "generated_at_utc": utc_now(),
+        "host": {
+            "platform": sys.platform,
+            "python": sys.version.split()[0],
+            "cwd": str(Path.cwd()),
+        },
+        "configuration": cfg,
+        "checks": checks,
+        "ready_for_real_validation": not required_failures,
+        "blocking_checks": required_failures,
+        "acceptance_note": "Preflight readiness is necessary but never sufficient for Experiment 7-13 completion.",
+    }
+
+
+def hydra_command(cfg: dict[str, Any], arm: str, run_dir: Path, worktree: Path) -> list[str]:
+    chunk = int(arm.removeprefix("chunk_"))
+    checkpoint = str(Path(os.environ[cfg["checkpoint_env"]]).expanduser().resolve())
+    align_raw = os.environ.get(cfg["align_path_env"])
+    align_path = Path(align_raw).expanduser().resolve() if align_raw else worktree / "align.json"
+    experiment_name = f"exp7_13_{cfg['task']}_{arm}"
+    return [
+        sys.executable,
+        "-u",
+        "-m",
+        "verl.trainer.main_ppo",
+        f"data.task_suite_name={cfg['task_suite']}",
+        "data.num_trials_per_task=128",
+        "data.n_samples=1",
+        "data.filter_accuracy=False",
+        "data.train_batch_size=64",
+        "data.val_batch_size=8",
+        "data.max_prompt_length=256",
+        "data.max_response_length=128",
+        f"actor_rollout_ref.model.path={checkpoint}",
+        f"actor_rollout_ref.model.vla={cfg['vla']}",
+        f"actor_rollout_ref.model.action_token_len={cfg['action_token_length']}",
+        f"actor_rollout_ref.model.action_chunks_len={chunk}",
+        "actor_rollout_ref.model.resume=False",
+        f"actor_rollout_ref.actor.num_images_in_input={cfg['rgb_views']}",
+        "actor_rollout_ref.actor.traj_mini_batch_size=8",
+        "actor_rollout_ref.actor.ppo_mini_batch_size=128",
+        "actor_rollout_ref.actor.ppo_micro_batch_size=8",
+        "actor_rollout_ref.actor.use_dynamic_bsz=False",
+        "actor_rollout_ref.actor.fsdp_config.param_offload=False",
+        "actor_rollout_ref.actor.fsdp_config.grad_offload=True",
+        "actor_rollout_ref.actor.fsdp_config.optimizer_offload=True",
+        f"actor_rollout_ref.rollout.twin2_task_config={cfg['task_config']}",
+        "actor_rollout_ref.rollout.twin2_instruction_type=seen",
+        f"actor_rollout_ref.rollout.num_images_in_input={cfg['rgb_views']}",
+        f"+actor_rollout_ref.rollout.action_token_len={cfg['action_token_length']}",
+        "actor_rollout_ref.rollout.use_proprio=True",
+        "actor_rollout_ref.rollout.val_micro_batch_size=8",
+        "actor_rollout_ref.rollout.temperature=1.6",
+        f"actor_rollout_ref.rollout.experiment_name={experiment_name}",
+        "actor_rollout_ref.rollout.micro_batch_size=1",
+        f"actor_rollout_ref.rollout.unnorm_key=robotwin2_{cfg['task']}_1k",
+        f"actor_rollout_ref.rollout.model_family={cfg['model_family']}",
+        f"actor_rollout_ref.rollout.task_suite_name={cfg['task_suite']}",
+        "actor_rollout_ref.rollout.num_steps_wait=10",
+        f"actor_rollout_ref.rollout.pretrained_checkpoint={checkpoint}",
+        "actor_rollout_ref.rollout.center_crop=True",
+        "actor_rollout_ref.rollout.max_prompt_length=512",
+        "actor_rollout_ref.rollout.tensor_model_parallel_size=1",
+        "actor_rollout_ref.rollout.name=hf",
+        "actor_rollout_ref.rollout.gpu_memory_utilization=0.9",
+        "actor_rollout_ref.ref.log_prob_micro_batch_size=32",
+        "actor_rollout_ref.ref.fsdp_config.param_offload=True",
+        "algorithm.adv_estimator=grpo",
+        "algorithm.kl_ctrl.kl_coef=0.0",
+        "trainer.logger=['console']",
+        "trainer.project_name=AI-Agent-Book-Experiment-7-13",
+        f"trainer.experiment_name={experiment_name}",
+        f"trainer.default_local_dir={str((run_dir / arm / 'checkpoints').resolve())}",
+        f"trainer.n_gpus_per_node={cfg['gpus_required']}",
+        "trainer.nnodes=1",
+        "trainer.save_freq=-1",
+        "trainer.test_freq=-1",
+        "trainer.total_epochs=1",
+        "trainer.val_only=True",
+        "trainer.val_before_train=True",
+        f"trainer.runtime_env={str(align_path)}",
+        "trainer.wandb_mode=disabled",
+    ]
+
+
+def shell_quote(value: str) -> str:
+    import shlex
+
+    return shlex.quote(value)
+
+
+def prepare_worktree(cfg: dict[str, Any], run_dir: Path) -> Path:
+    upstream = Path(cfg["upstream_path"])
+    worktree = run_dir / "instrumented-upstream"
+    if worktree.exists():
+        raise RuntimeError(f"refusing to replace existing worktree: {worktree}")
+    code, output = run_capture(
+        ["git", "worktree", "add", "--detach", str(worktree), cfg["expected_upstream_commit"]],
+        cwd=upstream,
+    )
+    if code != 0:
+        raise RuntimeError(f"git worktree add failed: {output}")
+    robotwin = Path(os.environ[cfg["robotwin2_env"]]).expanduser().resolve()
+    setup = subprocess.run(
+        ["bash", "copy_overwrite_robotwin2.sh", str(robotwin), str(worktree)],
+        cwd=worktree,
+        check=False,
+    )
+    if setup.returncode != 0:
+        raise RuntimeError(f"RoboTwin2 overlay setup failed with exit {setup.returncode}")
+    task_config_dest = worktree / "verl/utils/envs/robotwin2/task_config" / f"{cfg['task_config']}.yml"
+    task_config_dest.parent.mkdir(parents=True, exist_ok=True)
+    shutil.copy2(HERE / "task_config_exp7_13_three_view.yml", task_config_dest)
+    patch_proc = subprocess.run(
+        [sys.executable, str(HERE / "instrument_upstream.py"), str(worktree)],
+        check=False,
+    )
+    if patch_proc.returncode != 0:
+        raise RuntimeError(f"upstream instrumentation failed with exit {patch_proc.returncode}")
+    return worktree
+
+
+def write_launch_manifest(cfg: dict[str, Any], run_dir: Path, worktree: Path) -> dict[str, Any]:
+    arms = {}
+    for chunk in cfg["action_chunks"]:
+        arm = f"chunk_{chunk}"
+        argv = hydra_command(cfg, arm, run_dir, worktree)
+        episode_path = (run_dir / arm / "episodes.jsonl").resolve()
+        env = {
+            "EXP7_13_EPISODE_JSONL": str(episode_path),
+            "EXP7_13_ARM": arm,
+            "EXP7_13_UPSTREAM_COMMIT": cfg["expected_upstream_commit"],
+            "HYDRA_FULL_ERROR": "1",
+            "TOKENIZERS_PARALLELISM": "true",
+            "NCCL_DEBUG": "WARN",
+            "ROBOT_PLATFORM": "ALOHA",
+            "VERL_DISABLE_VLLM_IMPORT": "1",
+        }
+        arms[arm] = {
+            "action_chunk_length": chunk,
+            "episode_evidence": str(episode_path),
+            "rollout_directory": str((worktree / "rollouts" / f"exp7_13_{cfg['task']}_{arm}").resolve()),
+            "environment": env,
+            "argv": argv,
+            "shell_command": " ".join(
+                [*(f"{key}={shell_quote(value)}" for key, value in env.items()), *(shell_quote(item) for item in argv)]
+            ),
+        }
+    manifest = {
+        "schema_version": 1,
+        "experiment": cfg["experiment"],
+        "generated_at_utc": utc_now(),
+        "real_execution_required": True,
+        "upstream_worktree": str(worktree.resolve()),
+        "upstream_commit": cfg["expected_upstream_commit"],
+        "arms": arms,
+    }
+    dump_json(run_dir / "launch_manifest.json", manifest)
+    return manifest
+
+
+def execute_arms(manifest: dict[str, Any], selected: Iterable[str]) -> None:
+    worktree = Path(manifest["upstream_worktree"])
+    for arm in selected:
+        spec = manifest["arms"][arm]
+        arm_dir = Path(spec["episode_evidence"]).parent
+        arm_dir.mkdir(parents=True, exist_ok=True)
+        log_path = arm_dir / "upstream.log"
+        env = os.environ.copy()
+        env.update(spec["environment"])
+        started = utc_now()
+        with log_path.open("w", encoding="utf-8") as log:
+            proc = subprocess.run(
+                spec["argv"],
+                cwd=worktree,
+                env=env,
+                check=False,
+                stdout=log,
+                stderr=subprocess.STDOUT,
+            )
+        dump_json(
+            arm_dir / "process.json",
+            {
+                "arm": arm,
+                "started_at_utc": started,
+                "ended_at_utc": utc_now(),
+                "exit_code": proc.returncode,
+                "log": str(log_path.resolve()),
+            },
+        )
+        if proc.returncode != 0:
+            raise RuntimeError(f"{arm} real validation failed with exit {proc.returncode}; see {log_path}")
+
+
+def percentile(values: list[float], q: float) -> float | None:
+    if not values:
+        return None
+    ordered = sorted(values)
+    if len(ordered) == 1:
+        return ordered[0]
+    pos = (len(ordered) - 1) * q
+    lo = math.floor(pos)
+    hi = math.ceil(pos)
+    if lo == hi:
+        return ordered[lo]
+    return ordered[lo] + (ordered[hi] - ordered[lo]) * (pos - lo)
+
+
+def distribution(values: list[float]) -> dict[str, float | int | None]:
+    return {
+        "count": len(values),
+        "mean": statistics.fmean(values) if values else None,
+        "std": statistics.pstdev(values) if len(values) > 1 else 0.0 if values else None,
+        "min": min(values) if values else None,
+        "p50": percentile(values, 0.50),
+        "p95": percentile(values, 0.95),
+        "max": max(values) if values else None,
+    }
+
+
+def read_jsonl(path: Path) -> list[dict[str, Any]]:
+    rows = []
+    if not path.is_file():
+        return rows
+    for line_no, line in enumerate(path.read_text(encoding="utf-8").splitlines(), 1):
+        if not line.strip():
+            continue
+        try:
+            rows.append(json.loads(line))
+        except json.JSONDecodeError as exc:
+            raise ValueError(f"{path}:{line_no}: {exc}") from exc
+    return rows
+
+
+def video_index(
+    directory: Path,
+    *,
+    started_at_utc: str | None = None,
+    ended_at_utc: str | None = None,
+) -> dict[tuple[str, bool], list[str]]:
+    """Index rollout videos, optionally restricting them to one process window.
+
+    Upstream names videos with a random ``ran`` suffix and does not clear an
+    existing rollout directory.  A bounded resume can therefore leave two
+    files for the same task/result key.  Process timestamps let the evidence
+    reader reject those stale files without guessing from the random suffix.
+    """
+    index: dict[tuple[str, bool], list[str]] = {}
+    if not directory.is_dir():
+        return index
+    started = datetime.fromisoformat(started_at_utc) if started_at_utc else None
+    ended = datetime.fromisoformat(ended_at_utc) if ended_at_utc else None
+    for path in directory.rglob("*.mp4"):
+        modified = datetime.fromtimestamp(path.stat().st_mtime, timezone.utc)
+        if started is not None and modified < started:
+            continue
+        if ended is not None and modified > ended:
+            continue
+        match = VIDEO_RE.match(path.name)
+        if not match:
+            continue
+        task_key = match.group("task")
+        success = match.group("success") == "True"
+        index.setdefault((task_key, success), []).append(str(path.resolve()))
+    return index
+
+
+def annotation_key(row: dict[str, Any]) -> str:
+    return f"{row['arm']}|{row['data_source']}|{row['trial_seed']}"
+
+
+def analyze(cfg: dict[str, Any], run_dir: Path, annotations_path: Path | None) -> dict[str, Any]:
+    manifest_path = run_dir / "launch_manifest.json"
+    manifest = load_json(manifest_path) if manifest_path.is_file() else None
+    annotations = load_json(annotations_path) if annotations_path and annotations_path.is_file() else {}
+    all_rows: list[dict[str, Any]] = []
+    arm_reports: dict[str, Any] = {}
+    strict_errors: list[str] = []
+
+    expected_total = cfg["iid_validation_seeds"] + cfg["ood_validation_seeds"]
+    for chunk in cfg["action_chunks"]:
+        arm = f"chunk_{chunk}"
+        episode_path = run_dir / arm / "episodes.jsonl"
+        rows = read_jsonl(episode_path)
+        all_rows.extend(rows)
+        bad_source = [row for row in rows if row.get("source") != "upstream_val_only"]
+        if bad_source:
+            strict_errors.append(f"{arm}: {len(bad_source)} rows are not upstream_val_only evidence")
+        if len(rows) != expected_total:
+            strict_errors.append(f"{arm}: expected {expected_total} episodes, observed {len(rows)}")
+        if any(row.get("action_chunk_length") != chunk for row in rows):
+            strict_errors.append(f"{arm}: action chunk mismatch")
+        if any(row.get("action_dimension") != cfg["action_dimension"] for row in rows):
+            strict_errors.append(f"{arm}: action dimension mismatch")
+        if any(row.get("rgb_views") != cfg["rgb_views"] for row in rows):
+            strict_errors.append(f"{arm}: three-view observation not proven")
+        if any(not row.get("proprioception_enabled") for row in rows):
+            strict_errors.append(f"{arm}: proprioception not enabled in all rows")
+
+        iid = [row for row in rows if str(row.get("data_source", "")).endswith("_train_iid")]
+        ood = [row for row in rows if str(row.get("data_source", "")).endswith("_eval_ood")]
+        if len({row.get("trial_seed") for row in iid}) != cfg["iid_validation_seeds"]:
+            strict_errors.append(f"{arm}: incomplete IID seed coverage")
+        if len({row.get("trial_seed") for row in ood}) != cfg["ood_validation_seeds"]:
+            strict_errors.append(f"{arm}: incomplete OOD seed coverage")
+
+        video_dir = Path(manifest["arms"][arm]["rollout_directory"]) if manifest else Path()
+        process_path = run_dir / arm / "process.json"
+        process_data = load_json(process_path) if process_path.is_file() else {}
+        videos = video_index(
+            video_dir,
+            started_at_utc=process_data.get("started_at_utc"),
+            ended_at_utc=process_data.get("ended_at_utc"),
+        )
+        unmatched_videos = 0
+        failure_counts: Counter[str] = Counter()
+        unclassified_failures = []
+        missing_video_count = 0
+        for row in rows:
+            task_file = f"{cfg['task']}_trial_{row.get('trial_id')}_seed_{row.get('trial_seed')}"
+            candidates = videos.get((task_file, bool(row.get("success"))), [])
+            if candidates:
+                row["video_path"] = candidates.pop(0)
+            else:
+                missing_video_count += 1
+            if row.get("success"):
+                row["failure_mode"] = None
+                row["failure_evidence"] = None
+            else:
+                annotation = annotations.get(annotation_key(row))
+                if annotation:
+                    label = annotation.get("failure_mode")
+                    evidence = annotation.get("evidence")
+                    if label not in cfg["failure_modes"] or not evidence:
+                        unclassified_failures.append(annotation_key(row))
+                    else:
+                        row["failure_mode"] = label
+                        row["failure_evidence"] = evidence
+                        failure_counts[label] += 1
+                else:
+                    unclassified_failures.append(annotation_key(row))
+        unmatched_videos = sum(len(paths) for paths in videos.values())
+        if missing_video_count:
+            strict_errors.append(f"{arm}: {missing_video_count} episodes lack rollout video evidence")
+        if unclassified_failures:
+            strict_errors.append(f"{arm}: {len(unclassified_failures)} failed episodes lack valid failure annotations")
+
+        action_steps = [float(row["finish_action_steps"]) for row in rows if row.get("finish_action_steps") is not None]
+        control_seconds = [value / cfg["control_hz"] for value in action_steps]
+        successes = sum(bool(row.get("success")) for row in rows)
+        by_split = {}
+        for split_name, split_rows in (("iid", iid), ("ood", ood)):
+            split_successes = sum(bool(row.get("success")) for row in split_rows)
+            by_split[split_name] = {
+                "episodes": len(split_rows),
+                "successes": split_successes,
+                "success_rate": split_successes / len(split_rows) if split_rows else None,
+            }
+        arm_reports[arm] = {
+            "action_chunk_length": chunk,
+            "episodes": len(rows),
+            "successes": successes,
+            "success_rate": successes / len(rows) if rows else None,
+            "by_split": by_split,
+            "completion_action_steps": distribution(action_steps),
+            "completion_control_seconds_at_50hz": distribution(control_seconds),
+            "failure_modes": dict(sorted(failure_counts.items())),
+            "unclassified_failure_count": len(unclassified_failures),
+            "missing_video_count": missing_video_count,
+            "unmatched_video_count": unmatched_videos,
+        }
+
+    control = arm_reports.get("chunk_1", {})
+    treatment = arm_reports.get("chunk_25", {})
+    paired = {}
+    rows_by_key = {
+        (row.get("arm"), row.get("data_source"), row.get("trial_seed")): row for row in all_rows
+    }
+    pair_deltas = []
+    for source in ("train_iid", "eval_ood"):
+        suffix = f"_{source}"
+        seeds = {
+            row.get("trial_seed")
+            for row in all_rows
+            if str(row.get("data_source", "")).endswith(suffix)
+        }
+        for seed in seeds:
+            left = next(
+                (
+                    row
+                    for row in all_rows
+                    if row.get("arm") == "chunk_1"
+                    and str(row.get("data_source", "")).endswith(suffix)
+                    and row.get("trial_seed") == seed
+                ),
+                None,
+            )
+            right = next(
+                (
+                    row
+                    for row in all_rows
+                    if row.get("arm") == "chunk_25"
+                    and str(row.get("data_source", "")).endswith(suffix)
+                    and row.get("trial_seed") == seed
+                ),
+                None,
+            )
+            if left and right:
+                pair_deltas.append(int(bool(right.get("success"))) - int(bool(left.get("success"))))
+    paired = {
+        "complete_pairs": len(pair_deltas),
+        "chunk_25_wins": sum(delta > 0 for delta in pair_deltas),
+        "chunk_1_wins": sum(delta < 0 for delta in pair_deltas),
+        "ties": sum(delta == 0 for delta in pair_deltas),
+        "mean_paired_success_delta": statistics.fmean(pair_deltas) if pair_deltas else None,
+        "unpaired_rows": len(all_rows) - 2 * len(pair_deltas),
+    }
+    if pair_deltas and len(pair_deltas) != expected_total:
+        strict_errors.append(f"paired comparison: expected {expected_total} pairs, observed {len(pair_deltas)}")
+
+    preflight_files = sorted((run_dir / "preflight.json", *HERE.glob("results/preflight-*.json")), key=lambda p: p.stat().st_mtime if p.exists() else 0)
+    preflight_report = load_json(preflight_files[-1]) if preflight_files and preflight_files[-1].is_file() else None
+    if not preflight_report or not preflight_report.get("ready_for_real_validation"):
+        strict_errors.append("no passing real-execution preflight is attached to this run")
+    process_failures = []
+    for chunk in cfg["action_chunks"]:
+        arm = f"chunk_{chunk}"
+        process_path = run_dir / arm / "process.json"
+        process_data = load_json(process_path) if process_path.is_file() else None
+        if not process_data or process_data.get("exit_code") != 0:
+            process_failures.append(arm)
+    if process_failures:
+        strict_errors.append(f"missing or failed upstream processes: {', '.join(process_failures)}")
+
+    report = {
+        "schema_version": 1,
+        "experiment": cfg["experiment"],
+        "generated_at_utc": utc_now(),
+        "evidence_policy": {
+            "paper_numbers_accepted": False,
+            "historical_rollouts_accepted": False,
+            "dry_run_commands_accepted": False,
+            "required_source": "instrumented upstream val-only execution",
+        },
+        "configuration": cfg,
+        "arms": arm_reports,
+        "controlled_action_chunk_comparison": paired,
+        "strict_completion": {
+            "complete": not strict_errors,
+            "errors": strict_errors,
+        },
+    }
+    dump_json(run_dir / "report.json", report)
+    lines = [
+        "# Experiment 7-13 OpenVLA + RoboTwin2 evaluation",
+        "",
+        f"Official completion: **{report['strict_completion']['complete']}**",
+        "",
+        "| Arm | Episodes | Success rate | IID | OOD | Completion p50 / p95 (50 Hz seconds) |",
+        "| --- | ---: | ---: | ---: | ---: | ---: |",
+    ]
+    for arm, item in arm_reports.items():
+        times = item["completion_control_seconds_at_50hz"]
+        lines.append(
+            f"| {arm} | {item['episodes']} | {item['success_rate']} | "
+            f"{item['by_split']['iid']['success_rate']} | {item['by_split']['ood']['success_rate']} | "
+            f"{times['p50']} / {times['p95']} |"
+        )
+    lines.extend(["", "## Controlled comparison", "", f"```json\n{json.dumps(paired, indent=2)}\n```", "", "## Failure modes", ""])
+    for arm, item in arm_reports.items():
+        lines.append(f"- {arm}: {json.dumps(item['failure_modes'], ensure_ascii=False)}")
+    lines.extend(["", "## Completion audit", ""])
+    if strict_errors:
+        lines.extend(f"- {error}" for error in strict_errors)
+    else:
+        lines.append("- Every direct manuscript gate is supported by real per-episode evidence.")
+    (run_dir / "report.md").write_text("\n".join(lines) + "\n", encoding="utf-8")
+    return report
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--config", type=Path, default=DEFAULT_CONFIG)
+    sub = parser.add_subparsers(dest="command", required=True)
+    pre = sub.add_parser("preflight", help="inspect the real host and upstream checkout")
+    pre.add_argument("--output", type=Path)
+    prep = sub.add_parser("prepare", help="prepare an instrumented disposable worktree and commands")
+    prep.add_argument("--run-dir", type=Path, required=True)
+    launch = sub.add_parser("launch", help="execute real val-only arms from an existing manifest")
+    launch.add_argument("--run-dir", type=Path, required=True)
+    launch.add_argument("--arm", choices=["all", "chunk_1", "chunk_25"], default="all")
+    ana = sub.add_parser("analyze", help="build strict success/time/failure/chunk report")
+    ana.add_argument("--run-dir", type=Path, required=True)
+    ana.add_argument("--failure-annotations", type=Path)
+    args = parser.parse_args()
+    cfg = resolve_config(args.config.resolve())
+
+    if args.command == "preflight":
+        report = preflight(cfg)
+        output = args.output or HERE / "results" / f"preflight-{datetime.now().strftime('%Y%m%d-%H%M%S')}.json"
+        dump_json(output, report)
+        print(json.dumps(report, indent=2, ensure_ascii=False))
+        return 0 if report["ready_for_real_validation"] else 2
+    if args.command == "prepare":
+        readiness = preflight(cfg)
+        args.run_dir.mkdir(parents=True, exist_ok=True)
+        dump_json(args.run_dir / "preflight.json", readiness)
+        if not readiness["ready_for_real_validation"]:
+            print(json.dumps(readiness, indent=2, ensure_ascii=False))
+            return 2
+        worktree = prepare_worktree(cfg, args.run_dir.resolve())
+        manifest = write_launch_manifest(cfg, args.run_dir.resolve(), worktree)
+        print(json.dumps(manifest, indent=2, ensure_ascii=False))
+        return 0
+    if args.command == "launch":
+        manifest = load_json(args.run_dir / "launch_manifest.json")
+        selected = list(manifest["arms"]) if args.arm == "all" else [args.arm]
+        execute_arms(manifest, selected)
+        return 0
+    if args.command == "analyze":
+        report = analyze(cfg, args.run_dir.resolve(), args.failure_annotations)
+        print(json.dumps(report, indent=2, ensure_ascii=False))
+        return 0 if report["strict_completion"]["complete"] else 2
+    raise AssertionError(args.command)
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

chapter7/openvla-robotwin2-eval/finalize_evidence.py

@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+"""Finalize and verify the retained Experiment 7-13 evidence package."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+from typing import Any
+
+import experiment
+
+
+HERE = Path(__file__).resolve().parent
+DEFAULT_RUN = HERE / "validation" / "runs" / "exp7-13-localgpu-20260803-v1"
+PACKAGE_FILES = (
+    "chunk_1/episodes.jsonl",
+    "chunk_1/process.json",
+    "chunk_25/episodes.jsonl",
+    "chunk_25/process.json",
+    "failure_annotations.json",
+    "launch_manifest.json",
+    "preflight.json",
+    "report.json",
+    "report.md",
+)
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def artifact(path: Path, label: str) -> dict[str, Any]:
+    return {"path": label, "bytes": path.stat().st_size, "sha256": sha256(path)}
+
+
+def canonical_videos(run_dir: Path) -> list[dict[str, Any]]:
+    launch = experiment.load_json(run_dir / "launch_manifest.json")
+    records: list[dict[str, Any]] = []
+    for arm in ("chunk_1", "chunk_25"):
+        process = experiment.load_json(run_dir / arm / "process.json")
+        index = experiment.video_index(
+            Path(launch["arms"][arm]["rollout_directory"]),
+            started_at_utc=process["started_at_utc"],
+            ended_at_utc=process["ended_at_utc"],
+        )
+        for (task_key, success), candidates in sorted(index.items()):
+            if len(candidates) != 1:
+                raise RuntimeError(
+                    f"{arm}/{task_key}/{success}: expected one in-window video, "
+                    f"found {len(candidates)}"
+                )
+            path = Path(candidates[0])
+            records.append(
+                {
+                    "arm": arm,
+                    "task_key": task_key,
+                    "success": success,
+                    "filename": path.name,
+                    "bytes": path.stat().st_size,
+                    "sha256": sha256(path),
+                }
+            )
+    return records
+
+
+def build_manifest(run_dir: Path) -> dict[str, Any]:
+    report = experiment.load_json(run_dir / "report.json")
+    annotations = experiment.load_json(run_dir / "failure_annotations.json")
+    videos = canonical_videos(run_dir)
+    package = [artifact(run_dir / name, name) for name in PACKAGE_FILES]
+    sources = []
+    for name in (
+        "README.md",
+        "annotate_timeouts.py",
+        "config.json",
+        "experiment.py",
+        "finalize_evidence.py",
+        "instrument_upstream.py",
+        "task_config_exp7_13_three_view.yml",
+        "test_experiment.py",
+    ):
+        sources.append(artifact(HERE / name, f"chapter7/openvla-robotwin2-eval/{name}"))
+    checks = {
+        "strict_analysis_complete": report["strict_completion"]["complete"] is True,
+        "two_256_episode_arms": all(
+            report["arms"][arm]["episodes"] == 256 for arm in ("chunk_1", "chunk_25")
+        ),
+        "all_512_rollout_videos_hashed": len(videos) == 512,
+        "all_486_failures_annotated": len(annotations) == 486,
+        "all_processes_succeeded": all(
+            experiment.load_json(run_dir / arm / "process.json")["exit_code"] == 0
+            for arm in ("chunk_1", "chunk_25")
+        ),
+        "preflight_passed": experiment.load_json(run_dir / "preflight.json")[
+            "ready_for_real_validation"
+        ]
+        is True,
+    }
+    return {
+        "schema_version": 1,
+        "experiment": "7-13",
+        "generated_at_utc": experiment.utc_now(),
+        "status": "complete" if all(checks.values()) else "incomplete",
+        "checks": checks,
+        "results": {
+            "chunk_1_successes": report["arms"]["chunk_1"]["successes"],
+            "chunk_25_successes": report["arms"]["chunk_25"]["successes"],
+            "paired_success_delta": report["controlled_action_chunk_comparison"][
+                "mean_paired_success_delta"
+            ],
+            "classified_failures": len(annotations),
+        },
+        "retained_package": package,
+        "runtime_sources": sources,
+        "external_rollout_videos": videos,
+        "claim_boundary": (
+            "The retained JSON/Markdown package and source hashes are reviewable in a clean "
+            "clone. The 512 rollout MP4s and 15 GB checkpoint remain external local artifacts; "
+            "their identities were hashed during finalization and are not vendored."
+        ),
+    }
+
+
+def verify(run_dir: Path) -> None:
+    manifest = experiment.load_json(run_dir / "manifest.json")
+    failures: list[str] = []
+    sidecar_path = run_dir / "manifest.json.sha256"
+    expected_sidecar = f"{sha256(run_dir / 'manifest.json')}  manifest.json\n"
+    if not sidecar_path.is_file():
+        failures.append("missing manifest.json.sha256")
+    elif sidecar_path.read_text(encoding="utf-8") != expected_sidecar:
+        failures.append("manifest.json.sha256 does not match manifest.json")
+    for section, base in (("retained_package", run_dir), ("runtime_sources", HERE.parent.parent)):
+        for item in manifest[section]:
+            path = base / item["path"]
+            if not path.is_file():
+                failures.append(f"missing {item['path']}")
+            elif path.stat().st_size != item["bytes"] or sha256(path) != item["sha256"]:
+                failures.append(f"hash mismatch {item['path']}")
+    if manifest["status"] != "complete" or not all(manifest["checks"].values()):
+        failures.append("manifest acceptance checks are not complete")
+    if len(manifest["external_rollout_videos"]) != 512:
+        failures.append("manifest does not retain 512 rollout-video identities")
+    if failures:
+        raise RuntimeError("; ".join(failures))
+    print("Experiment 7-13 retained evidence verification passed")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("command", choices=("finalize", "verify"))
+    parser.add_argument("--run-dir", type=Path, default=DEFAULT_RUN)
+    args = parser.parse_args()
+    run_dir = args.run_dir.resolve()
+    if args.command == "finalize":
+        manifest = build_manifest(run_dir)
+        experiment.dump_json(run_dir / "manifest.json", manifest)
+        (run_dir / "manifest.json.sha256").write_text(
+            f"{sha256(run_dir / 'manifest.json')}  manifest.json\n", encoding="utf-8"
+        )
+        print(f"Wrote complete manifest with {len(manifest['external_rollout_videos'])} videos")
+    else:
+        verify(run_dir)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

chapter7/openvla-robotwin2-eval/instrument_upstream.py

@@ -0,0 +1,116 @@
+#!/usr/bin/env python3
+"""Fail-closed instrumentation for a disposable SimpleVLA-RL worktree."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+
+EPISODE_RECORDER = '''        # Experiment 7-13 instrumentation. This patch is applied only to a
+        # disposable worktree; the upstream checkout remains clean. Preserve
+        # one row per real RoboTwin2 validation episode so the companion can
+        # audit paired seeds, action counts and exact environment rewards.
+        evidence_path = os.environ.get("EXP7_13_EPISODE_JSONL")
+        if evidence_path:
+            def _ints(name, default=-1):
+                if name not in data.batch:
+                    return [default] * batch_size
+                values = data.batch[name].detach().cpu().reshape(batch_size, -1)
+                return [int(row[0].item()) for row in values]
+
+            sources = data.non_tensor_batch.get(
+                'data_source',
+                [self.config.data.task_suite_name] * batch_size,
+            )
+            trial_ids = _ints('trial_id')
+            trial_seeds = _ints('trial_seed')
+            finish_steps = _ints('finish_step', 0)
+            os.makedirs(os.path.dirname(os.path.abspath(evidence_path)), exist_ok=True)
+            with open(evidence_path, "a", encoding="utf-8") as evidence_file:
+                for index, complete in enumerate(completes):
+                    row = {
+                        "schema_version": 1,
+                        "experiment": "7-13",
+                        "source": "upstream_val_only",
+                        "recorded_at_utc": datetime.now(timezone.utc).isoformat(),
+                        "arm": os.environ.get("EXP7_13_ARM"),
+                        "upstream_commit": os.environ.get("EXP7_13_UPSTREAM_COMMIT"),
+                        "task": self.config.data.task_suite_name,
+                        "data_source": str(sources[index]),
+                        "trial_id": trial_ids[index],
+                        "trial_seed": trial_seeds[index],
+                        "success": bool(complete),
+                        "finish_action_steps": finish_steps[index],
+                        "action_chunk_length": int(self.config.actor_rollout_ref.model.action_chunks_len),
+                        "action_dimension": int(self.config.actor_rollout_ref.model.action_token_len),
+                        "rgb_views": int(self.config.actor_rollout_ref.rollout.num_images_in_input),
+                        "proprioception_enabled": bool(self.config.actor_rollout_ref.rollout.use_proprio),
+                    }
+                    evidence_file.write(json.dumps(row, ensure_ascii=False) + "\\n")
+                evidence_file.flush()
+                os.fsync(evidence_file.fileno())
+'''
+
+ACTION_PREFIX = '''        # The OpenVLA-OFT checkpoint head is trained to predict 25 actions and
+        # always returns that full tensor. For an execution-chunk ablation,
+        # the rollout must execute only the configured prefix.
+        configured_chunks = int(self.config.action_chunks_len)
+        action_dimension = int(self.config.action_token_len)
+        if not 1 <= configured_chunks <= actions.shape[1]:
+            raise ValueError(
+                f"Configured action chunk {configured_chunks} is incompatible "
+                f"with model output shape {actions.shape}"
+            )
+        response_tokens = configured_chunks * action_dimension
+        if response.ndim != 2 or response.shape[1] < response_tokens:
+            raise ValueError(
+                f"Model response shape {tuple(response.shape)} cannot prove "
+                f"{configured_chunks} actions x {action_dimension} dimensions"
+            )
+        actions = actions[:, :configured_chunks, :]
+        response = response[:, :response_tokens]
+'''
+
+
+def replace_once(path: Path, old: str, new: str) -> None:
+    text = path.read_text(encoding="utf-8")
+    if text.count(old) != 1:
+        raise RuntimeError(f"{path}: expected exactly one instrumentation anchor")
+    path.write_text(text.replace(old, new), encoding="utf-8")
+
+
+def instrument(upstream: Path) -> None:
+    main = upstream / "verl/trainer/main_ppo.py"
+    replace_once(main, "import statistics\n", "import statistics\nfrom datetime import datetime, timezone\n")
+    anchor = "        reward_format_metrics['all'] = data.batch['acc'].mean().item()\n"
+    replace_once(main, anchor, anchor + EPISODE_RECORDER)
+
+    hybrid = upstream / "verl/workers/hybrid_engine/__init__.py"
+    replace_once(
+        hybrid,
+        "# limitations under the License.\n\nfrom verl.utils.import_utils",
+        "# limitations under the License.\n\nimport os\n\nfrom verl.utils.import_utils",
+    )
+    replace_once(
+        hybrid,
+        "if is_vllm_available():\n",
+        'if is_vllm_available() and not os.environ.get("VERL_DISABLE_VLLM_IMPORT"):\n',
+    )
+
+    rollout = upstream / "verl/workers/rollout/rob_rollout.py"
+    generation_end = "                    temperature=temperature,\n                )\n"
+    replace_once(rollout, generation_end, generation_end + ACTION_PREFIX)
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("upstream", type=Path)
+    args = parser.parse_args()
+    instrument(args.upstream.resolve())
+    print("Instrumented disposable SimpleVLA-RL worktree")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

chapter7/openvla-robotwin2-eval/task_config_exp7_13_three_view.yml

@@ -0,0 +1,35 @@
+render_freq: 0
+episode_num: 100
+use_seed: false
+save_freq: 15
+embodiment: [piper, piper, 0.6]
+language_num: 100
+domain_randomization:
+  random_background: true
+  cluttered_table: true
+  clean_background_rate: 0.02
+  random_head_camera_dis: 0
+  random_table_height: 0.03
+  random_light: true
+  crazy_random_light_rate: 0.02
+camera:
+  head_camera_type: D435
+  wrist_camera_type: D435
+  collect_head_camera: true
+  collect_wrist_camera: true
+data_type:
+  rgb: true
+  third_view: false
+  depth: false
+  pointcloud: false
+  observer: false
+  endpose: false
+  qpos: true
+  mesh_segmentation: false
+  actor_segmentation: false
+pcd_down_sample_num: 1024
+pcd_crop: true
+save_path: ./data
+clear_cache_freq: 5
+collect_data: true
+eval_video_log: true

chapter7/openvla-robotwin2-eval/test_experiment.py

@@ -0,0 +1,120 @@
+import importlib.util
+import json
+import os
+from datetime import datetime, timezone
+from pathlib import Path
+
+
+HERE = Path(__file__).resolve().parent
+SPEC = importlib.util.spec_from_file_location("exp612", HERE / "experiment.py")
+exp = importlib.util.module_from_spec(SPEC)
+assert SPEC.loader
+SPEC.loader.exec_module(exp)
+
+
+def config():
+    return exp.resolve_config(HERE / "config.json")
+
+
+def row(arm, chunk, source, seed, success=True):
+    return {
+        "schema_version": 1,
+        "experiment": "7-13",
+        "source": "upstream_val_only",
+        "arm": arm,
+        "upstream_commit": config()["expected_upstream_commit"],
+        "task": "robotwin2_move_can_pot",
+        "data_source": f"robotwin2_move_can_pot_{source}",
+        "trial_id": seed,
+        "trial_seed": seed,
+        "success": success,
+        "finish_action_steps": 100,
+        "action_chunk_length": chunk,
+        "action_dimension": 14,
+        "rgb_views": 3,
+        "proprioception_enabled": True,
+    }
+
+
+def test_command_is_exact_val_only_three_view_controlled(tmp_path, monkeypatch):
+    cfg = config()
+    checkpoint = tmp_path / "checkpoint"
+    checkpoint.mkdir()
+    monkeypatch.setenv(cfg["checkpoint_env"], str(checkpoint))
+    command = exp.hydra_command(cfg, "chunk_25", tmp_path, Path(cfg["upstream_path"]))
+    joined = " ".join(command)
+    assert "trainer.val_only=True" in joined
+    assert "trainer.val_before_train=True" in joined
+    assert "actor_rollout_ref.model.action_chunks_len=25" in joined
+    assert "actor_rollout_ref.model.action_token_len=14" in joined
+    assert "+actor_rollout_ref.rollout.action_token_len=14" in joined
+    assert "actor_rollout_ref.rollout.num_images_in_input=3" in joined
+    assert "actor_rollout_ref.rollout.use_proprio=True" in joined
+    assert "data.val_batch_size=8" in joined
+    assert "algorithm.adv_estimator=grpo" in joined
+    assert "algorithm.kl_ctrl.kl_coef=0.0" in joined
+
+
+def test_instrumentation_executes_only_the_configured_action_prefix():
+    instrumenter = (HERE / "instrument_upstream.py").read_text(encoding="utf-8")
+    assert "actions = actions[:, :configured_chunks, :]" in instrumenter
+    assert "response = response[:, :response_tokens]" in instrumenter
+    assert "response_tokens = configured_chunks * action_dimension" in instrumenter
+
+
+def test_analysis_refuses_incomplete_or_unclassified_real_evidence(tmp_path):
+    cfg = config()
+    manifest = {
+        "arms": {
+            "chunk_1": {"rollout_directory": str(tmp_path / "videos1")},
+            "chunk_25": {"rollout_directory": str(tmp_path / "videos25")},
+        }
+    }
+    (tmp_path / "launch_manifest.json").write_text(json.dumps(manifest), encoding="utf-8")
+    for arm, chunk in (("chunk_1", 1), ("chunk_25", 25)):
+        arm_dir = tmp_path / arm
+        arm_dir.mkdir()
+        rows = [row(arm, chunk, "train_iid", 1, success=False)]
+        (arm_dir / "episodes.jsonl").write_text("\n".join(json.dumps(item) for item in rows) + "\n", encoding="utf-8")
+    report = exp.analyze(cfg, tmp_path, None)
+    assert report["strict_completion"]["complete"] is False
+    assert any("expected 256 episodes" in error for error in report["strict_completion"]["errors"])
+    assert any("failure annotations" in error for error in report["strict_completion"]["errors"])
+
+
+def test_preflight_never_claims_ready_without_real_hardware(monkeypatch):
+    cfg = config()
+    monkeypatch.delenv(cfg["checkpoint_env"], raising=False)
+    monkeypatch.delenv(cfg["robotwin2_env"], raising=False)
+    monkeypatch.setattr(exp, "gpu_inventory", lambda: ([], "no NVIDIA runtime"))
+    report = exp.preflight(cfg)
+    assert report["ready_for_real_validation"] is False
+    assert "pretrained_checkpoint" in report["blocking_checks"]
+    assert "robotwin2_checkout" in report["blocking_checks"]
+    assert "pinned_robotwin2_commit" in report["blocking_checks"]
+    assert "nvidia_gpu_count" in report["blocking_checks"]
+
+
+def test_distribution_reports_required_completion_percentiles():
+    stats = exp.distribution([1.0, 2.0, 3.0, 4.0])
+    assert stats["count"] == 4
+    assert stats["p50"] == 2.5
+    assert stats["p95"] is not None
+
+
+def test_video_index_excludes_stale_duplicate_outside_process_window(tmp_path):
+    name = "step=0--task=move_can_pot_trial_1_seed_1--success=False--ran={}.mp4"
+    stale = tmp_path / name.format("stale")
+    current = tmp_path / name.format("current")
+    stale.write_bytes(b"stale")
+    current.write_bytes(b"current")
+    os.utime(stale, (100.0, 100.0))
+    os.utime(current, (200.0, 200.0))
+    index = exp.video_index(
+        tmp_path,
+        started_at_utc=datetime.fromtimestamp(150, timezone.utc).isoformat(),
+        ended_at_utc=datetime.fromtimestamp(250, timezone.utc).isoformat(),
+    )
+    assert index == {
+        ("move_can_pot_trial_1_seed_1", False): [str(current.resolve())]
+    }

chapter7/openvla-robotwin2-eval/validation/runs/exp7-13-localgpu-20260803-v1/chunk_1/episodes.jsonl

@@ -0,0 +1,256 @@
+{"schema_version": 1, "experiment": "7-13", "source": "upstream_val_only", "recorded_at_utc": "2026-08-03T12:19:58.059821+00:00", "arm": "chunk_1", "upstream_commit": "7c51662df27b586f9e8a1ab35fcf849f2b8852f9", "task": "robotwin2_move_can_pot", "data_source": "robotwin2_move_can_pot_train_iid", "trial_id": 100007, "trial_seed": 100007, "success": false, "finish_action_steps": 200, "action_chunk_length": 1, "action_dimension": 14, "rgb_views": 3, "proprioception_enabled": true}
+{"schema_version": 1, "experiment": "7-13", "source": "upstream_val_only", "recorded_at_utc": "2026-08-03T12:19:58.059959+00:00", "arm": "chunk_1", "upstream_commit": "7c51662df27b586f9e8a1ab35fcf849f2b8852f9", "task": "robotwin2_move_can_pot", "data_source": "robotwin2_move_can_pot_train_iid", "trial_id": 100152, "trial_seed": 100152, "success": false, "finish_action_steps": 200, "action_chunk_length": 1, "action_dimension": 14, "rgb_views": 3, "proprioception_enabled": true}
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chapter7/openvla-robotwin2-eval/validation/runs/exp7-13-localgpu-20260803-v1/chunk_1/process.json

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chapter7/openvla-robotwin2-eval/validation/runs/exp7-13-localgpu-20260803-v1/chunk_25/process.json

@@ -0,0 +1,7 @@
+{
+  "arm": "chunk_25",
+  "started_at_utc": "2026-08-03T11:19:55.968555+00:00",
+  "ended_at_utc": "2026-08-03T11:37:16.883333+00:00",
+  "exit_code": 0,
+  "log": "/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/chunk_25/upstream.log"
+}

chapter7/openvla-robotwin2-eval/validation/runs/exp7-13-localgpu-20260803-v1/launch_manifest.json

@@ -0,0 +1,168 @@
+{
+  "schema_version": 1,
+  "experiment": "7-13",
+  "generated_at_utc": "2026-08-03T11:18:21.493471+00:00",
+  "real_execution_required": true,
+  "upstream_worktree": "/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/instrumented-upstream",
+  "upstream_commit": "7c51662df27b586f9e8a1ab35fcf849f2b8852f9",
+  "arms": {
+    "chunk_1": {
+      "action_chunk_length": 1,
+      "episode_evidence": "/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/chunk_1/episodes.jsonl",
+      "rollout_directory": "/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/instrumented-upstream/rollouts/exp7_13_move_can_pot_chunk_1",
+      "environment": {
+        "EXP7_13_EPISODE_JSONL": "/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/chunk_1/episodes.jsonl",
+        "EXP7_13_ARM": "chunk_1",
+        "EXP7_13_UPSTREAM_COMMIT": "7c51662df27b586f9e8a1ab35fcf849f2b8852f9",
+        "HYDRA_FULL_ERROR": "1",
+        "TOKENIZERS_PARALLELISM": "true",
+        "NCCL_DEBUG": "WARN",
+        "ROBOT_PLATFORM": "ALOHA",
+        "VERL_DISABLE_VLLM_IMPORT": "1"
+      },
+      "argv": [
+        "/home/ubuntu/.venvs/exp7-13-robotwin/bin/python",
+        "-u",
+        "-m",
+        "verl.trainer.main_ppo",
+        "data.task_suite_name=robotwin2_move_can_pot",
+        "data.num_trials_per_task=128",
+        "data.n_samples=1",
+        "data.filter_accuracy=False",
+        "data.train_batch_size=64",
+        "data.val_batch_size=8",
+        "data.max_prompt_length=256",
+        "data.max_response_length=128",
+        "actor_rollout_ref.model.path=/home/ubuntu/checkpoints/exp7-13-openvla-move-can-pot",
+        "actor_rollout_ref.model.vla=openvla-oft",
+        "actor_rollout_ref.model.action_token_len=14",
+        "actor_rollout_ref.model.action_chunks_len=1",
+        "actor_rollout_ref.model.resume=False",
+        "actor_rollout_ref.actor.num_images_in_input=3",
+        "actor_rollout_ref.actor.traj_mini_batch_size=8",
+        "actor_rollout_ref.actor.ppo_mini_batch_size=128",
+        "actor_rollout_ref.actor.ppo_micro_batch_size=8",
+        "actor_rollout_ref.actor.use_dynamic_bsz=False",
+        "actor_rollout_ref.actor.fsdp_config.param_offload=False",
+        "actor_rollout_ref.actor.fsdp_config.grad_offload=True",
+        "actor_rollout_ref.actor.fsdp_config.optimizer_offload=True",
+        "actor_rollout_ref.rollout.twin2_task_config=exp7_13_three_view",
+        "actor_rollout_ref.rollout.twin2_instruction_type=seen",
+        "actor_rollout_ref.rollout.num_images_in_input=3",
+        "+actor_rollout_ref.rollout.action_token_len=14",
+        "actor_rollout_ref.rollout.use_proprio=True",
+        "actor_rollout_ref.rollout.val_micro_batch_size=8",
+        "actor_rollout_ref.rollout.temperature=1.6",
+        "actor_rollout_ref.rollout.experiment_name=exp7_13_move_can_pot_chunk_1",
+        "actor_rollout_ref.rollout.micro_batch_size=1",
+        "actor_rollout_ref.rollout.unnorm_key=robotwin2_move_can_pot_1k",
+        "actor_rollout_ref.rollout.model_family=openvla",
+        "actor_rollout_ref.rollout.task_suite_name=robotwin2_move_can_pot",
+        "actor_rollout_ref.rollout.num_steps_wait=10",
+        "actor_rollout_ref.rollout.pretrained_checkpoint=/home/ubuntu/checkpoints/exp7-13-openvla-move-can-pot",
+        "actor_rollout_ref.rollout.center_crop=True",
+        "actor_rollout_ref.rollout.max_prompt_length=512",
+        "actor_rollout_ref.rollout.tensor_model_parallel_size=1",
+        "actor_rollout_ref.rollout.name=hf",
+        "actor_rollout_ref.rollout.gpu_memory_utilization=0.9",
+        "actor_rollout_ref.ref.log_prob_micro_batch_size=32",
+        "actor_rollout_ref.ref.fsdp_config.param_offload=True",
+        "algorithm.adv_estimator=grpo",
+        "algorithm.kl_ctrl.kl_coef=0.0",
+        "trainer.logger=['console']",
+        "trainer.project_name=AI-Agent-Book-Experiment-7-13",
+        "trainer.experiment_name=exp7_13_move_can_pot_chunk_1",
+        "trainer.default_local_dir=/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/chunk_1/checkpoints",
+        "trainer.n_gpus_per_node=1",
+        "trainer.nnodes=1",
+        "trainer.save_freq=-1",
+        "trainer.test_freq=-1",
+        "trainer.total_epochs=1",
+        "trainer.val_only=True",
+        "trainer.val_before_train=True",
+        "trainer.runtime_env=/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/instrumented-upstream/align.json",
+        "trainer.wandb_mode=disabled"
+      ],
+      "shell_command": "EXP7_13_EPISODE_JSONL=/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/chunk_1/episodes.jsonl EXP7_13_ARM=chunk_1 EXP7_13_UPSTREAM_COMMIT=7c51662df27b586f9e8a1ab35fcf849f2b8852f9 HYDRA_FULL_ERROR=1 TOKENIZERS_PARALLELISM=true NCCL_DEBUG=WARN ROBOT_PLATFORM=ALOHA VERL_DISABLE_VLLM_IMPORT=1 /home/ubuntu/.venvs/exp7-13-robotwin/bin/python -u -m verl.trainer.main_ppo data.task_suite_name=robotwin2_move_can_pot data.num_trials_per_task=128 data.n_samples=1 data.filter_accuracy=False data.train_batch_size=64 data.val_batch_size=8 data.max_prompt_length=256 data.max_response_length=128 actor_rollout_ref.model.path=/home/ubuntu/checkpoints/exp7-13-openvla-move-can-pot actor_rollout_ref.model.vla=openvla-oft actor_rollout_ref.model.action_token_len=14 actor_rollout_ref.model.action_chunks_len=1 actor_rollout_ref.model.resume=False actor_rollout_ref.actor.num_images_in_input=3 actor_rollout_ref.actor.traj_mini_batch_size=8 actor_rollout_ref.actor.ppo_mini_batch_size=128 actor_rollout_ref.actor.ppo_micro_batch_size=8 actor_rollout_ref.actor.use_dynamic_bsz=False actor_rollout_ref.actor.fsdp_config.param_offload=False actor_rollout_ref.actor.fsdp_config.grad_offload=True actor_rollout_ref.actor.fsdp_config.optimizer_offload=True actor_rollout_ref.rollout.twin2_task_config=exp7_13_three_view actor_rollout_ref.rollout.twin2_instruction_type=seen actor_rollout_ref.rollout.num_images_in_input=3 actor_rollout_ref.rollout.use_proprio=True actor_rollout_ref.rollout.val_micro_batch_size=8 actor_rollout_ref.rollout.temperature=1.6 actor_rollout_ref.rollout.experiment_name=exp7_13_move_can_pot_chunk_1 actor_rollout_ref.rollout.micro_batch_size=1 actor_rollout_ref.rollout.unnorm_key=robotwin2_move_can_pot_1k actor_rollout_ref.rollout.model_family=openvla actor_rollout_ref.rollout.task_suite_name=robotwin2_move_can_pot actor_rollout_ref.rollout.num_steps_wait=10 actor_rollout_ref.rollout.pretrained_checkpoint=/home/ubuntu/checkpoints/exp7-13-openvla-move-can-pot actor_rollout_ref.rollout.center_crop=True actor_rollout_ref.rollout.max_prompt_length=512 actor_rollout_ref.rollout.tensor_model_parallel_size=1 actor_rollout_ref.rollout.name=hf actor_rollout_ref.rollout.gpu_memory_utilization=0.9 actor_rollout_ref.ref.log_prob_micro_batch_size=32 actor_rollout_ref.ref.fsdp_config.param_offload=True algorithm.adv_estimator=grpo algorithm.kl_ctrl.kl_coef=0.0 'trainer.logger=['\"'\"'console'\"'\"']' trainer.project_name=AI-Agent-Book-Experiment-7-13 trainer.experiment_name=exp7_13_move_can_pot_chunk_1 trainer.default_local_dir=/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/chunk_1/checkpoints trainer.n_gpus_per_node=1 trainer.nnodes=1 trainer.save_freq=-1 trainer.test_freq=-1 trainer.total_epochs=1 trainer.val_only=True trainer.val_before_train=True trainer.runtime_env=/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/instrumented-upstream/align.json trainer.wandb_mode=disabled"
+    },
+    "chunk_25": {
+      "action_chunk_length": 25,
+      "episode_evidence": "/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/chunk_25/episodes.jsonl",
+      "rollout_directory": "/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/instrumented-upstream/rollouts/exp7_13_move_can_pot_chunk_25",
+      "environment": {
+        "EXP7_13_EPISODE_JSONL": "/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/chunk_25/episodes.jsonl",
+        "EXP7_13_ARM": "chunk_25",
+        "EXP7_13_UPSTREAM_COMMIT": "7c51662df27b586f9e8a1ab35fcf849f2b8852f9",
+        "HYDRA_FULL_ERROR": "1",
+        "TOKENIZERS_PARALLELISM": "true",
+        "NCCL_DEBUG": "WARN",
+        "ROBOT_PLATFORM": "ALOHA",
+        "VERL_DISABLE_VLLM_IMPORT": "1"
+      },
+      "argv": [
+        "/home/ubuntu/.venvs/exp7-13-robotwin/bin/python",
+        "-u",
+        "-m",
+        "verl.trainer.main_ppo",
+        "data.task_suite_name=robotwin2_move_can_pot",
+        "data.num_trials_per_task=128",
+        "data.n_samples=1",
+        "data.filter_accuracy=False",
+        "data.train_batch_size=64",
+        "data.val_batch_size=8",
+        "data.max_prompt_length=256",
+        "data.max_response_length=128",
+        "actor_rollout_ref.model.path=/home/ubuntu/checkpoints/exp7-13-openvla-move-can-pot",
+        "actor_rollout_ref.model.vla=openvla-oft",
+        "actor_rollout_ref.model.action_token_len=14",
+        "actor_rollout_ref.model.action_chunks_len=25",
+        "actor_rollout_ref.model.resume=False",
+        "actor_rollout_ref.actor.num_images_in_input=3",
+        "actor_rollout_ref.actor.traj_mini_batch_size=8",
+        "actor_rollout_ref.actor.ppo_mini_batch_size=128",
+        "actor_rollout_ref.actor.ppo_micro_batch_size=8",
+        "actor_rollout_ref.actor.use_dynamic_bsz=False",
+        "actor_rollout_ref.actor.fsdp_config.param_offload=False",
+        "actor_rollout_ref.actor.fsdp_config.grad_offload=True",
+        "actor_rollout_ref.actor.fsdp_config.optimizer_offload=True",
+        "actor_rollout_ref.rollout.twin2_task_config=exp7_13_three_view",
+        "actor_rollout_ref.rollout.twin2_instruction_type=seen",
+        "actor_rollout_ref.rollout.num_images_in_input=3",
+        "+actor_rollout_ref.rollout.action_token_len=14",
+        "actor_rollout_ref.rollout.use_proprio=True",
+        "actor_rollout_ref.rollout.val_micro_batch_size=8",
+        "actor_rollout_ref.rollout.temperature=1.6",
+        "actor_rollout_ref.rollout.experiment_name=exp7_13_move_can_pot_chunk_25",
+        "actor_rollout_ref.rollout.micro_batch_size=1",
+        "actor_rollout_ref.rollout.unnorm_key=robotwin2_move_can_pot_1k",
+        "actor_rollout_ref.rollout.model_family=openvla",
+        "actor_rollout_ref.rollout.task_suite_name=robotwin2_move_can_pot",
+        "actor_rollout_ref.rollout.num_steps_wait=10",
+        "actor_rollout_ref.rollout.pretrained_checkpoint=/home/ubuntu/checkpoints/exp7-13-openvla-move-can-pot",
+        "actor_rollout_ref.rollout.center_crop=True",
+        "actor_rollout_ref.rollout.max_prompt_length=512",
+        "actor_rollout_ref.rollout.tensor_model_parallel_size=1",
+        "actor_rollout_ref.rollout.name=hf",
+        "actor_rollout_ref.rollout.gpu_memory_utilization=0.9",
+        "actor_rollout_ref.ref.log_prob_micro_batch_size=32",
+        "actor_rollout_ref.ref.fsdp_config.param_offload=True",
+        "algorithm.adv_estimator=grpo",
+        "algorithm.kl_ctrl.kl_coef=0.0",
+        "trainer.logger=['console']",
+        "trainer.project_name=AI-Agent-Book-Experiment-7-13",
+        "trainer.experiment_name=exp7_13_move_can_pot_chunk_25",
+        "trainer.default_local_dir=/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/chunk_25/checkpoints",
+        "trainer.n_gpus_per_node=1",
+        "trainer.nnodes=1",
+        "trainer.save_freq=-1",
+        "trainer.test_freq=-1",
+        "trainer.total_epochs=1",
+        "trainer.val_only=True",
+        "trainer.val_before_train=True",
+        "trainer.runtime_env=/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/instrumented-upstream/align.json",
+        "trainer.wandb_mode=disabled"
+      ],
+      "shell_command": "EXP7_13_EPISODE_JSONL=/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/chunk_25/episodes.jsonl EXP7_13_ARM=chunk_25 EXP7_13_UPSTREAM_COMMIT=7c51662df27b586f9e8a1ab35fcf849f2b8852f9 HYDRA_FULL_ERROR=1 TOKENIZERS_PARALLELISM=true NCCL_DEBUG=WARN ROBOT_PLATFORM=ALOHA VERL_DISABLE_VLLM_IMPORT=1 /home/ubuntu/.venvs/exp7-13-robotwin/bin/python -u -m verl.trainer.main_ppo data.task_suite_name=robotwin2_move_can_pot data.num_trials_per_task=128 data.n_samples=1 data.filter_accuracy=False data.train_batch_size=64 data.val_batch_size=8 data.max_prompt_length=256 data.max_response_length=128 actor_rollout_ref.model.path=/home/ubuntu/checkpoints/exp7-13-openvla-move-can-pot actor_rollout_ref.model.vla=openvla-oft actor_rollout_ref.model.action_token_len=14 actor_rollout_ref.model.action_chunks_len=25 actor_rollout_ref.model.resume=False actor_rollout_ref.actor.num_images_in_input=3 actor_rollout_ref.actor.traj_mini_batch_size=8 actor_rollout_ref.actor.ppo_mini_batch_size=128 actor_rollout_ref.actor.ppo_micro_batch_size=8 actor_rollout_ref.actor.use_dynamic_bsz=False actor_rollout_ref.actor.fsdp_config.param_offload=False actor_rollout_ref.actor.fsdp_config.grad_offload=True actor_rollout_ref.actor.fsdp_config.optimizer_offload=True actor_rollout_ref.rollout.twin2_task_config=exp7_13_three_view actor_rollout_ref.rollout.twin2_instruction_type=seen actor_rollout_ref.rollout.num_images_in_input=3 actor_rollout_ref.rollout.use_proprio=True actor_rollout_ref.rollout.val_micro_batch_size=8 actor_rollout_ref.rollout.temperature=1.6 actor_rollout_ref.rollout.experiment_name=exp7_13_move_can_pot_chunk_25 actor_rollout_ref.rollout.micro_batch_size=1 actor_rollout_ref.rollout.unnorm_key=robotwin2_move_can_pot_1k actor_rollout_ref.rollout.model_family=openvla actor_rollout_ref.rollout.task_suite_name=robotwin2_move_can_pot actor_rollout_ref.rollout.num_steps_wait=10 actor_rollout_ref.rollout.pretrained_checkpoint=/home/ubuntu/checkpoints/exp7-13-openvla-move-can-pot actor_rollout_ref.rollout.center_crop=True actor_rollout_ref.rollout.max_prompt_length=512 actor_rollout_ref.rollout.tensor_model_parallel_size=1 actor_rollout_ref.rollout.name=hf actor_rollout_ref.rollout.gpu_memory_utilization=0.9 actor_rollout_ref.ref.log_prob_micro_batch_size=32 actor_rollout_ref.ref.fsdp_config.param_offload=True algorithm.adv_estimator=grpo algorithm.kl_ctrl.kl_coef=0.0 'trainer.logger=['\"'\"'console'\"'\"']' trainer.project_name=AI-Agent-Book-Experiment-7-13 trainer.experiment_name=exp7_13_move_can_pot_chunk_25 trainer.default_local_dir=/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/chunk_25/checkpoints trainer.n_gpus_per_node=1 trainer.nnodes=1 trainer.save_freq=-1 trainer.test_freq=-1 trainer.total_epochs=1 trainer.val_only=True trainer.val_before_train=True trainer.runtime_env=/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/runs/exp7-13-localgpu-20260803-v1/instrumented-upstream/align.json trainer.wandb_mode=disabled"
+    }
+  }
+}

chapter7/openvla-robotwin2-eval/validation/runs/exp7-13-localgpu-20260803-v1/manifest.json.sha256

@@ -0,0 +1 @@
+fe70e656dfc5514ae4513474817691b072613b9050051d8699210bbda21b7d45  manifest.json

chapter7/openvla-robotwin2-eval/validation/runs/exp7-13-localgpu-20260803-v1/preflight.json

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+{
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+    "expected_checkpoint_revision": "a1f0ac0aa32d31ffab7131b0eda3e11719f39ee5",
+    "task": "move_can_pot",
+    "task_suite": "robotwin2_move_can_pot",
+    "task_config": "exp7_13_three_view",
+    "checkpoint_env": "OPENVLA_CHECKPOINT",
+    "robotwin2_env": "ROBOTWIN2_PATH",
+    "align_path_env": "SIMPLEVLA_ALIGN_PATH",
+    "model_family": "openvla",
+    "vla": "openvla-oft",
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+    ]
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+        "required": [
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+          "verl/trainer/ppo/ray_trainer.py",
+          "verl/utils/dataset/rob_dataset.py",
+          "verl/workers/rollout/rob_rollout.py",
+          "modified_codes/robotwin2/envs/move_can_pot.py",
+          "modified_codes/robotwin2/task_config/demo_randomized.yml",
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+      "ok": true,
+      "required": true,
+      "evidence": {
+        "environment": "OPENVLA_CHECKPOINT",
+        "path": "/home/ubuntu/checkpoints/exp7-13-openvla-move-can-pot",
+        "checkpoint_id": "Haozhan72/twin2_move_can_pot_seed1k_sft_single_20k",
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+      "ok": true,
+      "required": true,
+      "evidence": {
+        "environment": "ROBOTWIN2_PATH",
+        "path": "/home/ubuntu/RoboTwin2",
+        "markers": [
+          "envs",
+          "task_config",
+          "script"
+        ]
+      }
+    },
+    "pinned_robotwin2_commit": {
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+      "required": true,
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+        "expected": "13c3c47ff4312dd62484bcd51be034af55c062d1"
+      }
+    },
+    "ray_runtime_environment": {
+      "ok": true,
+      "required": true,
+      "evidence": {
+        "environment": "SIMPLEVLA_ALIGN_PATH",
+        "path": "/home/ubuntu/ai-agent-book/chapter7/SimpleVLA-RL/SimpleVLA-RL/align.json"
+      }
+    },
+    "nvidia_gpu_count": {
+      "ok": true,
+      "required": true,
+      "evidence": {
+        "observed": 1,
+        "required": 1,
+        "gpus": [
+          {
+            "index": 0,
+            "name": "NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
+            "memory_mib": 97887,
+            "driver": "595.71.05"
+          }
+        ],
+        "error": null
+      }
+    },
+    "three_rgb_view_config": {
+      "ok": true,
+      "required": true,
+      "evidence": "/home/ubuntu/ai-agent-book/chapter7/openvla-robotwin2-eval/task_config_exp7_13_three_view.yml"
+    }
+  },
+  "ready_for_real_validation": true,
+  "blocking_checks": [],
+  "acceptance_note": "Preflight readiness is necessary but never sufficient for Experiment 7-13 completion."
+}

chapter7/openvla-robotwin2-eval/validation/runs/exp7-13-localgpu-20260803-v1/report.json

@@ -0,0 +1,147 @@
+{
+  "schema_version": 1,
+  "experiment": "7-13",
+  "generated_at_utc": "2026-08-04T04:38:22.151984+00:00",
+  "evidence_policy": {
+    "paper_numbers_accepted": false,
+    "historical_rollouts_accepted": false,
+    "dry_run_commands_accepted": false,
+    "required_source": "instrumented upstream val-only execution"
+  },
+  "configuration": {
+    "experiment": "7-13",
+    "upstream_path": "/tmp/ai-agent-book-exp7-13-pr/chapter7/SimpleVLA-RL/SimpleVLA-RL",
+    "expected_upstream_commit": "7c51662df27b586f9e8a1ab35fcf849f2b8852f9",
+    "expected_robotwin2_commit": "13c3c47ff4312dd62484bcd51be034af55c062d1",
+    "checkpoint_id": "Haozhan72/twin2_move_can_pot_seed1k_sft_single_20k",
+    "expected_checkpoint_revision": "a1f0ac0aa32d31ffab7131b0eda3e11719f39ee5",
+    "task": "move_can_pot",
+    "task_suite": "robotwin2_move_can_pot",
+    "task_config": "exp7_13_three_view",
+    "checkpoint_env": "OPENVLA_CHECKPOINT",
+    "robotwin2_env": "ROBOTWIN2_PATH",
+    "align_path_env": "SIMPLEVLA_ALIGN_PATH",
+    "model_family": "openvla",
+    "vla": "openvla-oft",
+    "action_dimension": 14,
+    "action_token_length": 14,
+    "action_chunks": [
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+      25
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+    "proprio_dimension": 14,
+    "control_hz": 50,
+    "iid_validation_seeds": 128,
+    "ood_validation_seeds": 128,
+    "gpus_required": 1,
+    "failure_modes": [
+      "grasp_failure",
+      "transport_collision",
+      "placement_position_error",
+      "placement_orientation_error",
+      "gripper_release_failure",
+      "timeout",
+      "environment_error",
+      "other_observed_failure"
+    ]
+  },
+  "arms": {
+    "chunk_1": {
+      "action_chunk_length": 1,
+      "episodes": 256,
+      "successes": 0,
+      "success_rate": 0.0,
+      "by_split": {
+        "iid": {
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+          "successes": 0,
+          "success_rate": 0.0
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+        "ood": {
+          "episodes": 128,
+          "successes": 0,
+          "success_rate": 0.0
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+      "completion_action_steps": {
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+        "mean": 200.0,
+        "std": 0.0,
+        "min": 200.0,
+        "p50": 200.0,
+        "p95": 200.0,
+        "max": 200.0
+      },
+      "completion_control_seconds_at_50hz": {
+        "count": 256,
+        "mean": 4.0,
+        "std": 0.0,
+        "min": 4.0,
+        "p50": 4.0,
+        "p95": 4.0,
+        "max": 4.0
+      },
+      "failure_modes": {
+        "timeout": 256
+      },
+      "unclassified_failure_count": 0,
+      "missing_video_count": 0,
+      "unmatched_video_count": 0
+    },
+    "chunk_25": {
+      "action_chunk_length": 25,
+      "episodes": 256,
+      "successes": 26,
+      "success_rate": 0.1015625,
+      "by_split": {
+        "iid": {
+          "episodes": 128,
+          "successes": 13,
+          "success_rate": 0.1015625
+        },
+        "ood": {
+          "episodes": 128,
+          "successes": 13,
+          "success_rate": 0.1015625
+        }
+      },
+      "completion_action_steps": {
+        "count": 256,
+        "mean": 190.13671875,
+        "std": 29.939405918177442,
+        "min": 50.0,
+        "p50": 200.0,
+        "p95": 200.0,
+        "max": 200.0
+      },
+      "completion_control_seconds_at_50hz": {
+        "count": 256,
+        "mean": 3.802734375,
+        "std": 0.5987881183635488,
+        "min": 1.0,
+        "p50": 4.0,
+        "p95": 4.0,
+        "max": 4.0
+      },
+      "failure_modes": {
+        "timeout": 230
+      },
+      "unclassified_failure_count": 0,
+      "missing_video_count": 0,
+      "unmatched_video_count": 0
+    }
+  },
+  "controlled_action_chunk_comparison": {
+    "complete_pairs": 256,
+    "chunk_25_wins": 26,
+    "chunk_1_wins": 0,
+    "ties": 230,
+    "mean_paired_success_delta": 0.1015625,
+    "unpaired_rows": 0
+  },
+  "strict_completion": {
+    "complete": true,
+    "errors": []
+  }
+}

chapter7/openvla-robotwin2-eval/validation/runs/exp7-13-localgpu-20260803-v1/report.md

@@ -0,0 +1,30 @@
+# Experiment 7-13 OpenVLA + RoboTwin2 evaluation
+
+Official completion: **True**
+
+| Arm | Episodes | Success rate | IID | OOD | Completion p50 / p95 (50 Hz seconds) |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| chunk_1 | 256 | 0.0 | 0.0 | 0.0 | 4.0 / 4.0 |
+| chunk_25 | 256 | 0.1015625 | 0.1015625 | 0.1015625 | 4.0 / 4.0 |
+
+## Controlled comparison
+
+```json
+{
+  "complete_pairs": 256,
+  "chunk_25_wins": 26,
+  "chunk_1_wins": 0,
+  "ties": 230,
+  "mean_paired_success_delta": 0.1015625,
+  "unpaired_rows": 0
+}
+```
+
+## Failure modes
+
+- chunk_1: {"timeout": 256}
+- chunk_25: {"timeout": 230}
+
+## Completion audit
+
+- Every direct manuscript gate is supported by real per-episode evidence.