ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter6/xlerobot-teleoperation/README.md

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+# 实验 6-10：专家控制建立机器人能力上限
+
+本目录对应实验 6-10；运行器、验证器与证据中的实验标识均已统一为 `6-10`。
+
+这是一个可在本机 GPU 上完成的、非致动的桌面操作上限实验。它用批量二维桌面模拟器实现“像遥操作员一样直接把物体移到目标”的专家控制器，目的是建立后续自主策略的上限和基准，不把模拟结果冒充成 XLeRobot 真机结果。
+
+## 运行
+
+```bash
+cd chapter6/xlerobot-teleoperation
+python teleop.py --episodes 512 --object-counts 1,2,3,4 --seeds 20260808,20260809,20260810,20260811,20260812 --output-dir validation/runs/local-gpu
+python validate_evidence.py validation/runs/local-gpu/evidence.json
+```
+
+脚本优先使用 CUDA，其次使用 Apple MPS；默认拒绝 CPU 回退。正式协议使用 5 个随机种子、4 种物体数量和每格 512 个回合，共 10240 个回合，并额外重复一个固定条件检查结果是否一致。`--allow-cpu` 只用于调试，不能作为正文实验结果。输出包括 GPU 信息、每个条件的成功率、步数、路径长度、指标文件哈希，以及一份明确标注为“需要硬件和安全条件”的 XLeRobot 真机扩展状态。
+
+## 观察重点
+
+- 专家控制器在随机物体位置上是否稳定完成所有目标；
+- 完成时间和路径长度的分布；
+- 这个结果只是“硬件加上一个理想控制者”的上限，不代表自主策略已经达到该水平。
+
+## 真机扩展
+
+XLeRobot 的键盘、Xbox、Joy-Con 和 VR 入口仍由 `upstream.lock.json` 记录，但它们需要真实机械臂、校准、急停和现场观察员。本实验的本地 GPU 验收不会打开串口，也不会执行任何真机动作；只有获得明确授权后，才可另行运行硬件 teleop 复现。

chapter6/xlerobot-teleoperation/evidence.blocked.example.json

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+{
+  "schema_version": "1.0",
+  "experiment_id": "6-10",
+  "status": "blocked",
+  "upstream": {
+    "repository": "https://github.com/Vector-Wangel/XLeRobot.git",
+    "commit": "3d14695e40c9c68229c0aacffca6053c75cd3eb6",
+    "guide_path": "docs/en/source/software/getting_started/XLeRobot_teleop.md",
+    "guide_blob": "3992358282ff54cfce8d90a525e784aedcf045f7"
+  },
+  "run": {
+    "started_at": null,
+    "ended_at": null,
+    "operator": null,
+    "host": "documentation-only-host",
+    "actuation_authorized": false
+  },
+  "safety": {
+    "robot_calibrated": false,
+    "clear_workspace": false,
+    "emergency_stop_ready": false,
+    "human_observer_present": false
+  },
+  "modes": [],
+  "task_outcomes": [],
+  "artifacts": [],
+  "blockers": [
+    "No XLeRobot hardware or explicit robot-actuation authorization was available; no physical run was attempted."
+  ]
+}

chapter6/xlerobot-teleoperation/evidence.schema.json

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+{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "$id": "https://ai-agent-book.local/schemas/experiment-6-10-local-gpu.json",
+  "title": "Experiment 6-10 local GPU expert upper bound evidence",
+  "type": "object",
+  "required": ["schema_version", "experiment_id", "status", "kind", "metrics", "artifacts", "hardware_extension", "blockers"],
+  "properties": {
+    "schema_version": {"const": "3.0"},
+    "experiment_id": {"const": "6-10"},
+    "status": {"const": "complete"},
+    "kind": {"const": "local_gpu_expert_upper_bound"},
+    "metrics": {"type": "object"},
+    "artifacts": {"type": "array"},
+    "hardware_extension": {"type": "object"},
+    "blockers": {"type": "array"}
+  },
+  "additionalProperties": true
+}

chapter6/xlerobot-teleoperation/preflight.py

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+#!/usr/bin/env python3
+"""Read-only preflight for the pinned Experiment 6-10 reproduction track."""
+
+from __future__ import annotations
+
+import argparse
+import importlib.util
+import json
+import platform
+import subprocess
+from datetime import datetime, timezone
+from pathlib import Path
+
+COMMIT = "3d14695e40c9c68229c0aacffca6053c75cd3eb6"
+PINNED_BLOBS = {
+    "docs/en/source/software/getting_started/XLeRobot_teleop.md": "3992358282ff54cfce8d90a525e784aedcf045f7",
+    "software/examples/4_xlerobot_teleop_keyboard.py": "efbe076dfbda3c6280fa54f0eb5bca1a12518a0d",
+    "software/examples/5_xlerobot_teleop_xbox.py": "de7bc17d570167e58b15e38c06c0fa23af74632a",
+    "software/examples/7_xlerobot_teleop_joycon.py": "21a48258d22b1fc002f63555a2f3dc2950bdfb24",
+    "software/examples/8_xlerobot_teleop_vr.py": "315bb81f13a37746de0f329e3ba11240a2230806",
+}
+
+
+def git(repo: Path, *args: str) -> str:
+    return subprocess.run(["git", "-C", str(repo), *args], check=False, capture_output=True, text=True).stdout.strip()
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--upstream", type=Path, required=True, help="local checkout of Vector-Wangel/XLeRobot")
+    parser.add_argument("--serial-port", action="append", default=[], help="expected robot serial device; checked by path only")
+    parser.add_argument("--safety-checklist-complete", action="store_true", help="operator attests calibration, clear workspace, observer, and E-stop")
+    parser.add_argument("--output", type=Path)
+    args = parser.parse_args()
+    checks: list[dict[str, object]] = []
+
+    def add(check_id: str, passed: bool, detail: str, required: bool = True) -> None:
+        checks.append({"id": check_id, "passed": passed, "required_for_hardware_run": required, "detail": detail})
+
+    head = git(args.upstream, "rev-parse", "HEAD")
+    add("pinned_commit", head == COMMIT, f"expected {COMMIT}; found {head or 'not a git checkout'}")
+    for path, blob in PINNED_BLOBS.items():
+        found = git(args.upstream, "rev-parse", f"HEAD:{path}")
+        add(f"blob:{path}", found == blob, f"expected {blob}; found {found or 'missing'}")
+    for module in ("numpy", "pygame", "lerobot", "joyconrobotics"):
+        found = importlib.util.find_spec(module) is not None
+        add(f"python_module:{module}", found, "installed" if found else "not importable")
+    add("serial_ports_supplied", bool(args.serial_port), "no serial ports supplied" if not args.serial_port else ", ".join(args.serial_port))
+    for device in args.serial_port:
+        add(f"device:{device}", Path(device).exists(), "path exists" if Path(device).exists() else "path missing")
+    add("safety_checklist", args.safety_checklist_complete, "operator attestation present" if args.safety_checklist_complete else "not attested")
+
+    blockers = [str(item["id"]) for item in checks if item["required_for_hardware_run"] and not item["passed"]]
+    report = {
+        "schema_version": "1.0",
+        "experiment_id": "6-10",
+        "kind": "non_actuating_preflight",
+        "generated_at": datetime.now(timezone.utc).isoformat(),
+        "host": platform.node() or "unknown",
+        "upstream_path": str(args.upstream.resolve()),
+        "status": "ready" if not blockers else "blocked",
+        "checks": checks,
+        "blockers": blockers,
+        "actuation_attempted": False,
+    }
+    rendered = json.dumps(report, indent=2) + "\n"
+    if args.output:
+        args.output.parent.mkdir(parents=True, exist_ok=True)
+        args.output.write_text(rendered, encoding="utf-8")
+        print(f"wrote {args.output}")
+    else:
+        print(rendered, end="")
+    return 0 if not blockers else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

chapter6/xlerobot-teleoperation/teleop.py

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+#!/usr/bin/env python3
+"""Experiment 6-10: local GPU expert-control upper-bound benchmark.
+
+This is the reproducible, non-actuating companion for the chapter.  It uses a
+small batched tabletop simulator to measure what a perfect teleoperator-like
+controller can do.  The pinned XLeRobot hardware path remains an optional,
+explicitly gated extension documented in README.md.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+import time
+from pathlib import Path
+
+import torch
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+from robotics_lab_common import device_info, relative_or_absolute, select_device, seed_everything, sha256, write_json
+
+
+def run_upper_bound(episodes: int, objects: int, seed: int, device: torch.device) -> dict[str, object]:
+    if objects < 1 or objects > 4:
+        raise ValueError("objects must be between 1 and 4")
+    generator = torch.Generator(device=device).manual_seed(seed)
+    object_xy = torch.rand((episodes, objects, 2), generator=generator, device=device) * 0.60 + 0.20
+    target_xy = torch.rand((episodes, objects, 2), generator=generator, device=device) * 0.60 + 0.20
+    ee = torch.full((episodes, 2), 0.50, dtype=torch.float32, device=device)
+    current = torch.zeros(episodes, dtype=torch.long, device=device)
+    phase = torch.zeros(episodes, dtype=torch.long, device=device)  # 0=approach, 1=carry, 2=advance
+    finished = torch.zeros(episodes, dtype=torch.bool, device=device)
+    path = torch.zeros(episodes, dtype=torch.float32, device=device)
+    steps = torch.zeros(episodes, dtype=torch.long, device=device)
+    max_steps = 900
+    speed = 0.018
+    tolerance = 0.025
+
+    for _ in range(max_steps):
+        active = ~finished
+        if not bool(active.any().item()):
+            break
+        idx = current.clamp(max=objects - 1)
+        obj = object_xy[torch.arange(episodes, device=device), idx]
+        target = target_xy[torch.arange(episodes, device=device), idx]
+        destination = torch.where((phase == 1).unsqueeze(1), target, obj)
+        delta = destination - ee
+        distance = torch.linalg.vector_norm(delta, dim=1)
+        step = delta / distance.clamp_min(1e-6).unsqueeze(1) * speed
+        step = torch.where((distance < speed).unsqueeze(1), delta, step)
+        step = torch.where(active.unsqueeze(1), step, torch.zeros_like(step))
+        ee = ee + step
+        path += torch.linalg.vector_norm(step, dim=1)
+        steps += active.to(torch.long)
+        arrived = distance <= tolerance
+        phase = torch.where(active & (phase == 0) & arrived, torch.ones_like(phase), phase)
+        arrived_target = active & (phase == 1) & arrived
+        phase = torch.where(arrived_target, torch.full_like(phase, 2), phase)
+        current = torch.where(arrived_target, current + 1, current)
+        phase = torch.where((phase == 2) & (current < objects), torch.zeros_like(phase), phase)
+        finished = current >= objects
+        phase = torch.where(finished, torch.full_like(phase, 3), phase)
+
+    success = finished
+    return {
+        "seed": seed,
+        "episodes": episodes,
+        "objects_per_episode": objects,
+        "max_steps": max_steps,
+        "control_hz": 20,
+        "successes": int(success.sum().item()),
+        "success_rate": float(success.float().mean().item()),
+        "mean_steps": float(steps.float().mean().item()),
+        "p95_steps": float(torch.quantile(steps.float(), 0.95).item()),
+        "mean_path_length_m": float(path.mean().item()),
+        "device": device_info(device),
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--episodes", type=int, default=512, help="episodes per seed/object-count cell")
+    parser.add_argument("--object-counts", default="1,2,3,4")
+    parser.add_argument("--seeds", default="20260808,20260809,20260810,20260811,20260812")
+    parser.add_argument("--output-dir", type=Path, default=Path(__file__).parent / "validation" / "runs" / "local-gpu")
+    parser.add_argument("--allow-cpu", action="store_true", help="debug only; the book gate requires an accelerator")
+    args = parser.parse_args()
+    if args.episodes < 128:
+        parser.error("--episodes must be at least 128 for the benchmark protocol")
+    try:
+        object_counts = [int(value) for value in args.object_counts.split(",")]
+        seeds = [int(value) for value in args.seeds.split(",")]
+    except ValueError:
+        parser.error("--object-counts and --seeds must be comma-separated integers")
+    if not object_counts or any(value < 1 or value > 4 for value in object_counts):
+        parser.error("object counts must be in 1..4")
+    if len(seeds) < 3:
+        parser.error("at least three independent seeds are required")
+    if args.episodes < 1:
+        parser.error("--episodes must be positive")
+    seed_everything(seeds[0])
+    try:
+        device = select_device(not args.allow_cpu)
+    except RuntimeError as exc:
+        parser.error(str(exc))
+    started = time.perf_counter()
+    cells = [run_upper_bound(args.episodes, objects, seed, device) for seed in seeds for objects in object_counts]
+    replay_a = run_upper_bound(128, object_counts[0], seeds[0], device)
+    replay_b = run_upper_bound(128, object_counts[0], seeds[0], device)
+    deterministic = replay_a == replay_b
+    metrics = {
+        "device": device_info(device),
+        "protocol": {"seeds": seeds, "object_counts": object_counts, "episodes_per_cell": args.episodes, "total_episodes": len(cells) * args.episodes},
+        "cells": cells,
+        "aggregate_success_rate": sum(cell["success_rate"] for cell in cells) / len(cells),
+        "worst_cell_success_rate": min(cell["success_rate"] for cell in cells),
+        "max_p95_steps": max(cell["p95_steps"] for cell in cells),
+        "deterministic_replay": deterministic,
+        "wall_time_ms": round((time.perf_counter() - started) * 1000, 3),
+    }
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    metrics_path = args.output_dir / "metrics.json"
+    cells_path = args.output_dir / "cells.json"
+    write_json(metrics_path, metrics)
+    write_json(cells_path, {"cells": cells})
+    receipt = {
+        "schema_version": "3.0",
+        "experiment_id": "6-10",
+        "status": "complete",
+        "kind": "local_gpu_expert_upper_bound",
+        "seed": seeds[0],
+        "run": {"accelerator_required": not args.allow_cpu},
+        "metrics": metrics,
+        "artifacts": [{"kind": "metrics", "path": relative_or_absolute(metrics_path, args.output_dir), "sha256": sha256(metrics_path)}, {"kind": "cells", "path": relative_or_absolute(cells_path, args.output_dir), "sha256": sha256(cells_path)}],
+        "hardware_extension": {"status": "gated", "actuation_attempted": False, "upstream": "Vector-Wangel/XLeRobot"},
+        "blockers": [] if not args.allow_cpu else ["CPU debug mode is not a GPU acceptance run"],
+    }
+    evidence_path = args.output_dir / "evidence.json"
+    write_json(evidence_path, receipt)
+    print(json.dumps(receipt, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

chapter6/xlerobot-teleoperation/test_validator.py

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+import json
+import subprocess
+import sys
+import unittest
+from pathlib import Path
+
+from validate_evidence import validate
+
+
+class EvidenceGateTests(unittest.TestCase):
+    def test_local_gpu_evidence_is_accepted(self):
+        run = Path(__file__).parent / "validation" / "runs" / "local-gpu" / "evidence.json"
+        if not run.is_file():
+            self.skipTest("run the local GPU experiment first")
+        data = json.loads(run.read_text(encoding="utf-8"))
+        self.assertEqual(validate(data, run.parent), [])
+
+    def test_cpu_claim_is_rejected(self):
+        data = {
+            "schema_version": "3.0",
+            "experiment_id": "6-10",
+            "status": "complete",
+            "kind": "local_gpu_expert_upper_bound",
+            "metrics": {"device": {"device": "cpu"}, "protocol": {"seeds": [1, 2, 3], "object_counts": [1, 2, 3], "total_episodes": 2048}, "cells": [], "worst_cell_success_rate": 1.0, "deterministic_replay": True},
+            "artifacts": [],
+            "hardware_extension": {"actuation_attempted": False},
+        }
+        self.assertTrue(validate(data))
+
+    def test_runner_requires_accelerator_by_default(self):
+        runner = Path(__file__).with_name("teleop.py")
+        result = subprocess.run([sys.executable, str(runner), "--help"], capture_output=True, text=True, check=False)
+        self.assertEqual(result.returncode, 0)
+        self.assertIn("local GPU", result.stdout)
+
+
+if __name__ == "__main__":
+    unittest.main()

chapter6/xlerobot-teleoperation/upstream.lock.json

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+{
+  "experiment_id": "6-10",
+  "repository": "https://github.com/Vector-Wangel/XLeRobot.git",
+  "commit": "3d14695e40c9c68229c0aacffca6053c75cd3eb6",
+  "guide": {
+    "published_url": "https://xlerobot.readthedocs.io/en/latest/software/getting_started/XLeRobot_teleop.html",
+    "path": "docs/en/source/software/getting_started/XLeRobot_teleop.md",
+    "git_blob": "3992358282ff54cfce8d90a525e784aedcf045f7"
+  },
+  "entrypoints": {
+    "keyboard": {
+      "path": "software/examples/4_xlerobot_teleop_keyboard.py",
+      "git_blob": "efbe076dfbda3c6280fa54f0eb5bca1a12518a0d"
+    },
+    "xbox": {
+      "path": "software/examples/5_xlerobot_teleop_xbox.py",
+      "git_blob": "de7bc17d570167e58b15e38c06c0fa23af74632a"
+    },
+    "joycon": {
+      "path": "software/examples/7_xlerobot_teleop_joycon.py",
+      "git_blob": "21a48258d22b1fc002f63555a2f3dc2950bdfb24"
+    },
+    "vr": {
+      "path": "software/examples/8_xlerobot_teleop_vr.py",
+      "git_blob": "315bb81f13a37746de0f329e3ba11240a2230806"
+    }
+  },
+  "verified_at": "2026-07-29"
+}

chapter6/xlerobot-teleoperation/validate_evidence.py

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+#!/usr/bin/env python3
+"""Validate the honest local GPU evidence for Experiment 6-10."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import sys
+from pathlib import Path
+from typing import Any
+
+
+def file_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 validate(data: dict[str, Any], evidence_dir: Path | None = None) -> list[str]:
+    errors: list[str] = []
+
+    def expect(condition: bool, message: str) -> None:
+        if not condition:
+            errors.append(message)
+
+    expect(data.get("schema_version") == "3.0", "schema_version must be 3.0")
+    expect(data.get("experiment_id") == "6-10", "experiment_id must be 6-10")
+    expect(data.get("kind") == "local_gpu_expert_upper_bound", "wrong evidence kind")
+    expect(data.get("status") == "complete", "local evidence must be complete")
+    metrics = data.get("metrics", {})
+    expect(metrics.get("device", {}).get("device") in {"mps", "cuda"}, "evidence must use a local GPU accelerator")
+    protocol = metrics.get("protocol", {})
+    expect(len(protocol.get("seeds", [])) >= 3, "at least three seeds are required")
+    expect(len(protocol.get("object_counts", [])) >= 3, "at least three object-count conditions are required")
+    expect(protocol.get("total_episodes", 0) >= 2000, "at least 2000 benchmark episodes are required")
+    cells = metrics.get("cells", [])
+    expect(len(cells) == len(protocol.get("seeds", [])) * len(protocol.get("object_counts", [])), "one result cell is required per seed/object-count pair")
+    expect(metrics.get("worst_cell_success_rate") == 1.0, "expert upper bound must solve every benchmark cell")
+    expect(metrics.get("deterministic_replay") is True, "repeating a fixed seed must reproduce the same metrics")
+    artifacts = data.get("artifacts", [])
+    expect(len(artifacts) == 2, "metrics and episode-cell artifacts are required")
+    if evidence_dir is not None:
+        for artifact in artifacts:
+            path = evidence_dir / str(artifact.get("path", ""))
+            expect(path.is_file(), f"artifact does not exist: {path}")
+            if path.is_file():
+                expect(file_sha256(path) == artifact.get("sha256"), f"artifact hash mismatch: {path.name}")
+    extension = data.get("hardware_extension", {})
+    expect(extension.get("actuation_attempted") is False, "local run must not claim hardware actuation")
+    return errors
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("evidence", type=Path)
+    args = parser.parse_args()
+    try:
+        data = json.loads(args.evidence.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError) as exc:
+        print(f"INVALID: {exc}", file=sys.stderr)
+        return 2
+    errors = validate(data, args.evidence.resolve().parent)
+    if errors:
+        print("INVALID")
+        for error in errors:
+            print(f"- {error}")
+        return 1
+    print("VALID: experiment 6-10 local GPU evidence")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

chapter6/xlerobot-teleoperation/validation/preflight_2026-07-29.json

@@ -0,0 +1,90 @@
+{
+  "schema_version": "1.0",
+  "experiment_id": "6-10",
+  "kind": "non_actuating_preflight",
+  "generated_at": "2026-07-29T15:32:08.929534+00:00",
+  "host": "bojMacBook-Pro.local",
+  "upstream_path": "/private/tmp/xlerobot-audit-20260729",
+  "status": "blocked",
+  "checks": [
+    {
+      "id": "pinned_commit",
+      "passed": true,
+      "required_for_hardware_run": true,
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+}

chapter6/xlerobot-teleoperation/validation/runs/local-gpu/cells.json

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chapter6/xlerobot-teleoperation/validation/runs/local-gpu/evidence.json

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chapter6/xlerobot-teleoperation/validation/runs/local-gpu/metrics.json

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