AI 中文总结
该研究提出TWS框架，解决传统机器人世界状态表示缺乏溯源性的问题，通过不可变快照、哈希链等实现确定性回放，经多组测试验证其有效性与低存储开销。
AI 中文摘要
执行长期任务的机器人系统必须维护由不同时间到达、置信度各异且可能存在修订的观测结果组装而成的世界状态。传统表示方法侧重最新估计值，阻碍了事实溯源、决策复现或执行审计。我们提出可追溯世界状态（Traceable World State，TWS），这是一种与中间件无关的语义表示及参考运行时，用于实现溯源感知的机器人世界状态。TWS快照捕获实体、关系、观测结果、置信度和修订元数据。经过验证的更新操作以不可变方式转换快照，有序更新支持确定性回放，规范的SHA-256哈希链确保日志具备防篡改特性。我们通过模式一致性、完整状态生命周期、确定性回放和故障注入对TWS进行评估：在Python 3.10-3.14中通过38项测试，该框架可检测记录损坏、哈希链接断裂、序列中断和世界状态不匹配；在10个公开BEHAVIOR-1K任务定义中，TWS导入153个实体和146个关系并通过验证；在包含78369帧的103条NVIDIA Unitree G1模拟轨迹上，TWS在所有回合中实现精确的终端状态回放，检测到全部412处注入的损坏，存储开销较Plain JSONL仅为1.72%。
英文摘要
Robotic systems operating over extended tasks must maintain a world state assembled from observations arriving at different times, with varying confidence and potential revisions. Conventional representations emphasize latest estimates, hindering fact provenance, decision reproduction, or execution auditing. We present Traceable World State (TWS), a middleware-neutral semantic representation and reference runtime for provenance-aware robot world state. A TWS snapshot captures entities, relations, observations, confidence, and revision metadata. Validated update operations transform snapshots immutably, ordered updates support deterministic replay, and a canonical SHA-256 hash chain ensures tamper-evident logs. We evaluate TWS through schema conformance, complete state lifecycles, deterministic replay, and fault injection. Passing 38 tests across Python 3.10-3.14, the framework detects record corruptions, broken hash links, sequence discontinuities, and world mismatches. Across ten public BEHAVIOR-1K task definitions, TWS imported 153 entities and 146 relations with successful validation. On 103 NVIDIA Unitree G1 simulated trajectories containing 78,369 frames, TWS achieved exact terminal-state replay in all episodes and detected 412/412 injected corruptions with a 1.72% storage overhead over Plain JSONL.
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