PIER：面向机器人操纵的证据门控执行接口
PIER: An Evidence-Gated Execution Interface for Robotic Manipulation
浏览论文内容
中文总结 AI 辅助
针对机器人操纵中动作执行的观测依据问题，提出PIER证据门控执行接口，通过分离证据检查等模块并结合重新观测，在合成噪声等场景下优化了决策效果，同时明确了其局限性。
中文摘要 AI 辅助
生成合理的机器人动作并不意味着当前观测结果支持该动作的执行。受严重遮挡下高置信度视觉输出的探索性观测启发，我们提出PIER，一种执行授权接口，它将证据检查、决策溯源和阶段范围内的重新观测与硬件控制分离开来。确定性门控模块评估声明的视觉和触觉输入，而其调用者维护每个阶段最多一次重新观测的预算。我们使用1600个阈值网格案例和1200个配对合成轨迹评估该实现，这些轨迹涵盖分数噪声、触觉输入缺失、过时观测以及虚假令人安心的分数。有限网格产生零声明不变量违反，匹配的布尔基线复现所有非恢复决策。在合成分数噪声下，重新观测将有效状态拒绝从57/120降至18/120，同时将无效状态执行从5/120增至7/120。过时和虚假令人安心的输入暴露了仅阈值检查无法解决的局限性。探索性视觉、触觉和机器人设置记录提供上下文，但无法确立物理任务性能。这些结果表征了可检查的授权接口及其输入契约局限性，未声称优于等效规则逻辑、校准的触觉精度或经认证的物理安全性。
英文摘要
Generating a plausible robot action does not establish that current observations justify its execution. Motivated by exploratory observations of high-confidence visual outputs under severe occlusion, we present PIER, an execution-authorization interface that separates evidence checks, decision provenance, and stage-scoped re-observation from hardware control. The deterministic gate evaluates declared visual and tactile inputs, while its caller maintains a budget of at most one re-observation per stage. We evaluate the implementation using 1,600 threshold-grid cases and 1,200 paired synthetic traces spanning score noise, missing tactile inputs, stale observations, and falsely reassuring scores. The finite grid yields zero declared invariant violations, and a matched Boolean baseline reproduces all non-recovery decisions. Under synthetic score noise, re-observation reduces valid-state denials from 57/120 to 18/120 while increasing invalid-state proceeds from 5/120 to 7/120. Stale and falsely reassuring inputs expose limitations that threshold checks alone cannot resolve. Exploratory visual, tactile, and robot setup records provide context but do not establish physical task performance. These results characterize an inspectable authorization interface and its input-contract limitations, without claiming superiority over equivalent rule logic, calibrated tactile accuracy, or certified physical safety.
发表机构
- University of Washington（华盛顿大学）
- ZJU-UIUC Institute（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究院）
机构由 AI 辅助整理，请以论文原文为准。
