用物理仿真评估不确定环境下的机器人操作安全，高效过滤危险动作。
Safety filtering of robotic manipulation under environment uncertainty: a computational approach
- 结合高保真仿真与关键状态重评，动态评估控制策略安全性。
- 在物体质量与摩擦未知的双臂操作任务中，成功识别并过滤不安全轨迹。
- 适合需要在复杂不确定环境中保证操作安全的研究者和工程师。
在动态非结构化环境中进行机器人操作需要利用对世界的已知信息与不确定性来构建安全机制。现有安全滤波器常假设完全可观测性，限制了其在真实任务中的应用。本文提出一种基于物理的安全滤波方法，通过高保真仿真评估在世界参数不确定情况下的控制策略。该方法结合密集的名义参数滚动预测与关键状态转移处可并行的稀疏重评估，以广义安全系数量化抓取稳定性与执行器极限，并通过探测动作实现针对性不确定性降低。我们在一个具有不确定物体质量与摩擦的双臂操作模拟任务中验证了该方法，结果表明能高效识别并过滤不安全轨迹。研究证明，基于物理的稀疏安全评估是一种在不确定性下可扩展的机器人安全操作策略。
原文摘要 · Abstract (English)
Robotic manipulation in dynamic and unstructured environments requires safety mechanisms that exploit what is known and what is uncertain about the world. Existing safety filters often assume full observability, limiting their applicability in real-world tasks. We propose a physics-based safety filtering scheme that leverages high-fidelity simulation to assess control policies under uncertainty in world parameters. The method combines dense rollout with nominal parameters and parallelizable sparse re-evaluation at critical state-transitions, quantified through generalized factors of safety for stable grasping and actuator limits, and targeted uncertainty reduction through probing actions. We demonstrate the approach in a simulated bimanual manipulation task with uncertain object mass and friction, showing that unsafe trajectories can be identified and filtered efficiently. Our results highlight physics-based sparse safety evaluation as a scalable strategy for safe robotic manipulation under uncertainty.
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