环境不确定性下机器人操作的安全过滤：一种计算方法
Safety filtering of robotic manipulation under environment uncertainty: a computational approach
AI总结：
针对动态非结构化环境中机器人操作的安全问题，提出基于物理的安全过滤方案，利用高保真仿真结合密集展开与稀疏重评估，有效识别并过滤不安全轨迹。
摘要：
动态和非结构化环境中的机器人操作需要利用对世界的已知和未知信息的安全机制。现有的安全过滤器通常假设完全可观测，限制了其在真实世界任务中的适用性。我们提出了一种基于物理的安全过滤方案，利用高保真仿真来评估世界参数不确定性下的控制策略。该方法将使用标称参数的密集展开与在关键状态转换处的可并行稀疏重评估相结合，通过稳定抓取和执行器限制的广义安全系数进行量化，并通过探测动作实现针对性的不确定性降低。我们在具有不确定物体质量和摩擦的模拟双手操作任务中演示了该方法，表明不安全轨迹可以被有效识别和过滤。我们的结果强调基于物理的稀疏安全评估是应对不确定性下安全机器人操作的可扩展策略。
英文摘要：
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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