Abstract
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.
Abstract (translated)
在动态和非结构化的环境中进行机器人操作需要利用对世界已知信息和不确定性的安全机制。现有的安全过滤器通常假设可以完全观察环境，这限制了它们在实际任务中的应用范围。我们提出了一种基于物理的安全过滤方案，该方案利用高保真模拟来评估不确定性参数下的控制策略。这种方法结合了密集的仿真运行与名义参数以及通过广义安全因子量化的关键状态转换并行化的稀疏重新评估，并通过探测动作实现目标不确定性的减少。我们在一个模拟的双臂操作任务中展示了这种技术，该任务中的物体质量和摩擦力是未知的。结果显示可以有效地识别和过滤不安全的操作路径。我们的研究结果强调了基于物理的稀疏安全性评估是一种在不确定性条件下进行机器人安全操作的有效策略。
URL
https://arxiv.org/abs/2509.12674
