X-Safe: Embodiment-Agnostic Safeguarding for Robot Arm Manipulation Policies via Action Masking
Ensuring safety of learning-enabled robotic manipulation across diverse embodiments and tasks still requires significant manual engineering. For collision avoidance, existing approaches typically rely on embodiment-specific learned safety approximations or simplified robot models together with stopping-based fallback controllers that compromise performance. In this paper, we propose an embodiment-agnostic safeguarding approach, X-Safe, which provides formal probabilistic guarantees for collision avoidance without model simplifications or learned safety functions. By operating in the configuration space, our safeguarding method works with different types of robot policies. X-Safe reasons l...
| Authors | Alex Beaudin, Hanna Krasowski, Kartik Nagpal et al. |
|---|---|
| Primary category | cs.RO |
| Published | 21 June 2026 |
| Last updated at source | 14 October 2026 |
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