Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning
While foundation models offer promise toward improving robot safety in out-of-distribution (OOD) scenarios, how to effectively harness their generalist knowledge for real-time, dynamically feasible response remains a crucial problem. We present FORTRESS, a joint reasoning and planning framework that generates semantically safe fallback strategies to prevent safety-critical, OOD failures.
Evidence notes
- Experiments use ANYmal as the physical evaluation platform.
- The reported results provide an independent evaluation on ANYmal hardware.
Company context
ANYbotics develops autonomous legged robots for industrial inspection in complex and hazardous environments. Its ANYmal platform targets repeatable monitoring, inspection and safety workflows.