Don't Fool Me Twice: Adapting to Adversity in the Wild with Experience-Driven Reasoning
In robotics, dangers and adversity modes are often embodiment-specific and relative to each agent. A frontier of autonomous mobile robotics is to enable agents to operate effectively in the wild in unseen unstructured environments. A significant challenge in unseen unstructured environments is that it may not be possible to predict all the dangers to the specific robot. Although recent work has used large foundation vision-language models (VLMs) to preemptively predict an exhaustive list of common-sense dangers, it remains difficult to capture possible interaction and embodiment-dependent adversities. We propose a continual learning framework for a mobile embodied agent to learn online fr...
| Authors | Navin Sriram Ravie, Andrew Jong, Krrish Jain et al. |
|---|---|
| Primary category | cs.RO |
| Published | 29 May 2026 |
| Last updated at source | 22 July 2026 |
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