Think When It Matters: Conditional VLM Reasoning for Social Navigation with RL Policies
As mobile robots become more integrated into everyday human environments, social robot navigation is becoming essential for ensuring human comfort, safety, and trust. While reinforcement learning (RL) navigation policies provide the fast inference and reactive behavior necessary for real-time deployment, they still lack flexible semantic reasoning capabilities and often fail to generalize to complex social scenarios. Recent approaches have increasingly turned to vision-language models (VLMs) in place of RL policies to improve semantic and social reasoning in robot navigation. Nevertheless, their high computational cost and slow inference remain major barriers to real-time deployment. To o...
| Authors | Ali Ahmadi, Hamed Rahimi, Adrien Jacquet Cretides et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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