SOURCE-LINKED INTELLIGENCE
Performance-Preserving Online Adaptation in Social Navigation via Diffusion Steering
In social navigation, modeling the complex interactions between humans and robots is difficult, and deep reinforcement learning has therefore been actively studied. However, because simulation alone cannot fully reproduce diverse scenarios, robot dynamics, and the social conventions that vary across deployment environments, fine-tuning in the deployment environment is promising. In doing so, learning that preserves the base model's performance is required, so as not to compromise the primary objective of navigation, namely avoiding pedestrians and reaching the destination. In this study, we pr
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T09:16:45.000Z
First collected: 2026-09-23T08:01:43.213Z. This is not the publication date.