Training Generalist Navigation Policies via Action Self-Distillation
Language provides a natural interface for humans to specify and reason about goals, but training language-conditioned robotic policies and reasoning typically requires expensive manual annotations. We propose Action Self-Distillation (ASD), a method for learning language-conditioned reasoning for robotic navigation without human-annotated language or reasoning labels. ASD uses an open-source VLM, which contains broad world knowledge and reasoning capabilities, as both a teacher network and a student policy. During training, the self-teacher receives privileged trajectory feedback and infers the underlying navigation goal together with goal-oriented reasoning. The student is then trained t...
| Authors | Bowen Feng, Keqin Wang, Yulong Yang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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