CASD: Chunk-Aligned Semantic Distillation for Multi-StageRobot Manipulation
An action chunk can span several stages of a manipulation task, yet a label for its first step describes only the current stage. We introduce Chunk-Aligned Semantic Distillation (CASD), which derives semantic targets for entire action chunks. An offline vision--language model segments demonstrations into described stages. Their occupancy within each action chunk determines a weighted semantic target, including transitions between stages. A CASD generator learns to predict this target from the current observation, robot state, and task instruction. We then freeze the generator and train a policy conditioned on its predictions. The semantic branch runs once per policy query, without online ...
| Authors | Tinghe Ding, Jiahao Li, He Wang |
|---|---|
| Primary category | cs.RO |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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