CASD: Chunk-Aligned Semantic Distillation for Multi-StageRobot Manipulation
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Abstract
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 VLM calls or reasoning-trace decoding. Teacher matching on annotated LIBERO training episodes is above chance for both single-stage and boundary-crossing chunks. We evaluate three Fast-WAM variants and a DreamZero integration across four benchmarks, including distribution shifts on LIBERO-Plus. Compared with published references, IDM+CASD reaches 98.9\% versus 98.0\% average success on LIBERO, while Uncond falls below its reference. Joint+CASD reaches 93.0\% versus 90.6\% on RoboTwin 2.0, and DreamZero+CASD reaches a 47.9\% four-category MolmoSpaces manipulation average versus 40.7\%. Performance varies across backbone integrations.
