PRISM: Primitive Routing via In-context Skill Mixing for Retentive VLA Adaptation
Overview
This paper introduces PRISM, a framework that stores reusable skills as frozen low-rank action-expert adapters in a primitive bank. A per-chunk vision-language model conditioned router composes these primitives during inference by tracking task progress via prefix representations and is supervised by an action-derived oracle. Evaluated on RoboCasa365, PRISM achieves 56.43% atomic-skill success and 20.00% long-horizon composite task success, outperforming a standard two-stage LoRA fine-tune (17.86% atomic, 17.50% composite). The method is also validated on an AgileX PIPER real-robot setup.
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- arXiv Robotics — research abstractsSignalPRISM: Primitive Routing via In-context Skill Mixing for Retentive VLA Adaptation
This paper introduces PRISM, a framework that stores reusable skills as frozen low-rank action-expert adapters in a primitive bank. A per-chunk vision-language model conditioned router composes these primitives during inference by tracking task progress via prefix representations and is supervised by an action-derived oracle. Evaluated on RoboCasa365, PRISM achieves 56.43% atomic-skill success and 20.00% long-horizon composite task success, outperforming a standard two-stage LoRA fine-tune (17.86% atomic, 17.50% composite). The method is also validated on an AgileX PIPER real-robot setup.
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