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RoboCode: Learning Reusable Action Primitives for Robot Manipulation

RoboCode learns short-horizon motion patterns that can be reused across tasks through a shared soft-gated primitive action interface, representing action chunks as soft compositions of multiple learned primitives rather than a single discrete code. The interface is learned on action chunks pooled across multiple tasks and then frozen, after which task-specific state encoders are trained to map into the shared latent space. On five ManiSkill tasks, it achieves an average closed-loop success rate of 75.6%, above ACT's 68.8% and Diffusion Policy's 41.6%, with an average action chunk reconstruction MSE of 0.0187.

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