APPL is an agentic robot learning framework connecting policy learning with agent-based skill selection and composition through structural priors for generalization from a few demonstrations to new scenes and task compositions.
robotics imitation-learning robot-learning few-shot-learning embodied-ai metaworld diffusion-policy llm-agents agentic-ai maniskill skill-composition structural-priors
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Updated
Oct 4, 2026 - Python