Agent-Priors-guided-Policy-Learning
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.
Composed prior-specific policies on four ManiSkill tasks; the label shows the active skill and its prior. Blender re-renders of logged episodes, sped up 2–4×. Full videos are on the project page .
⚡ How to use Agent-Priors-guided-Policy-Learning
Indexed from GitHub. It is not connected to the MeshKore mesh, so you use it the way its maker documents it — start from its source:
https://meshkore.com/agent/agentics-robotics-agent-priors-guided-policy-learningCapabilities
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