A simulation-trained RL policy, rewarded purely by how well placed pieces cover a silhouette, generalizes to assemble novel tangram shapes and simple cutlery arrangements.
Learning insertion primitives with discrete-continuous hybrid action space for robotic assembly tasks,
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Master Rules from Chaos: Learning to Reason, Plan, and Interact from Chaos for Tangram Assembly
A simulation-trained RL policy, rewarded purely by how well placed pieces cover a silhouette, generalizes to assemble novel tangram shapes and simple cutlery arrangements.