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Georgia Institute of Technology
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SIDO enables static-trained policies to excel in dynamic environments, achieving higher success rates in moving object manipulation without sacrificing static performance.
WARP achieves zero-shot whole-body mobile manipulation from offline human demonstrations, eliminating the reliance on teleoperation data.
Interleaving motion planning with VLAs can yield over double the task progress in complex mobile manipulation scenarios without requiring more training data.
Ditching short-term motion prediction for explicit intention prediction from motion history unlocks more accurate and socially acceptable autonomous valet parking.
