DRP couples a transformer policy pretrained on 10M cuRobo trajectories and refined by student-teacher finetuning with a point-cloud reactive goal proposer (DCP-RMP) to outperform prior planners in dynamic manipulation.
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Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments
DRP couples a transformer policy pretrained on 10M cuRobo trajectories and refined by student-teacher finetuning with a point-cloud reactive goal proposer (DCP-RMP) to outperform prior planners in dynamic manipulation.