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title
Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps
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We show how simple ingredients can dramatically speed up learning dexterous behaviors.
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2026-02-28 21:04:35
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Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps Sudeep Dasari 1 Abhinav Gupta 1 Vikash Kumar 2 1 Carnegie Mellon University 2 Meta AI Research [arXiv] [Code] [Video] [Appendix] Overview How can we learn diverse, dexterous manipulation behaviors without expensive, per-task tuning? Our work proposes two key ideas: a pre-grasp enhanced learning pipeline, and a large-scale dexterous task benchmark. Introducing PGDM Our P re- G rasp informed D exterous M anipulation ( PGDM ) framework generates diverse behaviors, without any hyper-parameter tuning. At the core of PGDM is a well known robotics construct, pre-grasps (i.e. the hand-pose preparing for object interaction). Simply moving the robot hand to a pre-grasp position before starting optimization can induce efficient exploration strategies fo...
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