Video2Sim2Real turns a single human video into a deployable robot manipulation skill by reconstructing a digital twin, anchoring motions to object-centric simulator configurations, and bridging sim-to-real gaps with imitation learning and residual RL.
Learning dexterous manipulation from exemplar object trajectories and pre-grasps
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FastGrasp uses two-stage RL with CVAE for diverse grasp candidates from point clouds and tactile sensing for impact adjustments to achieve robust fast whole-body grasping in sim and real-world settings.
citing papers explorer
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Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video
Video2Sim2Real turns a single human video into a deployable robot manipulation skill by reconstructing a digital twin, anchoring motions to object-centric simulator configurations, and bridging sim-to-real gaps with imitation learning and residual RL.
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FastGrasp: Learning-based Whole-body Control method for Fast Dexterous Grasping with Mobile Manipulators
FastGrasp uses two-stage RL with CVAE for diverse grasp candidates from point clouds and tactile sensing for impact adjustments to achieve robust fast whole-body grasping in sim and real-world settings.