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 diverse bimanual dexterous manipulation skills from human demonstrations
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 4years
2026 4verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
CoDex combines VLMs, constrained optimization, and RL to autonomously discover grasp-move-actuate policies for functional manipulation of unseen objects with internal mechanisms.
SynManDex generates human-like dexterous grasps for robots from synthetic human pre-grasps via retargeting and force-closure optimization, reporting 86.4% stability, 4.67/5 human-likeness, 80.7% sim success, and 83.3% real-robot success.
A structured survey of dexterous robotic hand research that reviews hardware, control methods, data resources, and benchmarks while identifying major limitations and future directions.
citing papers explorer
-
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.
-
CoDex: Learning Compositional Dexterous Functional Manipulation without Demonstrations
CoDex combines VLMs, constrained optimization, and RL to autonomously discover grasp-move-actuate policies for functional manipulation of unseen objects with internal mechanisms.
-
SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps
SynManDex generates human-like dexterous grasps for robots from synthetic human pre-grasps via retargeting and force-closure optimization, reporting 86.4% stability, 4.67/5 human-likeness, 80.7% sim success, and 83.3% real-robot success.
-
Towards Robotic Dexterous Hand Intelligence: A Survey
A structured survey of dexterous robotic hand research that reviews hardware, control methods, data resources, and benchmarks while identifying major limitations and future directions.