EgoEngine transforms egocentric human videos into high-fidelity robot data enabling zero-shot visuomotor dexterous policy learning without real-robot demonstrations.
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Spider: Scalable physics-informed dexterous retargeting
15 Pith papers cite this work. Polarity classification is still indexing.
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2026 15representative citing papers
MotionDisco discovers long-horizon humanoid loco-manipulation motions from scratch via LLM-guided evolutionary search, trajectory optimization, and pruning, then transfers them to real robots with RL policies.
POMDAR is a taxonomy-grounded benchmark that quantifies dexterity as task throughput across vertical, horizontal, rotation, and grasping configurations with mechanical constraints for unambiguous measurement.
Wrist-guided whole-body RL retargets human–object interactions without finger pose supervision, matching supervised methods and generalizing across hand morphologies in simulation.
DO AS I DO reconstructs and retargets hand-object interactions from in-the-wild monocular RGB videos to produce dexterous robot manipulation trajectories, outperforming prior methods on ground-truth and online video datasets.
HOWTransfer recovers 3D hand motion from video, localizes contact intervals via hand-object cues, generates multi-modal grasp hypotheses, and edits trajectories to produce diverse robot-executable motions achieving 86% success.
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.
SCSP is a cascaded optimization framework using a surrogate contact model and discrete-continuous search to enable simultaneous contact selection and planning for robust contact-rich manipulation.
SynAgent enables generalizable cooperative humanoid manipulation by transferring skills from solo human-object interactions to multi-agent scenarios via interaction-preserving retargeting, single-agent pretraining with multi-agent PPO, and a conditional VAE generative policy.
Sampling-Based Retargeter (SBR) delivers lower-jitter real-time kinematic hand retargeting and higher task success with less operator fatigue than gradient-based baselines in an 18-person study.
CHORD uses object-centric contact wrench guidance to improve RL scalability for long-horizon dexterous manipulation, reporting 82.12% average success on 1,831 of 4,739 benchmark tasks with real-world transfer.
KITE decouples task reasoning from embodiment-specific control via learned latent interaction intents to enable zero-shot transfer across structurally different robots.
TopoRetarget uses a sparse interaction graph and distance-weighted Laplacian deformation optimization with kinematic and penetration constraints to retarget human demonstrations to dexterous hands while preserving task-relevant contacts.
LUCID learns embodiment-agnostic intent models from unstructured human videos to train dexterous robot policies in simulation, enabling zero-shot transfer on real-world tasks like stirring and wiping.
ConTrack introduces a constrained RL method with online dual-variable adaptation and adaptive resets for improved long-horizon hand tracking in simulation and on real robots.
citing papers explorer
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EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations
EgoEngine transforms egocentric human videos into high-fidelity robot data enabling zero-shot visuomotor dexterous policy learning without real-robot demonstrations.
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MotionDisco: Motion Discovery for Extreme Humanoid Loco-Manipulation
MotionDisco discovers long-horizon humanoid loco-manipulation motions from scratch via LLM-guided evolutionary search, trajectory optimization, and pruning, then transfers them to real robots with RL policies.
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A Benchmark of Dexterity for Anthropomorphic Robotic Hands
POMDAR is a taxonomy-grounded benchmark that quantifies dexterity as task throughput across vertical, horizontal, rotation, and grasping configurations with mechanical constraints for unambiguous measurement.
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WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation
Wrist-guided whole-body RL retargets human–object interactions without finger pose supervision, matching supervised methods and generalizing across hand morphologies in simulation.
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Do as I Do: Dexterous Manipulation Data from Everyday Human Videos
DO AS I DO reconstructs and retargets hand-object interactions from in-the-wild monocular RGB videos to produce dexterous robot manipulation trajectories, outperforming prior methods on ground-truth and online video datasets.
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Hand-centric Human-to-Robot Trajectory Transfer from Video Demonstrations via Open-World Contact Localization
HOWTransfer recovers 3D hand motion from video, localizes contact intervals via hand-object cues, generates multi-modal grasp hypotheses, and edits trajectories to produce diverse robot-executable motions achieving 86% success.
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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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Simultaneous Contact Selection and Planning for Contact-Rich Manipulation with Cascaded Optimization
SCSP is a cascaded optimization framework using a surrogate contact model and discrete-continuous search to enable simultaneous contact selection and planning for robust contact-rich manipulation.
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SynAgent: Generalizable Cooperative Humanoid Manipulation via Solo-to-Cooperative Agent Synergy
SynAgent enables generalizable cooperative humanoid manipulation by transferring skills from solo human-object interactions to multi-agent scenarios via interaction-preserving retargeting, single-agent pretraining with multi-agent PPO, and a conditional VAE generative policy.
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Smooth Operator: A Real-Time Sampling-Based Algorithm for Kinematic Hand Retargeting
Sampling-Based Retargeter (SBR) delivers lower-jitter real-time kinematic hand retargeting and higher task success with less operator fatigue than gradient-based baselines in an 18-person study.
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Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
CHORD uses object-centric contact wrench guidance to improve RL scalability for long-horizon dexterous manipulation, reporting 82.12% average success on 1,831 of 4,739 benchmark tasks with real-world transfer.
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KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulation
KITE decouples task reasoning from embodiment-specific control via learned latent interaction intents to enable zero-shot transfer across structurally different robots.
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TopoRetarget: Interaction-Preserving Retargeting for Dexterous Manipulation
TopoRetarget uses a sparse interaction graph and distance-weighted Laplacian deformation optimization with kinematic and penetration constraints to retarget human demonstrations to dexterous hands while preserving task-relevant contacts.
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LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition
LUCID learns embodiment-agnostic intent models from unstructured human videos to train dexterous robot policies in simulation, enabling zero-shot transfer on real-world tasks like stirring and wiping.
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ConTrack: Constrained Hand Motion Tracking with Adaptive Trade-off Control
ConTrack introduces a constrained RL method with online dual-variable adaptation and adaptive resets for improved long-horizon hand tracking in simulation and on real robots.