HUG trains a flow-matching model on a new 1M-frame egocentric human grasp dataset to generate retargetable grasps from single RGB-D images, beating baselines by 23-34% on a new 90-object benchmark.
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Egozero: Robot learning from smart glasses
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EgoEngine transforms egocentric human videos into high-fidelity robot data enabling zero-shot visuomotor dexterous policy learning without real-robot demonstrations.
DreamDojo is a foundation world model pretrained on the largest human video dataset to date that uses continuous latent actions to transfer interaction knowledge and achieves controllable physics simulation after robot post-training.
A 1,362-hour multi-source egocentric human dataset and multi-lab study show co-training improves robot manipulation, but only when aligned human–robot data anchors transfer from diverse human data.
ReStruct steers robot policies at inference time by reconfiguring task structure with neural automata and synchronous products, claiming up to 25% gains over VLA models in success and preference adherence.
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.
ACE-Ego-0 is a VLA pretraining framework that turns egocentric human videos into robot-format pseudo-actions via a video-to-action pipeline and trains jointly with robot data under a reliability-aware objective.
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.
WARPED synthesizes realistic wrist-view observations from monocular egocentric human videos via foundation models, hand-object tracking, retargeting, and Gaussian Splatting to train visuomotor policies that match teleoperation success rates on five tabletop tasks with 5-8x less collection effort.
ActiveGlasses learns robot manipulation from ego-centric human demos captured with active vision via smart glasses, achieving zero-shot transfer using object-centric point-cloud policies.
X-Diffusion adapts Ambient Diffusion to selectively train on noised human actions for cross-embodiment robot policies, yielding 16% higher average success rates than naive co-training or manual filtering across five real-world manipulation tasks.
HumanEgo reports 92.5% average success on four real robot tasks using only 15-30 minutes of human video per task and zero robot data, with zero-shot transfer to new robots and cameras.
Human2Any transfers human video demonstrations to robots by representing tasks as object-object interactions and composing learned priors with robot-side planning.
citing papers explorer
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Human Universal Grasping
HUG trains a flow-matching model on a new 1M-frame egocentric human grasp dataset to generate retargetable grasps from single RGB-D images, beating baselines by 23-34% on a new 90-object benchmark.
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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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DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos
DreamDojo is a foundation world model pretrained on the largest human video dataset to date that uses continuous latent actions to transfer interaction knowledge and achieves controllable physics simulation after robot post-training.
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EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World
A 1,362-hour multi-source egocentric human dataset and multi-lab study show co-training improves robot manipulation, but only when aligned human–robot data anchors transfer from diverse human data.
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Inference-Time Robot Behavior Steering through Physically-Aware Reconfiguration of Task-Structure
ReStruct steers robot policies at inference time by reconfiguring task structure with neural automata and synchronous products, claiming up to 25% gains over VLA models in success and preference adherence.
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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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ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining
ACE-Ego-0 is a VLA pretraining framework that turns egocentric human videos into robot-format pseudo-actions via a video-to-action pipeline and trains jointly with robot data under a reliability-aware objective.
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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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WARPED: Wrist-Aligned Rendering for Robot Policy Learning from Egocentric Human Demonstrations
WARPED synthesizes realistic wrist-view observations from monocular egocentric human videos via foundation models, hand-object tracking, retargeting, and Gaussian Splatting to train visuomotor policies that match teleoperation success rates on five tabletop tasks with 5-8x less collection effort.
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ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration
ActiveGlasses learns robot manipulation from ego-centric human demos captured with active vision via smart glasses, achieving zero-shot transfer using object-centric point-cloud policies.
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X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations
X-Diffusion adapts Ambient Diffusion to selectively train on noised human actions for cross-embodiment robot policies, yielding 16% higher average success rates than naive co-training or manual filtering across five real-world manipulation tasks.
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HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos
HumanEgo reports 92.5% average success on four real robot tasks using only 15-30 minutes of human video per task and zero robot data, with zero-shot transfer to new robots and cameras.
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Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning
Human2Any transfers human video demonstrations to robots by representing tasks as object-object interactions and composing learned priors with robot-side planning.
- WARP: Whole-Body Retargeting for Learning from Offline Human Demonstrations