ActiveMimic pretrains on egocentric human video by recovering and modeling active camera motion as viewpoint actions, matching robot-data pretraining performance on real-world tasks.
Activeumi: Robotic manipulation with active perception from robot-free human demonstrations
13 Pith papers cite this work. Polarity classification is still indexing.
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A wearable interface with a shared dexterous hand module enables retargeting-free teleoperation and matched data collection, yielding policies with 88.75% average success across eight real-robot tasks that generalize and transfer across embodiments.
BifrostUMI enables robot-free human demonstration capture via VR and wrist cameras to train visuomotor policies that predict keypoint trajectories for transfer to humanoid whole-body control through retargeting.
UMI-3D integrates LiDAR into the UMI hardware for robust multimodal 3D perception in manipulation demonstrations, yielding higher policy success rates and enabling previously infeasible tasks like deformable object handling.
XRZero-G0 enables 2000-hour robot-free datasets that, when mixed 10:1 with real-robot data, match full real-robot performance at 1/20th the cost and support zero-shot transfer.
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.
HoMMI learns whole-body mobile manipulation policies from robot-free human demonstrations by augmenting UMI with egocentric sensing and bridging the embodiment gap through an agnostic visual representation, relaxed head actions, and a whole-body controller.
IGen generates realistic visuomotor training data including actions and temporally coherent visuals from unstructured open-world images via 3D reconstruction and VLM reasoning.
A hybrid data collection strategy with a mobile camera arm in dual-arm robots reduces shortcut learning in VLA models and improves spatial generalization to unseen poses and configurations across ACT, Diffusion, and VLA architectures.
Robot-free VR–UMI demos of sparse whole-body keypoints can be retargeted and executed as deployable Unitree G1 whole-body skills more efficiently than teleoperation.
HALOMI extends UMI with egocentric sensing and a manifold-constrained controller plus alignment adaptations to learn loco-manipulation on humanoids from human demos, reporting 85% average success on three real-world tasks.
The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.
A comprehensive survey that organizes the literature on world models in robot learning, their roles in policy learning, planning, simulation, and video-based generation, with connections to navigation, driving, datasets, and benchmarks.
citing papers explorer
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ActiveMimic: Egocentric Video Pretraining with Active Perception
ActiveMimic pretrains on egocentric human video by recovering and modeling active camera motion as viewpoint actions, matching robot-data pretraining performance on real-world tasks.
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RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning
A wearable interface with a shared dexterous hand module enables retargeting-free teleoperation and matched data collection, yielding policies with 88.75% average success across eight real-robot tasks that generalize and transfer across embodiments.
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BifrostUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation
BifrostUMI enables robot-free human demonstration capture via VR and wrist cameras to train visuomotor policies that predict keypoint trajectories for transfer to humanoid whole-body control through retargeting.
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UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception
UMI-3D integrates LiDAR into the UMI hardware for robust multimodal 3D perception in manipulation demonstrations, yielding higher policy success rates and enabling previously infeasible tasks like deformable object handling.
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XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios
XRZero-G0 enables 2000-hour robot-free datasets that, when mixed 10:1 with real-robot data, match full real-robot performance at 1/20th the cost and support zero-shot transfer.
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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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HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations
HoMMI learns whole-body mobile manipulation policies from robot-free human demonstrations by augmenting UMI with egocentric sensing and bridging the embodiment gap through an agnostic visual representation, relaxed head actions, and a whole-body controller.
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IGen: Scalable Data Generation for Robot Learning from Open-World Images
IGen generates realistic visuomotor training data including actions and temporally coherent visuals from unstructured open-world images via 3D reconstruction and VLM reasoning.
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The Moving Eye: Enhancing VLA Spatial Generalization via Hybrid Dynamic Data Collection
A hybrid data collection strategy with a mobile camera arm in dual-arm robots reduces shortcut learning in VLA models and improves spatial generalization to unseen poses and configurations across ACT, Diffusion, and VLA architectures.
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HumanoidUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation
Robot-free VR–UMI demos of sparse whole-body keypoints can be retargeted and executed as deployable Unitree G1 whole-body skills more efficiently than teleoperation.
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HALOMI: Learning Humanoid Loco-Manipulation with Active Perception from Human Demonstrations
HALOMI extends UMI with egocentric sensing and a manifold-constrained controller plus alignment adaptations to learn loco-manipulation on humanoids from human demos, reporting 85% average success on three real-world tasks.
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World Action Models: The Next Frontier in Embodied AI
The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.
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World Model for Robot Learning: A Comprehensive Survey
A comprehensive survey that organizes the literature on world models in robot learning, their roles in policy learning, planning, simulation, and video-based generation, with connections to navigation, driving, datasets, and benchmarks.