WARPED: Wrist-Aligned Rendering for Robot Policy Learning from Egocentric Human Demonstrations

• URL: http://arxiv.org/abs/2604.10809v1
• Date: Sun, 12 Apr 2026 20:40:59 GMT
• Title: WARPED: Wrist-Aligned Rendering for Robot Policy Learning from Egocentric Human Demonstrations
• Abstract summary: WARPED is a framework designed to synthesize realistic wrist-view observations from human demonstration videos.<n>A hand-object interaction pipeline is then employed to track the hand and manipulated object and retarget the trajectories to a robotic end-effector.<n>We demonstrate that WARPED achieves success rates comparable to policies trained on teleoperated demonstration data for five tabletop manipulation tasks.
• Score: 10.024841990710177
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in learning from human demonstration have shown promising results in addressing the scalability and high cost of data collection required to train robust visuomotor policies. However, existing approaches are often constrained by a reliance on multiview camera setups, depth sensors, or custom hardware and are typically limited to policy execution from third-person or egocentric cameras. In this paper, we present WARPED, a framework designed to synthesize realistic wrist-view observations from human demonstration videos to facilitate the training of visuomotor policies using only monocular RGB data. With data collected from an egocentric RGB camera, our system leverages vision foundation models to initialize the interactive scene. A hand-object interaction pipeline is then employed to track the hand and manipulated object and retarget the trajectories to a robotic end-effector. Lastly, photo-realistic wrist-view observations are synthesized via Gaussian Splatting to directly train a robotic policy. We demonstrate that WARPED achieves success rates comparable to policies trained on teleoperated demonstration data for five tabletop manipulation tasks, while requiring 5-8x less data collection time.

Related papers

• RGB-D Video Generation for Improving Human-to-Robot Object Handover Prediction [51.76737991200891]
  Human-to-robot (H2R) object handover is a fundamental capability for human-robot collaboration, yet progress is hindered by the scarcity of large-scale, human-centric datasets.<n>We introduce Hand2Bot, an RGB-D video dataset that provides rich contextual information such as body posture and facial expressions.<n>We propose PassGen, a generative pipeline that synthesizes realistic handover sequences while ensuring hand-object consistency.
  arXiv  Detail & Related papers  (2026-08-13T09:55:59Z)
• Contrastive Action-Image Pre-training for Visuomotor Control [91.5630572541332]
  We introduce CAIP, a vision encoder that treats human hand poses from large-scale egocentric video as a proxy for end-effector actions.<n>Leveraging 32,041 hours of egocentric human video and only 88 hours of robotic manipulation data, CAIP outperforms state-of-the-art vision encoders.
  arXiv  Detail & Related papers  (2026-06-15T20:00:20Z)
• Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations [47.549615826832856]
  Dexterous Point Policy learns dexterous manipulation policies directly from human videos and requires no robot demonstrations.<n>We extract 3D keypoints of task-relevant objects and human hands from raw videos, and train an autoregressive transformer over these keypoints.<n>On a suite of real-robot tasks spanning pick-and-place and tool use, Dexterous Point Policy attains 75.0% success, whereas a state-of-the-art VLA baseline reaches only 1.0%.
  arXiv  Detail & Related papers  (2026-06-09T09:13:36Z)
• BifrostUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation [6.733928872257592]
  BifrostUMI is a portable, efficient, and robot-free data collection framework for humanoid robots.<n>We demonstrate the efficacy and versatility of the proposed framework across two distinct experimental scenarios.
  arXiv  Detail & Related papers  (2026-05-05T07:35:09Z)
• ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration [51.69384671495837]
  ActiveGlasses is a system for learning robot manipulation from ego-centric human demonstrations with active vision.<n>A stereo camera mounted on smart glasses serves as the sole perception device for both data collection and policy inference.<n>To enable zero-transfer, we extract object trajectories from demonstrations and use an object-centric point-cloud policy to jointly predict manipulation and head movement.
  arXiv  Detail & Related papers  (2026-04-09T17:59:08Z)
• OCRA: Object-Centric Learning with 3D and Tactile Priors for Human-to-Robot Action Transfer [84.22494391514066]
  OCRA is a framework for video-based human-to-Robot Action transfer.<n>It learns directly from human demonstration videos to enable robust manipulation.
  arXiv  Detail & Related papers  (2026-03-15T14:31:03Z)
• EmbodMocap: In-the-Wild 4D Human-Scene Reconstruction for Embodied Agents [85.77432303199176]
  We propose EmbodMocap, a portable and affordable data collection pipeline using two moving iPhones.<n>Our key idea is to jointly calibrate dual RGB-D sequences to reconstruct both humans and scenes.<n>Based on the collected data, we empower three embodied AI tasks: monocular human-scene-reconstruction, where we fine-tune feedforward models that output metric-scale, world-space aligned humans and scenes; physics-based character animation, where we prove our data could be used to scale human-object interaction skills and scene-aware motion tracking; and robot motion control, where we train a humanoid robot via
  arXiv  Detail & Related papers  (2026-02-26T16:53:41Z)
• Dexterous Manipulation Policies from RGB Human Videos via 3D Hand-Object Trajectory Reconstruction [24.49384094440561]
  We propose VIDEOMANIP, a device-free framework that learns dexterous manipulation directly from RGB human videos.<n>In simulation, the learned grasping model achieves a 70.25% success rate across 20 diverse objects using the Inspire Hand.<n>In the real world, manipulation policies trained from RGB videos achieve an average 62.86% success rate across seven tasks using the LEAP Hand.
  arXiv  Detail & Related papers  (2026-02-09T18:56:02Z)
• DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos [110.98100817695307]
  We introduce DreamDojo, a foundation world model that learns diverse interactions and dexterous controls from 44k hours of egocentric human videos.<n>Our work enables several important applications based on generative world models, including live teleoperation, policy evaluation, and model-based planning.
  arXiv  Detail & Related papers  (2026-02-06T18:49:43Z)
• Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrations [52.29884993824894]
  Learning multi-fingered robot policies from humans performing daily tasks in natural environments has long been a grand goal in the robotics community.<n>AINA enables learning multi-fingered policies from data collected by anyone, anywhere, and in any environment using Aria Gen 2 glasses.
  arXiv  Detail & Related papers  (2025-11-20T18:59:02Z)
• Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models [53.22792173053473]
  We introduce an interactive robotic manipulation framework called Polaris. Polaris integrates perception and interaction by utilizing GPT-4 alongside grounded vision models. We propose a novel Synthetic-to-Real (Syn2Real) pose estimation pipeline.
  arXiv  Detail & Related papers  (2024-08-15T06:40:38Z)
• From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation [26.738893736520364]
  We introduce a novel single-camera teleoperation system to collect the 3D demonstrations efficiently with only an iPad and a computer. We construct a customized robot hand for each user in the physical simulator, which is a manipulator resembling the same kinematics structure and shape of the operator's hand. With imitation learning using our data, we show large improvement over baselines with multiple complex manipulation tasks.
  arXiv  Detail & Related papers  (2022-04-26T17:59:51Z)
• Few-Shot Visual Grounding for Natural Human-Robot Interaction [0.0]
  We propose a software architecture that segments a target object from a crowded scene, indicated verbally by a human user. At the core of our system, we employ a multi-modal deep neural network for visual grounding. We evaluate the performance of the proposed model on real RGB-D data collected from public scene datasets.
  arXiv  Detail & Related papers  (2021-03-17T15:24:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.