ObjRetarget: An Object-Aware Motion Retargeting Framework with Anthropomorphic Arm Constraints and Polyhedral Hand Modeling
- URL: http://arxiv.org/abs/2607.03828v1
- Date: Sat, 04 Jul 2026 11:31:23 GMT
- Title: ObjRetarget: An Object-Aware Motion Retargeting Framework with Anthropomorphic Arm Constraints and Polyhedral Hand Modeling
- Abstract summary: dexterous manipulation from human manipulation videos requires reliably human intent to executable robot actions.<n>Existing methods often ignore explicit contact modeling or reinforcement, resulting in limited accuracy and generalization.<n>We propose Retarget, a human-to-robot motion framework for learning robot dexterous manipulation from human videos.
- Score: 9.378632224914458
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Learning robot dexterous manipulation from human manipulation videos requires reliably retargeting human intent to executable robot actions while maintaining stable hand-object contact, which remains a key challenge in embodied intelligence. Existing retargeting methods often ignore explicit contact modeling or rely on reinforcement learning, resulting in limited accuracy and generalization. To address this, we propose ObjRetarget, a human-to-robot motion retargeting framework for learning robot dexterous manipulation from human videos, which integrates anthropomorphic arm trajectory constraints with structured hand-object geometric modeling. For arm motion, reference trajectories extracted from human videos are used for initialization, followed by anthropomorphic constraints and redundancy-aware optimization to generate natural and accurate movements. For hand manipulation, ObjRetarget represents multi-finger contacts using polytope clusters and preserves contact structure through geometric invariants to improve stability. Experiments on real robots show that ObjRetarget improves manipulation success rates and contact stability across multiple dexterous tasks, and generalizes well to different demonstrations, object poses, and task settings.
Related papers
- Unified Motion Retargeting for Humanoids with Learned Point Cloud Correspondence [24.060385614379783]
We present Unified Motion Retargeting (UMR), a framework that learns dense point cloud correspondence without requiring manually designed human-robot mappings.<n>UMR achieves higher motion fidelity and plausibility than state-of-the-art methods.
arXiv Detail & Related papers (2026-09-02T05:43:19Z) - AnyWorld: Factorized Egocentric World Models for Cross-Embodiment Generalization [88.25615187835321]
We propose AnyWorld, a cross-embodiment world modeling framework.<n>Our model factorizes an interaction into action, camera, and embodiment.<n>We train the model with large-scale human interaction pretraining.
arXiv Detail & Related papers (2026-08-29T12:55:15Z) - H2R-Bench: Benchmarking Human-to-Robot Manipulation Video Generation in World Models [56.60098789696351]
H2R-Bench is a benchmark for evaluating cross-embodiment human-to-robot manipulation video generation.<n>Each benchmark instance contains a human demonstration video, target embodiment constraints, and source-grounded annotations.<n>We benchmark eleven state-of-the-art video generation models across six manipulation families and two robot embodiments.
arXiv Detail & Related papers (2026-08-13T10:14:33Z) - GIRAF: Towards Generalizable Human Interactions with Articulated Objects [50.52795413484619]
We present a text-conditioned diffusion model that addresses the challenge of Synthesizing realistic full-body human interactions with articulated objects.<n>Our method demonstrated strong generalization to unseen object configurations, surpassing current state-of-the-art methods.
arXiv Detail & Related papers (2026-07-08T19:30:44Z) - EgoInfinity: A Web-Scale 4D Hand-Object Interaction Data Engine for Any-View Robot Retargeting and Video-to-Action Robot Learning [10.780924973366737]
EgoInfinity is a universal 4D hand-object interaction data engine that enables web-scale data generation for robot acquisition and learning.<n>EgoInfinity is a modular engine integrating perception, segmentation, reconstruction, interaction-aware refinement, and naive to automate this traditionally unscalable video-to-action problem.
arXiv Detail & Related papers (2026-06-16T00:44:16Z) - GenHOI: Contact-Aware Humanoid-Object Interaction by Imitating Generated Videos without Task-Specific Training [20.414478780328437]
Existing methods often require time-consuming task-specific policy training or rely on rigid trajectory replay.<n>We present textitGenHOI, a framework that enables humanoid robots to perform diverse object-interaction tasks in a zero-shot manner.<n>We validate the proposed framework in extensive simulation and real-world experiments across diverse object-interaction tasks.
arXiv Detail & Related papers (2026-06-11T07:31:05Z) - MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction [54.36564144414704]
MeshMimic is an innovative framework that bridges 3D scene reconstruction and embodied intelligence to enable humanoid robots to learn coupled "motion-terrain" interactions directly from video.<n>By leveraging state-of-the-art 3D vision models, our framework precisely segments and reconstructs both human trajectories and the underlying 3D geometry of terrains and objects.
arXiv Detail & Related papers (2026-02-17T17:09:45Z) - 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) - OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction [76.44108003274955]
A dominant paradigm for teaching humanoid robots complex skills is to retarget human motions as kinematic references to train reinforcement learning policies.<n>We introduce OmniRetarget, an interaction-preserving data generation engine based on an interaction mesh.<n>By minimizing the Laplacian deformation between the human and robot meshes, OmniRetarget generates kinematically feasible trajectories.
arXiv Detail & Related papers (2025-09-30T17:59:02Z) - Task-Oriented Human-Object Interactions Generation with Implicit Neural
Representations [61.659439423703155]
TOHO: Task-Oriented Human-Object Interactions Generation with Implicit Neural Representations.
Our method generates continuous motions that are parameterized only by the temporal coordinate.
This work takes a step further toward general human-scene interaction simulation.
arXiv Detail & Related papers (2023-03-23T09:31:56Z) - V-MAO: Generative Modeling for Multi-Arm Manipulation of Articulated
Objects [51.79035249464852]
We present a framework for learning multi-arm manipulation of articulated objects.
Our framework includes a variational generative model that learns contact point distribution over object rigid parts for each robot arm.
arXiv Detail & Related papers (2021-11-07T02:31:09Z)
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.