DexMani: Human-Derived Manipulability Guidance for Dexterous Rotation

• URL: http://arxiv.org/abs/2608.00554v1
• Date: Sat, 01 Aug 2026 09:17:30 GMT
• Title: DexMani: Human-Derived Manipulability Guidance for Dexterous Rotation
• Abstract summary: We introduce DexMani, a framework that transfers human demonstrations as contact-conditioned manipulability evolution.<n>We show that DexMani reaches an average success rate of 57.5% on LEAP Hand, outperforming other baselines and producing smoother rotatory motions.
• Score: 7.918622126766639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dexterous object rotation is a sequential contact problem: each support, release, and re-contact decision must both produce the desired object motion, and prepare the hand configuration for continued rotation. Existing reinforcement learning methods discover such movement patterns through trial and error on specific robotic hand embodiments, without explicitly accounting for how each contact transition affects the hand's ability to sustain object rotation in subsequent steps. We introduce DexMani, a framework that transfers human demonstrations as contact-conditioned manipulability evolution. This prior captures how successful human contact transitions reshape the object-rotation directions available to the hand. DexMani then learns this manipulability evolution and uses it to guide downstream reinforcement learning, enabling rotation skills to be acquired across robot embodiments with distinct kinematics and active-contact configurations. Across the Shadow Hand, Allegro Hand, and XHand, DexMani achieves the highest success rates in every evaluated setting for both seen and unseen objects. DexMani reaches an average success rate of 57.5% on LEAP Hand, outperforming other baselines and producing smoother rotatory motions. Project site: https://dexmani.github.io

Related papers

• Towards Human-level Dexterous Teleoperation [62.77371751776912]
  Human-level dexterous teleoperation requires precise control of object motion via dynamic hand-object contact.<n>We introduce TeleDexter, a hand-object co-tracking controller that maps operator intent into learned, low-level contact execution.<n>We evaluate TeleDexter on seven challenging dexterous teleoperation tasks spanning object reorientation and long-horizon tool use across two dexterous hands.
  arXiv  Detail & Related papers  (2026-07-13T12:36:47Z)
• AnyDexRT: Calibration-Free Dexterous Hand Retargeting with Few-Shot Human Guidance [52.973947477815614]
  AnyDexRT is a calibration-free method for intuitive dexterous teleoperation across human-like dexterous hands.<n>Experiments show that AnyDexRT improves quality, reduces manual tuning, and provides more intuitive and efficient control than prior methods.
  arXiv  Detail & Related papers  (2026-07-09T10:29:53Z)
• Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration [31.755221262774935]
  Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging.<n>We present Contact Wrench Guidance from Human Demonstration in Robotic Dexterous Manipulation (CHORD), a framework for long-horizon manipulation of rigid and articulated objects with reinforcement learning.
  arXiv  Detail & Related papers  (2026-06-22T22:54:33Z)
• Mana: Dexterous Manipulation of Articulated Tools [64.16038120159688]
  Mana is a general sim-to-real framework that reinterprets dexterous manipulation as an animation problem.<n>Inspired by computer animation, Mana employs a coarse-to-fine pipeline that transforms procedurally-generated grasps into manipulation trajectories.<n>Mana achieves zero-shot sim-to-real transfer for both grasping and in-hand manipulation, demonstrating a scalable approach to dexterous articulated tool use.
  arXiv  Detail & Related papers  (2026-06-11T17:59:49Z)
• DexImit: Learning Bimanual Dexterous Manipulation from Monocular Human Videos [56.64773686434068]
  We propose DexImit, an automated framework that converts human manipulation videos into physically plausible robot data.<n>DexImit can generate large-scale robot data based on human videos, either from the Internet or video generation models.<n>It is capable of handling diverse manipulation tasks, including tool use, long-horizon tasks, and fine-grained manipulations.
  arXiv  Detail & Related papers  (2026-02-10T18:59:02Z)
• DexFormer: Cross-Embodied Dexterous Manipulation via History-Conditioned Transformer [18.11650194615062]
  Dexterous manipulation remains one of the most challenging problems in robotics.<n>We present DexFormer, an end-to-end, dynamics-aware cross-embodiment policy.<n>Our results show that a single policy can generalize across heterogeneous hand embodiments.
  arXiv  Detail & Related papers  (2026-02-09T05:16:48Z)
• Learning Generalizable Hand-Object Tracking from Synthetic Demonstrations [71.15185260689928]
  We present a system for learning generalizable hand-object tracking controllers purely from synthetic data, without requiring any human demonstrations.<n>Our approach makes two key contributions: (1) HOP, a Hand-Object Planner, which can synthesize diverse hand-object trajectories; and (2) HOT, a Hand-Object Tracker, that bridges synthetic-to-physical transfer through reinforcement learning and interaction imitation learning.
  arXiv  Detail & Related papers  (2025-12-22T17:08:54Z)
• DexCanvas: Bridging Human Demonstrations and Robot Learning for Dexterous Manipulation [25.208854363099352]
  This dataset contains 7,000 hours of dexterous hand-object interactions seeded from 70 hours of real human demonstrations.<n>Each entry combines synchronized multi-view RGB-D, high-precision mocap with MANO hand parameters, and per-frame contact points with physically consistent force profiles.<n>Our real-to-sim pipeline uses reinforcement learning to train policies that control an actuated MANO hand in physics simulation.
  arXiv  Detail & Related papers  (2025-10-17T16:08:14Z)
• RealDex: Towards Human-like Grasping for Robotic Dexterous Hand [64.33746404551343]
  We introduce RealDex, a pioneering dataset capturing authentic dexterous hand grasping motions infused with human behavioral patterns.<n>RealDex holds immense promise in advancing humanoid robot for automated perception, cognition, and manipulation in real-world scenarios.
  arXiv  Detail & Related papers  (2024-02-21T14:59:46Z)

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.