HumDex: Humanoid Dexterous Manipulation Made Easy

• URL: http://arxiv.org/abs/2603.12260v2
• Date: Fri, 13 Mar 2026 04:40:44 GMT
• Title: HumDex: Humanoid Dexterous Manipulation Made Easy
• Abstract summary: HumDex is a portable teleoperation system designed for humanoid whole-body dexterous manipulation.<n>Our system leverages IMU-based motion tracking to address the portability-precision trade-off.<n>For dexterous hand control, we introduce a learning-based method that generates smooth and natural hand motions without manual parameter tuning.
• Score: 30.352529523812347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates humanoid whole-body dexterous manipulation, where the efficient collection of high-quality demonstration data remains a central bottleneck. Existing teleoperation systems often suffer from limited portability, occlusion, or insufficient precision, which hinders their applicability to complex whole-body tasks. To address these challenges, we introduce HumDex, a portable teleoperation system designed for humanoid whole-body dexterous manipulation. Our system leverages IMU-based motion tracking to address the portability-precision trade-off, enabling accurate full-body tracking while remaining easy to deploy. For dexterous hand control, we further introduce a learning-based retargeting method that generates smooth and natural hand motions without manual parameter tuning. Beyond teleoperation, HumDex enables efficient collection of human motion data. Building on this capability, we propose a two-stage imitation learning framework that first pre-trains on diverse human motion data to learn generalizable priors, and then fine-tunes on robot data to bridge the embodiment gap for precise execution. We demonstrate that this approach significantly improves generalization to new configurations, objects, and backgrounds with minimal data acquisition costs. The entire system is fully reproducible and open-sourced at https://github.com/physical-superintelligence-lab/humdex.

Related papers

• Pre-training Visual Dexterity in Simulation [67.17477115017896]
  We introduce a pre-training framework for dexterous manipulation that uses data entirely collected in simulation.<n>With the help of five operators, we collect 75 hours of multi-task dexterous manipulation over one week, and use it to pre-train a causal transformer on a sequence modeling objective.<n>We find that our approach outperforms training behavior cloning policies from scratch, showing that simulation teleoperation is a viable pre-training source for real-world dexterous manipulation.
  arXiv  Detail & Related papers  (2026-08-16T20:21:33Z)
• HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning [52.022681285103126]
  We present HumanoidMimicGen, a method for generating humanoid legged loco-manipulation data.<n>Our method adapts contact-rich whole-body skills from a handful of source demonstrations to new states.<n>We show that whole-body visuomotor policies co-trained with data generated by HumanoidMimicGen outperform those trained only on real-world data by 20%.
  arXiv  Detail & Related papers  (2026-05-26T21:57:11Z)
• ULTRA: Unified Multimodal Control for Autonomous Humanoid Whole-Body Loco-Manipulation [55.467742403416175]
  We introduce a physics-driven neural algorithm that translates large-scale motion capture to humanoid embodiments.<n>We learn a unified multimodal controller that supports both dense references and sparse task specifications.<n>Results show that ULTRA generalizes to autonomous, goal-conditioned whole-body loco-manipulation from egocentric perception.
  arXiv  Detail & Related papers  (2026-03-03T18:59:29Z)
• ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning [59.64325421657381]
  Humanoid whole-body loco-manipulation promises transformative capabilities for daily service and warehouse tasks.<n>We introduce ResMimic, a two-stage residual learning framework for precise and expressive humanoid control from human motion data.<n>Results show substantial gains in task success, training efficiency, and robustness over strong baselines.
  arXiv  Detail & Related papers  (2025-10-06T17:47:02Z)
• SSSUMO: Real-Time Semi-Supervised Submovement Decomposition [0.6499759302108926]
  Submovement analysis offers valuable insights into motor control.<n>Existing methods struggle with reconstruction accuracy, computational cost, and validation.<n>We address these challenges using a semi-supervised learning framework.
  arXiv  Detail & Related papers  (2025-07-08T21:26:25Z)
• ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning [24.675197489823898]
  We introduce ManipTrans, a novel method for transferring human bimanual skills to dexterous robotic hands in simulation.<n>Experiments show that ManipTrans surpasses state-of-the-art methods in success rate, fidelity, and efficiency.<n>We also create DexManipNet, a large-scale dataset featuring previously unexplored tasks like pen capping and bottle unscrewing.
  arXiv  Detail & Related papers  (2025-03-27T17:50:30Z)
• DexterityGen: Foundation Controller for Unprecedented Dexterity [67.15251368211361]
  Teaching robots dexterous manipulation skills, such as tool use, presents a significant challenge.<n>Current approaches can be broadly categorized into two strategies: human teleoperation (for imitation learning) and sim-to-real reinforcement learning.<n>We introduce DexterityGen, which uses RL to pretrain large-scale dexterous motion primitives, such as in-hand rotation or translation.<n>In the real world, we use human teleoperation as a prompt to the controller to produce highly dexterous behavior.
  arXiv  Detail & Related papers  (2025-02-06T18:49:35Z)
• DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning [42.88605563822155]
  We present a large-scale automated data generation system that synthesizes trajectories from human demonstrations for humanoid robots with dexterous hands.<n>We generate 21K demos across these tasks from just 60 source human demos.<n>We also present a real-to-sim-to-real pipeline and deploy it on a real-world humanoid can sorting task.
  arXiv  Detail & Related papers  (2024-10-31T17:48:45Z)
• 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.