One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation

• URL: http://arxiv.org/abs/2602.16712v1
• Date: Wed, 18 Feb 2026 18:59:57 GMT
• Title: One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation
• Abstract summary: We introduce a parameterized canonical representation that unifies a broad spectrum of dexterous hand architectures.<n>It comprises a unified parameter space and a canonical URDF format, offering three key advantages.<n>We validate these advantages through extensive analysis and experiments, including grasp policy replay, VAE latent encoding, and cross-embodiment zero-shot transfer.
• Score: 23.25726061664319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dexterous manipulation policies today largely assume fixed hand designs, severely restricting their generalization to new embodiments with varied kinematic and structural layouts. To overcome this limitation, we introduce a parameterized canonical representation that unifies a broad spectrum of dexterous hand architectures. It comprises a unified parameter space and a canonical URDF format, offering three key advantages. 1) The parameter space captures essential morphological and kinematic variations for effective conditioning in learning algorithms. 2) A structured latent manifold can be learned over our space, where interpolations between embodiments yield smooth and physically meaningful morphology transitions. 3) The canonical URDF standardizes the action space while preserving dynamic and functional properties of the original URDFs, enabling efficient and reliable cross-embodiment policy learning. We validate these advantages through extensive analysis and experiments, including grasp policy replay, VAE latent encoding, and cross-embodiment zero-shot transfer. Specifically, we train a VAE on the unified representation to obtain a compact, semantically rich latent embedding, and develop a grasping policy conditioned on the canonical representation that generalizes across dexterous hands. We demonstrate, through simulation and real-world tasks on unseen morphologies (e.g., 81.9% zero-shot success rate on 3-finger LEAP Hand), that our framework unifies both the representational and action spaces of structurally diverse hands, providing a scalable foundation for cross-hand learning toward universal dexterous manipulation.

Related papers

• Scaling Behavior Foundation Model for Humanoid Robots [77.31115920959364]
  Behavior Foundation Models (BFMs) have emerged as a promising solution to address challenges by leveraging large-scale behavioral data.<n>We show that our approach yields significant improvements in control fidelity and task generalization.<n>These results establish BFM as a principled and effective foundation for scalable and general-purpose humanoid control.
  arXiv  Detail & Related papers  (2026-07-16T16:08:27Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• X-DiffVLA: X-Embodied Diffusion Action Heads for Vision-Language-Action Models [39.033717938466246]
  We propose X-DiffVLA, a diffusion-based VLA model featuring a unified cross-embodied action head.<n>X-DiffVLA can leverage the generative strengths of diffusion models to capture both the diversity and latent correlations in cross-embodied datasets.<n>We show that X-DiffVLA achieves state-of-the-art performance, with improvements of 15.3% and 12.5%, respectively.
  arXiv  Detail & Related papers  (2026-05-24T12:41:34Z)
• DexGrasp-Zero: A Morphology-Aligned Policy for Zero-Shot Cross-Embodiment Dexterous Grasping [46.96135601170059]
  Cross-embodiment grasping is challenging due to heterogeneous hand kinematics and physical constraints.<n>Existing approaches typically predict intermediate motion targets and retarget them to each embodiment.<n>We propose DexGrasp-Zero, a policy that learns universal grasping skills from diverse embodiments.
  arXiv  Detail & Related papers  (2026-03-17T17:10:29Z)
• ReHARK: Refined Hybrid Adaptive RBF Kernels for Robust One-Shot Vision-Language Adaptation [3.8707695363745214]
  ReHARK is a training-free framework that reinterprets few-shot adaptation through global proximal regularization.<n>A new state-of-the-art for one-shot adaptation is established by ReHARK, which achieves an average accuracy of 65.83%.
  arXiv  Detail & Related papers  (2026-03-12T04:59:09Z)
• AffordGrasp: Cross-Modal Diffusion for Affordance-Aware Grasp Synthesis [50.793806818677716]
  AffordGrasp produces physically stable and semantically faithful human grasps with high precision.<n>AffordGrasp integrates an affordance-aware latent representation of hand poses with a dual-conditioning diffusion process.<n>We evaluate AffordGrasp across four instruction-augmented benchmarks derived from HO-3D, OakInk, GRAB, and AffordPose.
  arXiv  Detail & Related papers  (2026-03-09T06:56:35Z)
• Structural Action Transformer for 3D Dexterous Manipulation [80.07649565189035]
  Cross-embodiment skill transfer is a challenge for high-DoF robotic hands.<n>Existing methods, often relying on 2D observations and temporal-centric action representation, struggle to capture 3D spatial relations.<n>This paper proposes a new 3D dexterous manipulation policy that challenges this paradigm by introducing a structural-centric perspective.
  arXiv  Detail & Related papers  (2026-03-04T11:38:12Z)
• UniMorphGrasp: Diffusion Model with Morphology-Awareness for Cross-Embodiment Dexterous Grasp Generation [63.13144787866563]
  Cross-embodiment grasping aims to generate stable and diverse grasps for robotic hands with heterogeneous kinematic structures.<n>We propose textbfUniMorphGrasp, a diffusion-based framework that incorporates hand morphological information into the grasp generation process.<n>UniMorphGrasp achieves state-of-the-art performance on existing dexterous grasp benchmarks and exhibits strong zero-shot generalization to previously unseen hand structures.
  arXiv  Detail & Related papers  (2026-01-31T21:56:16Z)
• Do We Need Perfect Data? Leveraging Noise for Domain Generalized Segmentation [20.89655949578527]
  This paper presents FLEX-Seg, a framework that transforms this limitation into an opportunity for robust learning.<n>Experiments across five real-world datasets demonstrate consistent improvements over state-of-the-art methods.<n>Our findings validate that adaptive strategies for handling imperfect synthetic data lead to superior domain generalization.
  arXiv  Detail & Related papers  (2025-11-28T07:46:32Z)
• Hierarchical Neural Semantic Representation for 3D Semantic Correspondence [72.8101601086805]
  We design the hierarchical neural semantic representation (HNSR), which consists of a global semantic feature to capture high-level structure and multi-resolution local geometric features.<n>Second, we design a progressive global-to-local matching strategy, which establishes coarse semantic correspondence using the global semantic feature.<n>Third, our framework is training-free and broadly compatible with various pre-trained 3D generative backbones, demonstrating strong generalization across diverse shape categories.
  arXiv  Detail & Related papers  (2025-09-22T07:23:07Z)
• Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning [53.68371566336254]
  We argue that the key to better performance lies in meaningful latent modality structures instead of perfect modality alignment. Specifically, we design 1) a deep feature separation loss for intra-modality regularization; 2) a Brownian-bridge loss for inter-modality regularization; and 3) a geometric consistency loss for both intra- and inter-modality regularization.
  arXiv  Detail & Related papers  (2023-03-10T14:38:49Z)

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.