Modular Anthropomorphic Hand Design via Multi-Parameter Finger Benchmarking and Selection
- URL: http://arxiv.org/abs/2606.11826v1
- Date: Wed, 10 Jun 2026 09:03:34 GMT
- Title: Modular Anthropomorphic Hand Design via Multi-Parameter Finger Benchmarking and Selection
- Abstract summary: We develop a robotic hand platform where fingers can be modularly integrated into a full teleoperated hand.<n> Candidate finger designs are assessed using both mechanism-oriented and task-relevant metrics.<n>The framework is validated through the development of an anthropomorphic robotic hand with optimized fingers.
- Score: 7.144298246891022
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Designing anthropomorphic dexterous robotic hands remains challenging as the design space straddles morphology, actuation, and sensing properties, and performance metrics span both task-dependent and task-agnostic. Existing optimization methods are often unstructured or consider only a single performance metric, limiting systematic comparison and targeted refinement. While the design considerations of the entire hand are significant, the individual finger properties play a key role in dexterity. By developing a robotic hand platform where fingers can be modularly integrated into a full teleoperated hand, we propose that optimizing the fingers can significantly improve overall hand performance. This approach enables rapid screening of different finger-level prototypes through a number of quantitative benchmarks before their integration into the hand for task-level validation. Candidate finger designs (incorporating variations in joint, bone, skin, and sensor placement) are assessed using both mechanism-oriented and task-relevant metrics, which establish a quantitative link between component design and full hand embodiment. The framework is validated through the development of an anthropomorphic robotic hand with optimized fingers, demonstrating how these fingers enable performance improvements across tasks, including multi-object grasping and light bulb screwing.
Related papers
- Morphology and actuation as inductive biases in robotic hand manipulation [0.0]
A unified framework is presented in which the kinematic and actuation stages are analysed separately and in composition.<n>It is applied to two hands representing opposing design philosophies, the Shadow Dexterous Hand and the Anatomically Correct, Biomechatronic Hand.<n>All parameters are derived from the hands' canonical digital representations.
arXiv Detail & Related papers (2026-09-04T14:38:13Z) - AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion [60.79507611737292]
Humans achieve stable and adaptive grasps by seamlessly integrating visual perception and tactile feedback.<n>Existing robotic grasping approaches rely on visual inputs and lack mechanisms for tactile-guided adaptation after contact.<n>We propose a unified visuo-tactile-fusion grasping framework that integrates grasp generation, feasibility prediction, and adaptive refinement.
arXiv Detail & Related papers (2026-08-06T16:29:11Z) - 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) - Function-based Parametric Co-Design Optimization of Dexterous Hands [5.05447792208957]
We introduce a comprehensive framework for robotic hand generation that unifies palm structure, finger kinematics, fingertip geometry, and fine-scale surface curvatures.<n>We validate the framework on design optimization in grasp stability tasks in simulation and real-world dynamic scenarios.<n>Our framework produces simulation- and fabrication-ready hand models and will be released as open-source.
arXiv Detail & Related papers (2026-04-30T08:06:00Z) - A Kinematic Framework for Evaluating Pinch Configurations in Robotic Hand Design without Object or Contact Models [4.583417032181106]
This study proposes a kinematic evaluation method for analyzing pinch configurations of robotic hands based on interactions between fingertip workspaces.<n>The proposed evaluation framework can serve as a useful tool for comparing different robotic hand designs and analyzing pinch capability during the design stage.
arXiv Detail & Related papers (2026-04-22T15:38:40Z) - 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) - 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) - From Power to Precision: Learning Fine-grained Dexterity for Multi-fingered Robotic Hands [20.469682777841207]
Human grasps can be roughly categorized into two types: power grasps and precision grasps.<n>Today's multi-fingered robotic hands are effective in power grasps, but for tasks requiring precision, parallel grippers are still more widely adopted.<n>We bridge this gap by jointly optimizing the control and hardware design of a multi-fingered dexterous hand.
arXiv Detail & Related papers (2025-11-17T18:56:50Z) - Decomposed Vector-Quantized Variational Autoencoder for Human Grasp Generation [27.206656215734295]
We propose a novel Decomposed Vector-Quantized Variational Autoencoder (DVQ-VAE) to generate realistic human grasps.
Part-aware decomposed architecture facilitates more precise management of the interaction between each component of hand and object.
Our model achieved about 14.1% relative improvement in the quality index compared to the state-of-the-art methods in four widely-adopted benchmarks.
arXiv Detail & Related papers (2024-07-19T06:41:16Z) - MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints [50.61346764110482]
We integrate a musculoskeletal system with a learnable parametric hand model, MANO, to create MS-MANO.
This model emulates the dynamics of muscles and tendons to drive the skeletal system, imposing physiologically realistic constraints on the resulting torque trajectories.
We also propose a simulation-in-the-loop pose refinement framework, BioPR, that refines the initial estimated pose through a multi-layer perceptron network.
arXiv Detail & Related papers (2024-04-16T02:18:18Z) - Fin-QD: A Computational Design Framework for Soft Grippers: Integrating
MAP-Elites and High-fidelity FEM [14.554559832990835]
This work bridges the gap of computationally exploring the vast design space of finger-based soft grippers while grasping large geometrically distinct object types with a simple control scheme.
arXiv Detail & Related papers (2023-11-21T09:38:03Z) - Deformer: Dynamic Fusion Transformer for Robust Hand Pose Estimation [59.3035531612715]
Existing methods often struggle to generate plausible hand poses when the hand is heavily occluded or blurred.
In videos, the movements of the hand allow us to observe various parts of the hand that may be occluded or blurred in a single frame.
We propose the Deformer: a framework that implicitly reasons about the relationship between hand parts within the same image.
arXiv Detail & Related papers (2023-03-09T02:24:30Z) - A Skeleton-Driven Neural Occupancy Representation for Articulated Hands [49.956892429789775]
Hand ArticuLated Occupancy (HALO) is a novel representation of articulated hands that bridges the advantages of 3D keypoints and neural implicit surfaces.
We demonstrate the applicability of HALO to the task of conditional generation of hands that grasp 3D objects.
arXiv Detail & Related papers (2021-09-23T14:35:19Z)
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.