Development of the Bioinspired Tendon-Driven DexHand 021 with Proprioceptive Compliance Control
- URL: http://arxiv.org/abs/2511.03481v2
- Date: Sun, 09 Nov 2025 02:49:13 GMT
- Title: Development of the Bioinspired Tendon-Driven DexHand 021 with Proprioceptive Compliance Control
- Abstract summary: This letter presents Dex-Hand 021, a high-performance, cable-driven five-finger robotic hand with 12 active and 7 passive degrees of freedom (DoFs)<n>We propose a proprioceptive force-sensing-based admittance control method to enhance manipulation.<n> Experimental results demonstrate its superior performance: a single-finger load capacity exceeding 10 N, repeatability under 0.001 m, and force estimation errors below 0.2 N.
- Score: 10.382085972192723
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The human hand plays a vital role in daily life and industrial applications, yet replicating its multifunctional capabilities-including motion, sensing, and coordinated manipulation with robotic systems remains a formidable challenge. Developing a dexterous robotic hand requires balancing human-like agility with engineering constraints such as complexity, size-to-weight ratio, durability, and force-sensing performance. This letter presents Dex-Hand 021, a high-performance, cable-driven five-finger robotic hand with 12 active and 7 passive degrees of freedom (DoFs), achieving 19 DoFs dexterity in a lightweight 1 kg design. We propose a proprioceptive force-sensing-based admittance control method to enhance manipulation. Experimental results demonstrate its superior performance: a single-finger load capacity exceeding 10 N, fingertip repeatability under 0.001 m, and force estimation errors below 0.2 N. Compared to PID control, joint torques in multi-object grasping are reduced by 31.19%, significantly improves force-sensing capability while preventing overload during collisions. The hand excels in both power and precision grasps, successfully executing 33 GRASP taxonomy motions and complex manipulation tasks. This work advances the design of lightweight, industrial-grade dexterous hands and enhances proprioceptive control, contributing to robotic manipulation and intelligent manufacturing.
Related papers
- MIDAS Hand: Modular low-Impedance Direct-drive Anthropomorphic Sensing Hand [1.6170460121555008]
MIDAS Hand is a low-cost, open-source, human-scale dexterous hand for tactile manipulation research.<n>Built from 3D-printed components, it assembles in under three hours while providing strength, repeatability, and maintainability.<n>We characterize MIDAS Hand through grasp-taxonomy analysis, payload and reliability tests, backdrivability measurements, and teleoperation demonstrations with tactile sensing.
arXiv Detail & Related papers (2026-07-16T01:59:56Z) - T-Rex: Tactile-Reactive Dexterous Manipulation [139.71263755530654]
We introduce a variable-rate Mixture-of-Transformers (MoT) architecture equipped with a novel temporal tactile VQ-VAE encoder.<n>We demonstrate the effectiveness of tactile-reactive policies on 12 manipulation tasks requiring delicate force control and deformable object manipulation.
arXiv Detail & Related papers (2026-06-15T17:59:55Z) - AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning [56.20593626288444]
AetheRock is an arm-worn device featuring a modular and easily manufactured visuo-tactile sensor, GelSlim-MiniFab, at the fingertip.<n>We propose ForceVT, a representation learning framework that uses force and vision to guide fidelity-agnostic tactile learning.
arXiv Detail & Related papers (2026-06-08T17:38:57Z) - PHANTOM Hand [12.871356369910323]
Tendon-driven underactuated hands excel in adaptive grasping but often suffer from kinematic unpredictability and highly non-actuated force transmission.<n>We introduce the PHANTOM Hand (Hybrid Precision-Augmented Compliance): a modular, human-scale system featuring 6 actuators and 15 degrees of freedom (DoFs)<n>We propose a unified framework that bridges the gap between precise analytic shaping and robust compliant grasping.
arXiv Detail & Related papers (2026-03-24T12:52:18Z) - UniForce: A Unified Latent Force Model for Robot Manipulation with Diverse Tactile Sensors [51.88112610411651]
We propose a novel unified tactile representation learning framework that learns a shared latent force space across diverse tactile sensors.<n>UniForce reduces cross-sensor domain shift by jointly modeling inverse dynamics (image-to-force) and forward dynamics (force-to-image)<n>To avoid reliance on expensive external force/torque (F/T) sensors, we exploit static equilibrium and collect force-paired data via direct sensor--object--sensor interactions.
arXiv Detail & Related papers (2026-02-01T11:03:01Z) - Antagonistic Bowden-Cable Actuation of a Lightweight Robotic Hand: Toward Dexterous Manipulation for Payload Constrained Humanoids [4.320393382724067]
We present a lightweight anthropomorphic hand actuated by Bowden cables.<n>The design substantially reduces distal mass while maintaining anthropomorphic scale and dexterity.<n>Using the proposed methods, the hand assembly with a distal mass of 236g demonstrated reliable execution of dexterous tasks.
arXiv Detail & Related papers (2025-12-31T06:07:11Z) - CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation [70.5382178207975]
hIsight Perturbation (CHIP) is a plug-and-play module that enables controllable end-effector stiffness.<n>CHIP is easy to implement and requires neither data augmentation nor additional reward tuning.<n>We show that a generalist motion-tracking controller trained with CHIP can perform a diverse set of forceful manipulation tasks.
arXiv Detail & Related papers (2025-12-16T18:56:04Z) - Design and Validation of an Under-actuated Robotic Finger with Synchronous Tendon Routing [3.9881780222416316]
Tendon-driven robotic fingers provide advantages for dexterous manipulation through reduced actuator requirements.<n>This paper presents an under-actuated tendon-driven robotic finger (UTRF) featuring a synchronous tendon routing.<n>The proposed routing achieves predictable stiffness and reliable grasping performance with a minimal actuator count.
arXiv Detail & Related papers (2025-12-11T07:05:43Z) - Development of a 15-Degree-of-Freedom Bionic Hand with Cable-Driven Transmission and Distributed Actuation [20.94045484499254]
This work proposes a novel 15-DoF dexterous robotic hand with detailed analysis of its mechanical architecture, electrical system, and control system.<n>The bionic hand employs a new tendon-driven mechanism, significantly reducing the number of motors required by traditional tendon-driven systems.<n>The entire system weighs only 1.4kg, combining lightweight and high-performance features.
arXiv Detail & Related papers (2025-12-04T02:47:13Z) - 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) - Feel the Force: Contact-Driven Learning from Humans [52.36160086934298]
Controlling fine-grained forces during manipulation remains a core challenge in robotics.<n>We present FeelTheForce, a robot learning system that models human tactile behavior to learn force-sensitive manipulation.<n>Our approach grounds robust low-level force control in scalable human supervision, achieving a 77% success rate across 5 force-sensitive manipulation tasks.
arXiv Detail & Related papers (2025-06-02T17:57:52Z) - Imitation Learning for Adaptive Control of a Virtual Soft Exoglove [3.3030080038744947]
We propose a customized wearable robotic controller that is able to address specific muscle deficits and to provide compensation for hand-object manipulation tasks.<n>Video data of a same subject performing human grasping tasks is used to train a manipulation model through learning from demonstration.<n>This manipulation model is subsequently fine-tuned to perform object-specific interaction tasks.<n>The muscle forces in the musculoskeletal manipulation model are then weakened to simulate neurological motor impairments, which are later compensated by the actuation of a virtual wearable robotics glove.
arXiv Detail & Related papers (2025-05-14T03:09:21Z) - Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning [2.34860173297653]
This paper tackles the challenge of learning multi-goal dexterous hand manipulation tasks using model-based Reinforcement Learning.<n>We propose Goal-Conditioned Probabilistic Model Predictive Control (GC-PMPC) to describe the high-dimensional dexterous hand dynamics.<n>It successfully drives a cable-driven Dexterous hand, DexHand 021 with 12 Active DOFs and 5 tactile sensors, to learn manipulating a cubic die to three goal poses within approximately 80 minutes of interactions.
arXiv Detail & Related papers (2025-04-30T12:44:38Z) - RUKA: Rethinking the Design of Humanoid Hands with Learning [15.909251187339228]
This work presents RUKA, a tendon-driven humanoid hand that is compact, affordable, and capable.<n> RUKA has 5 fingers with 15 under degrees of freedom enabling diverse human-like grasps.<n>To address control challenges, we learn joint-to-actuator and fingertip-to-actuator models from motion-capture data collected by the MANUS glove.
arXiv Detail & Related papers (2025-04-17T17:58:59Z) - A High-Force Gripper with Embedded Multimodal Sensing for Powerful and Perception Driven Grasping [12.351326520775464]
We develop a modular high grasping force gripper equipped with embedded multi-modal perception functionalities.<n>The proposed gripper can generate a grasping force of 110 N in a compact implementation.<n>The high grasping force capability is combined with embedded multi-modal sensing, which includes an eye-in-hand camera, a Time-of-Flight (ToF) distance sensor, an Inertial Measurement Unit (IMU) and an omnidirectional microphone.
arXiv Detail & Related papers (2025-04-07T11:57:08Z) - Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning [54.26816599309778]
We propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL)<n> Specifically, we introduce a dynamic balance mechanism by leveraging an extended measure of Zero-Moment Point (ZMP)-driven rewards and task-driven rewards in a whole-body actor-critic framework.<n> Experiments conducted on a full-sized Unitree H1-2 robot verify the ability of our method to maintain balance on extremely narrow terrains.
arXiv Detail & Related papers (2025-02-24T14:53:45Z) - 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) - DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation [78.60543357822957]
Dexterous manipulation with contact-rich interactions is crucial for advanced robotics.<n>We introduce DexHandDiff, an interaction-aware diffusion planning framework for adaptive dexterous manipulation.<n>Our framework achieves an average of 70.7% success rate on goal adaptive dexterous tasks, highlighting its robustness and flexibility in contact-rich manipulation.
arXiv Detail & Related papers (2024-11-27T18:03:26Z) - Twisting Lids Off with Two Hands [82.21668778600414]
We show how policies trained in simulation can be effectively and efficiently transferred to the real world.
Specifically, we consider the problem of twisting lids of various bottle-like objects with two hands.
This is the first sim-to-real RL system that enables such capabilities on bimanual multi-fingered hands.
arXiv Detail & Related papers (2024-03-04T18:59:30Z) - Real-Time Model-Free Deep Reinforcement Learning for Force Control of a
Series Elastic Actuator [56.11574814802912]
State-of-the art robotic applications utilize series elastic actuators (SEAs) with closed-loop force control to achieve complex tasks such as walking, lifting, and manipulation.
Model-free PID control methods are more prone to instability due to nonlinearities in the SEA.
Deep reinforcement learning has proved to be an effective model-free method for continuous control tasks.
arXiv Detail & Related papers (2023-04-11T00:51:47Z) - Towards Safe Control of Continuum Manipulator Using Shielded Multiagent
Reinforcement Learning [1.2647816797166165]
The control of the robot is formulated as a one-DoF, one agent problem in the MADQN framework to improve the learning efficiency.
Shielded MADQN enabled the robot to perform point and trajectory tracking with submillimeter root mean square errors under external loads.
arXiv Detail & Related papers (2021-06-15T05:55:05Z)
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.