MIDAS Hand: Modular low-Impedance Direct-drive Anthropomorphic Sensing Hand
- URL: http://arxiv.org/abs/2607.14487v1
- Date: Thu, 16 Jul 2026 01:59:56 GMT
- Title: MIDAS Hand: Modular low-Impedance Direct-drive Anthropomorphic Sensing Hand
- Abstract summary: 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.
- Score: 1.6170460121555008
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Dexterous manipulation is limited not only by algorithms but by a shortage of accessible hand hardware that combines human-scale morphology, ease of manufacturing or maintenance, tactile sensing, and practical cost. Existing dexterous hands tend to optimize some of these properties at the expense of others. We present MIDAS Hand, a low-cost, open-source, human-scale dexterous hand with integrated tactile sensing for manipulation research. MIDAS Hand provides 16 total degrees of freedom (DoF) with 13 active DoF, directly driven actuation with measurably low backdrive torque, and 283 three-axis tactile taxels in a compact 700 g package with a bill of materials under 3,000 USD. Built from 3D-printed components, it assembles in under three hours while providing the strength, repeatability, and maintainability needed for repeated real-world experiments. Alongside the hardware, we release a full stack: design files, build documentation, control and tactile Python APIs, simulation models, and retargeting and teleoperation pipelines. We characterize MIDAS Hand through workspace and grasp-taxonomy analysis, payload and reliability tests, backdrivability measurements, and teleoperation demonstrations with tactile sensing, showing that it offers a balanced, reproducible platform for tactile dexterous manipulation and human-to-robot data collection. Project page: https://midas-hand.com
Related papers
- Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands [99.00248904465089]
Dexterous hands differ in finger structure, contact surfaces, and sensor layouts.<n>We present Bench2Dex, a simulation benchmark for visuo-tactile bimanual manipulation across 12 dexterous hands.
arXiv Detail & Related papers (2026-09-14T15:22:59Z) - 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) - 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) - RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning [21.875676616617838]
We present RealDexUMI, a wearable universal manipulation interface built around a shared dexterous end-effector module.<n>A palm-side is teleoperation maps human finger inputs to robot-hand commands, enabling real-time, glove-free, intuitive, and precise hand control.
arXiv Detail & Related papers (2026-06-04T11:28:23Z) - Dexora: Open-source VLA for High-DoF Bimanual Dexterity [97.98653150757526]
We introduce Dexora, the first open-source VLA system that targets dual-arm, dual-hand high-DoF manipulation.<n>We design a hybrid teleoperation pipeline that decouples gross arm kinematics from fine finger motion.<n>We show that Dexora outperforms competitive VLA baselines on both basic and dexterous benchmarks.
arXiv Detail & Related papers (2026-05-18T17:50:32Z) - MM-Hand: A 21-DOF Multi-modal Modular Dexterous Robotic Hand with Remote Actuation [25.7298718668076]
MM-Hand is a 21-DOF Multimodal Modular dexterous hand based on remote tendon-driven actuation.<n>The hand integrates spring-return tendon-driven fingers, modular 3D-printed finger and palm structures, quick tendon connectors for maintenance, and a multimodal sensing system.<n> MM-Hand provides a lightweight, sensor-rich, and maintainable hardware platform for dexterous manipulation research.
arXiv Detail & Related papers (2026-04-19T04:12:04Z) - Characterization, Analytical Planning, and Hybrid Force Control for the Inspire RH56DFX Hand [2.574393697080417]
dexterous robot hands are increasingly prevalent, but many remain difficult to use as scientific instruments.<n>We contribute three improvements to the Inspire RH56DFX to transform it from a black-box device to a research tool.<n>We validate these components on peg-in-hole insertion, achieving 65% success and outperforming a wrist-force-only baseline of 10%.
arXiv Detail & Related papers (2026-03-09T22:25:38Z) - Development of the Bioinspired Tendon-Driven DexHand 021 with Proprioceptive Compliance Control [10.382085972192723]
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.
arXiv Detail & Related papers (2025-11-05T14:07:03Z) - 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) - Learning Visuotactile Skills with Two Multifingered Hands [80.99370364907278]
We explore learning from human demonstrations using a bimanual system with multifingered hands and visuotactile data.
Our results mark a promising step forward in bimanual multifingered manipulation from visuotactile data.
arXiv Detail & Related papers (2024-04-25T17:59:41Z) - Reconfigurable Data Glove for Reconstructing Physical and Virtual Grasps [100.72245315180433]
We present a reconfigurable data glove design to capture different modes of human hand-object interactions.
The glove operates in three modes for various downstream tasks with distinct features.
We evaluate the system's three modes by (i) recording hand gestures and associated forces, (ii) improving manipulation fluency in VR, and (iii) producing realistic simulation effects of various tool uses.
arXiv Detail & Related papers (2023-01-14T05:35:50Z)
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.