# DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove

arXiv: https://arxiv.org/abs/2502.07730 · HTML: https://mlsanity.com/paper/2502.07730/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2025.

## Built on by (most cited first, 19 of 19)

- [DexWild: Dexterous Human Interactions for In-the-Wild Robot Policies](https://mlsanity.com/paper/2505.07813/) (2025)
- [DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation](https://mlsanity.com/paper/2505.24853/) (2025)
- [SPIDER: Scalable Physics-Informed Dexterous Retargeting](https://mlsanity.com/paper/2511.09484/) (2025)
- [DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation](https://mlsanity.com/paper/2509.04441/) (2025)
- [ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations](https://mlsanity.com/paper/2510.01607/) (2025)
- [OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer](https://mlsanity.com/paper/2512.08920/) (2025)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA](https://mlsanity.com/paper/2603.08122/) (2026)
- [MILE: A Mechanically Isomorphic Hand Exoskeleton and Visuotactile Robotic Hand for Data Collection in Dexterous Manipulation](https://mlsanity.com/paper/2512.00324/) (2025)
- [Hand-in-the-Loop: Improving VLA Policies for Dexterous Manipulation via Seamless Hand-Arm Intervention](https://mlsanity.com/paper/2605.15157/) (2026)
- [LeVR: A Modular VR Teleoperation Framework for Imitation Learning in Dexterous Manipulation](https://mlsanity.com/paper/2509.14349/) (2025)
- [IGen: Scalable Data Generation for Robot Learning from Open-World Images](https://mlsanity.com/paper/2512.01773/) (2025)
- [Structural Action Transformer for 3D Dexterous Manipulation](https://mlsanity.com/paper/2603.03960/) (2026)
- [HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies](https://mlsanity.com/paper/2603.12243/) (2026)
- [Tele-Catch: Adaptive Teleoperation for Dexterous Dynamic 3D Object Catching](https://mlsanity.com/paper/2603.28427/) (2026)
- [Learning Dexterous Grasping from Sparse Taxonomy Guidance](https://mlsanity.com/paper/2604.04138/) (2026)
- [ProxiDex: Learning Dynamics-Guided Proximity Policy for Dexterous Manipulation](https://mlsanity.com/paper/2609.16586/) (2026)
- [DexTaG: Tactile-as-Guidance in Reinforcement Learning for Dexterous Manipulation](https://mlsanity.com/paper/2609.33882/) (2026)
- [Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation](https://mlsanity.com/paper/2610.17161/) (2026)

## Abstract (arXiv)

Dexterous hand teleoperation plays a pivotal role in enabling robots to achieve human-level manipulation dexterity. However, current teleoperation systems often rely on expensive equipment and lack multi-modal sensory feedback, restricting human operators' ability to perceive object properties and perform complex manipulation tasks. To address these limitations, we present DOGlove, a low-cost, precise, and haptic force feedback glove system for teleoperation and manipulation. DoGlove can be assembled in hours at a cost under 600 USD. It features a customized joint structure for 21-DoF motion capture, a compact cable-driven torque transmission mechanism for 5-DoF multidirectional force feedback, and a linear resonate actuator for 5-DoF fingertip haptic feedback. Leveraging action and haptic force retargeting, DOGlove enables precise and immersive teleoperation of dexterous robotic hands, achieving high success rates in complex, contact-rich tasks. We further evaluate DOGlove in scenarios without visual feedback, demonstrating the critical role of haptic force feedback in task performance. In addition, we utilize the collected demonstrations to train imitation learning policies, highlighting the potential and effectiveness of DOGlove. DOGlove's hardware and software system will be fully open-sourced at https://do-glove.github.io/.