# DexSkills: Skill Segmentation Using Haptic Data for Learning Autonomous Long-Horizon Robotic Manipulation Tasks

arXiv: https://arxiv.org/abs/2405.03476 · HTML: https://mlsanity.com/paper/2405.03476/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2024.

## Built on by (most cited first, 10 of 10)

- [ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning](https://mlsanity.com/paper/2503.21860/) (2025)
- [AtomicVLA: Unlocking the Potential of Atomic Skill Learning in Robots](https://mlsanity.com/paper/2603.07648/) (2026)
- [End-to-End Dexterous Arm-Hand VLA Policies via Shared Autonomy: VR Teleoperation Augmented by Autonomous Hand VLA Policy for Efficient Data Collection](https://mlsanity.com/paper/2511.00139/) (2025)
- [Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey](https://mlsanity.com/paper/2506.00098/) (2025)
- [Learning Long-Horizon Robot Manipulation Skills via Privileged Action](https://mlsanity.com/paper/2502.15442/) (2025)
- [Hand-in-the-Loop: Improving VLA Policies for Dexterous Manipulation via Seamless Hand-Arm Intervention](https://mlsanity.com/paper/2605.15157/) (2026)
- [Improving Vision-Language-Action Model Fine-Tuning with Structured Stage and Keyframe Supervision](https://mlsanity.com/paper/2606.26801/) (2026)
- [Offline Discovery of Interpretable Skills from Multi-Task Trajectories](https://mlsanity.com/paper/2602.01018/) (2026)
- [Modality Selection and Skill Segmentation via Cross-Modality Attention](https://mlsanity.com/paper/2504.14573/) (2025)
- [TACTFUL: Tactile-Driven Exploration For Object Localization and Identification in Confined Environments](https://mlsanity.com/paper/2606.24712/) (2026)

## Abstract (arXiv)

Effective execution of long-horizon tasks with dexterous robotic hands remains a significant challenge in real-world problems. While learning from human demonstrations have shown encouraging results, they require extensive data collection for training. Hence, decomposing long-horizon tasks into reusable primitive skills is a more efficient approach. To achieve so, we developed DexSkills, a novel supervised learning framework that addresses long-horizon dexterous manipulation tasks using primitive skills. DexSkills is trained to recognize and replicate a select set of skills using human demonstration data, which can then segment a demonstrated long-horizon dexterous manipulation task into a sequence of primitive skills to achieve one-shot execution by the robot directly. Significantly, DexSkills operates solely on proprioceptive and tactile data, i.e., haptic data. Our real-world robotic experiments show that DexSkills can accurately segment skills, thereby enabling autonomous robot execution of a diverse range of tasks.