# MuST: Multi-Head Skill Transformer for Long-Horizon Dexterous Manipulation with Skill Progress

arXiv: https://arxiv.org/abs/2502.02753 · HTML: https://mlsanity.com/paper/2502.02753/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2025.

## Built on by (most cited first, 2 of 2)

- [ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL Problems](https://mlsanity.com/paper/2510.07151/) (2025)
- [RouteRLT: Learning When and Which RL Specialist Should Control a Vision-Language-Action Policy](https://mlsanity.com/paper/2609.26467/) (2026)

## Abstract (arXiv)

Robot picking and packing tasks require dexterous manipulation skills, such as rearranging objects to establish a good grasping pose, or placing and pushing items to achieve tight packing. These tasks are challenging for robots due to the complexity and variability of the required actions. To tackle the difficulty of learning and executing long-horizon tasks, we propose a novel framework called the Multi-Head Skill Transformer (MuST). This model is designed to learn and sequentially chain together multiple motion primitives (skills), enabling robots to perform complex sequences of actions effectively. MuST introduces a "progress value" for each skill, guiding the robot on which skill to execute next and ensuring smooth transitions between skills. Additionally, our model is capable of expanding its skill set and managing various sequences of sub-tasks efficiently. Extensive experiments in both simulated and real-world environments demonstrate that MuST significantly enhances the robot's ability to perform long-horizon dexterous manipulation tasks.