[Paper] Mana: Dexterous Manipulation of Articulated Tools
Source: arXiv - 2606.13677v1
Overview
Articulated tool manipulation remains a major challenge in dexterous robotics due to the need to coordinate internal degrees of freedom and contact-rich interactions. While prior work has largely focused on rigid objects, articulated tool use remains underexplored because of its physical complexity and the difficulty of learning functional grasping and manipulation policies. We present Mana (Manipulation Animator), a general sim-to-real framework that reinterprets dexterous manipulation as an animation problem. Inspired by computer animation, Mana employs a coarse-to-fine pipeline that transforms procedurally-generated grasp keyframes into manipulation trajectories through motion planning and reinforcement learning. The data generation process is largely automatic, requiring only a few mouse clicks to specify functional affordances (<1 minute per tool). Across four articulated tools spanning different scales and joint types, Mana achieves zero-shot sim-to-real transfer for both grasping and in-hand manipulation, demonstrating a scalable approach to dexterous articulated tool use.
Key Contributions
This paper presents research in the following areas:
- cs.RO
- cs.AI
- cs.CV
- cs.LG
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.RO.
Authors
- Zhao-Heng Yin
- Guanya Shi
- Pieter Abbeel
- C. Karen Liu
Paper Information
- arXiv ID: 2606.13677v1
- Categories: cs.RO, cs.AI, cs.CV, cs.LG
- Published: June 11, 2026
- PDF: Download PDF