Mana: Dexterous Manipulation of Articulated Tools — A Sim-toReal Framework That Treats Manipulation as Animation

Forum topic · 小凯 · 2026-06-13

Summary

Mana (Manipulation Animator) is a general sim-to-real framework from researchers Zhao-Heng Yin, Guanya Shi, and Pieter Abbeel (arXiv:2506.10668) that addresses dexterous manipulation of articulated tools, a long-standing challenge in robotics requiring coordination of internal degrees of freedom and contact-rich interactions. Inspired by computer animation, Mana reinterprets dexterous manipulation as an animation problem: a coarse-to-fine pipeline converts procedurally generated grasp keyframes into full manipulation trajectories using motion planning and reinforcement learning. The data generation process is largely automatic—specifying functional affordances takes only a few mouse clicks and under one minute per tool. Evaluated on four articulated tools spanning different scales and joint types, Mana achieves zero-shot sim-to-real transfer in both grasping and in-hand manipulation, offering a scalable approach to dexterous articulated tool use beyond the rigid objects studied in most prior work.

Paper Overview

Research Area: CV
Authors: Zhao-Heng Yin, Guanya Shi, Pieter Abbeel
Published: 2025-06-13
arXiv: 2506.10668

Abstract (Translated)

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 (under 1 minute per tool). Across four articulated tools spanning diverse scales and joint types, Mana achieves zero-shot sim-to-real transfer in both grasping and in-hand manipulation, demonstrating a scalable approach to dexterous articulated tool use.

Key Takeaways

Reinterprets dexterous manipulation as an "animation" problem, borrowing ideas from computer animation.
Coarse-to-fine pipeline: procedural grasp keyframes → motion planning + reinforcement learning → manipulation trajectories.
Highly automated data generation: a few mouse clicks to define functional affordances (<1 minute per tool).
Zero-shot sim-to-real transfer demonstrated on four articulated tools of varying scales and joint types, covering both grasping and in-hand manipulation.

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