Mana: Dexterous Manipulation of Articulated Tools

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

Mana (Manipulation Animator) is a general sim-to-real framework addressing the challenge of dexterous manipulation of articulated tools. It reinterprets this complex problem as an animation task, employing a coarse-to-fine pipeline. This process transforms procedurally-generated grasp keyframes into manipulation trajectories using motion planning for geometric reaching and reinforcement learning for contact-rich phases. The data generation is largely automatic, requiring under 1 minute of human input per tool. Mana achieves zero-shot sim-to-real transfer for both grasping and in-hand manipulation across four articulated tools: tongs, pliers, clothespins, and syringes. The system, using an Allegro hand with custom compliant fingertips and a point-cloud-conditioned diffusion policy, demonstrates approximately 70% success rates for both grasping and in-hand manipulation, requiring 3-7N actuation force on objects around 1 cm thick.

Key takeaway

For robotics engineers developing dexterous manipulation systems, Mana offers a scalable sim-to-real framework for articulated tools. You should consider its coarse-to-fine data generation and diffusion policy to overcome exploration challenges and achieve zero-shot transfer. Implementing custom compliant fingertips and robust force randomization will further enhance real-world performance on thin objects, enabling more reliable autonomous tool use.

Key insights

Mana reinterprets dexterous articulated tool manipulation as an animation problem, enabling zero-shot sim-to-real transfer via a coarse-to-fine pipeline.

Principles

• Decompose long-horizon tasks into keyframes and short, phase-specific transitions.
• Robustness scales with state coverage and diverse force randomization in simulation.
• Custom compliant fingertips improve stable contact for thin, force-sensitive objects.

Method

Mana uses a coarse-to-fine pipeline: procedurally generate grasp keyframes, then use motion planning for collision-free reaching and reinforcement learning for contact-rich in-hand actuation.

In practice

• Apply point-cloud randomization during training for sim-to-real robustness.
• Design custom compliant fingertips to handle thin objects (e.g., 1 cm thickness).
• Utilize IsaacLab for GPU-accelerated, stable actuator simulation with high iterations.

Topics

• Dexterous Manipulation
• Articulated Tools
• Sim-to-Real Transfer
• Reinforcement Learning
• Motion Planning
• Diffusion Policy
• Robotic Grasping

Best for: Research Scientist, AI Scientist, Robotics Engineer, Machine Learning Engineer

Related on AIssential

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.