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SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation

Description

The ability to manipulate tools significantly expands the set of tasks a robot can perform. Yet, tool manipulation represents a challenging class of dexterity, requiring grasping thin objects, in-hand object rotations, and forceful interactions. Since collecting teleoperation data for these behaviors is challenging, sim-to-real reinforcement learning (RL) is a promising alternative. However, prior approaches typically require substantial engineering effort to model objects and tune reward functi

Source

http://arxiv.org/abs/2602.16863v2