Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic

• URL: http://arxiv.org/abs/2505.13253v1
• Date: Mon, 19 May 2025 15:36:34 GMT
• Title: Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic
• Authors: Lennart Röstel, Dominik Winkelbauer, Johannes Pitz, Leon Sievers, Berthold Bäuml,
• Abstract summary: In-hand manipulation and grasping are fundamental yet often separately addressed tasks in robotics.<n>We propose a method for bridging this gap by leveraging the critic network of a reinforcement learning agent trained for in-hand manipulation.<n>Our experiments show that this method significantly increases the success rate of in-hand manipulation without requiring additional training.
• Score: 7.759447374181355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In-hand manipulation and grasping are fundamental yet often separately addressed tasks in robotics. For deriving in-hand manipulation policies, reinforcement learning has recently shown great success. However, the derived controllers are not yet useful in real-world scenarios because they often require a human operator to place the objects in suitable initial (grasping) states. Finding stable grasps that also promote the desired in-hand manipulation goal is an open problem. In this work, we propose a method for bridging this gap by leveraging the critic network of a reinforcement learning agent trained for in-hand manipulation to score and select initial grasps. Our experiments show that this method significantly increases the success rate of in-hand manipulation without requiring additional training. We also present an implementation of a full grasp manipulation pipeline on a real-world system, enabling autonomous grasping and reorientation even of unwieldy objects.

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