Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916948529381376 |
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| author | Röstel, Lennart Winkelbauer, Dominik Pitz, Johannes Sievers, Leon Bäuml, Berthold |
| author_facet | Röstel, Lennart Winkelbauer, Dominik Pitz, Johannes Sievers, Leon Bäuml, Berthold |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_13253 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic Röstel, Lennart Winkelbauer, Dominik Pitz, Johannes Sievers, Leon Bäuml, Berthold Robotics Artificial Intelligence 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. |
| title | Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2505.13253 |