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

This site complements our paper Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic by Lennart Röstel*, Dominik Winkelbauer*, Johannes Pitz,Leon Sievers and Berthold Bäuml.

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.

Consider citing this paper as:

@inproceedings{Roestel_2025,
   title={Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic},
   booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)},
   publisher={IEEE},
   author={Röstel, Lennart and Winkelbauer, Dominik and Pitz, Johannes Sievers, Leon and Bäuml, Berthold},
   year={2025},
   month=may }