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.
@article{arxiv.2505.13253,
title = {Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic},
author = {Lennart Röstel and Dominik Winkelbauer and Johannes Pitz and Leon Sievers and Berthold Bäuml},
journal= {arXiv preprint arXiv:2505.13253},
year = {2025}
}