Publication
Zero-Shot Transfer of a Tactile-based Continuous Force Control Policy from Simulation to Robot
Luca Lach; Robert Haschke; Davide Tateo; Jan Peters; Helge J. Ritter; Júlia Borràs Sol; Carme Torras
In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2024, Abu Dhabi, United Arab Emirates, October 14-18, 2024. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Pages 725-732, IEEE, 2024.
Abstract
The advent of tactile sensors in robotics has
sparked many ideas on how robots can leverage direct contact
measurements of their environment interactions to improve
manipulation tasks. An important line of research in this regard
is grasp force control, which aims to manipulate objects safely
by limiting the amount of force exerted on the object. While
prior works have either hand-modeled their force controllers,
employed model-based approaches, or not shown sim-to-real
transfer, we propose a model-free deep reinforcement learning
approach trained in simulation and then transferred to the
robot without further fine-tuning. We, therefore, present a
simulation environment that produces realistic normal forces,
which we use to train continuous force control policies. A
detailed evaluation shows that the learned policy performs
similarly or better than a hand-crafted baseline. Ablation
studies prove that the proposed inductive bias and domain
randomization facilitate sim-to-real transfer. Code, models,
and supplementary videos are available on https://sites.
google.com/view/rl-force-ctrl