Publication

Zero-shot transfer of a tactile-based continuous force control policy from simulation to robot

Conference Article

Conference

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Edition

2024

Pages

725-732

Doc link

http://dx.doi.org/10.1109/IROS58592.2024.10802386

File

Download the digital copy of the doc pdf document

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

Categories

intelligent robots.

Author keywords

Tactile sensors, Force control, Sim-to-real

Scientific reference

L.M. Lach, R. Haschke, D. Tateo, J. Peters, H. Ritter, J. Borràs and C. Torras. Zero-shot transfer of a tactile-based continuous force control policy from simulation to robot, 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024, Abu Dhabi, UAE, pp. 725-732.