Dexterous object manipulation remains an open problem in robotics, despite the rapid progress in machine learning during the past decade. We argue that a hindrance is the high cost of experimentation on real systems, in terms of both time and money. We address this problem by proposing an open-source robotic platform which can safely operate without human supervision. The hardware is inexpensive (about \SI{5000}[$]{}) yet highly dynamic, robust, and capable of complex interaction with external objects. The software operates at 1-kilohertz and performs safety checks to prevent the hardware from breaking. The easy-to-use front-end (in C++ and Python) is suitable for real-time control as well as deep reinforcement learning. In addition, the software framework is largely robot-agnostic and can hence be used independently of the hardware proposed herein. Finally, we illustrate the potential of the proposed platform through a number of experiments, including real-time optimal control, deep reinforcement learning from scratch, throwing, and writing.
@article{arxiv.2008.03596,
title = {TriFinger: An Open-Source Robot for Learning Dexterity},
author = {Manuel Wüthrich and Felix Widmaier and Felix Grimminger and Joel Akpo and Shruti Joshi and Vaibhav Agrawal and Bilal Hammoud and Majid Khadiv and Miroslav Bogdanovic and Vincent Berenz and Julian Viereck and Maximilien Naveau and Ludovic Righetti and Bernhard Schölkopf and Stefan Bauer},
journal= {arXiv preprint arXiv:2008.03596},
year = {2021}
}