TriFinger: An Open-Source Robot for Learning Dexterity
By Manuel Wüthrich, Felix Widmaier, F. Grimminger, J. Akpo, S. Joshi, Vaibhav Agrawal, Bilal Hammoud, M. Khadiv, Miroslav Bogdanovic, Vincent Berenz and 5 more. 2020
82 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Stefan Bauer
- Francesco Locatello
- Bernhard Schölkopf
- Frederik Trauble
- Ole Winther
- M. Wuthrich
- N. Rudin
- David Hoeller
In Control and Systems Engineering
- Stefan Bauer
- Bernhard Schölkopf
- Ole Winther
- N. Rudin
- Ankur Handa
- Miles Macklin
- David Hoeller
- Viktor Makoviychuk
Citations in the corpus
- On the Transfer of Disentangled Representations in Realistic Settings (2020, methodology): “In fact, our dataset is based on the TriFinger robot from W¨uthrich et al. (2020), which can be built to test the deployment of models in the real world.”
- Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning (2021, methodology): “Secondly, the TriFinger robot [32], which shows the ability to do 6-DoF manipulation by reposing the cube to a desired position and orientation, a task which has previously shown to be challenging for model-free reinforcement learning [4].”