TriFinger: An Open-Source Robot for Learning Dexterity
This provides a low-cost, accessible tool for robotics researchers to advance dexterous manipulation, though it is incremental as it builds on existing hardware and software concepts.
The authors tackled the high cost of real-world experimentation in dexterous object manipulation by developing TriFinger, an open-source robotic platform that is inexpensive (about $5000), robust, and capable of complex interactions, enabling experiments like real-time optimal control and deep reinforcement learning.
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.