TriFinger: An Open-Source Robot for Learning Dexterity

Abstract

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.

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Cited by in corpus (7)

• CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
• Open Continuum Robotics -- One Actuation Module to Create them All
• On the Transfer of Disentangled Representations in Realistic Settings
• Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge
• Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation
• Dexterous Manipulation Primitives for the Real Robot Challenge
• Solving the Real Robot Challenge using Deep Reinforcement Learning