论文标题

Trifinger:一种用于学习灵巧性的开源机器人

TriFinger: An Open-Source Robot for Learning Dexterity

论文作者

Wüthrich, Manuel, Widmaier, Felix, Grimminger, Felix, Akpo, Joel, Joshi, Shruti, Agrawal, Vaibhav, Hammoud, Bilal, Khadiv, Majid, Bogdanovic, Miroslav, Berenz, Vincent, Viereck, Julian, Naveau, Maximilien, Righetti, Ludovic, Schölkopf, Bernhard, Bauer, Stefan

论文摘要

尽管在过去的十年中,机器学习取得了迅速的进展,但灵巧的物体操纵仍然是一个空旷的问题。我们认为,就时间和金钱而言,障碍是对实际系统的高实验成本。我们通过提出一个开源机器人平台来解决这个问题,该平台可以在没有人类监督的情况下安全地操作。硬件很便宜(大约\ si {5000} [\ $] {})却高度动态,健壮,并且能够与外部对象进行复杂的相互作用。该软件在1 kilohertz运行,并执行安全检查以防止硬件破裂。易于使用的前端(在C ++和Python中)适用于实时控制以及深入的增强学习。此外,软件框架在很大程度上是机器人敏捷的,因此可以独立于本文提出的硬件。最后,我们通过许多实验来说明所提出的平台的潜力,包括实时最佳控制,从头开始,投掷和写作的深度强化学习。

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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