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
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.