Dexterous object manipulation is still an open problem in robotics, despite the rapid progress in machine learning during the past decade. We argue that a key issue which has hindered progress is the high cost of experimentation on real systems, in terms of both time and money. We address this problem by proposing a novel open-source robotic platform, consisting of hardware and software, to drastically reduce the cost of experimentation. The hardware is inexpensive yet highly dynamic, robust, and capable of complex contact interaction with external objects. The software allows for 1-kilohertz real-time control and performs safety checks to prevent the hardware from breaking. These properties enable the platform to run without human supervision. In addition, we provide easy-to-use C++ and Python interfaces. We illustrate the potential of the proposed platform by performing an object-manipulation task using an optimal-control algorithm and training a learning-based method directly on the real system.
ASJC Scopus subject areas
- Artificial Intelligence
- Control and Systems Engineering
- Statistics and Probability