  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.
