Hi all, I'm in the search for a RL stack that would be suitable to work for both simulated and real robot. Currently, what I had in mind was to use ROS to describe the robot (xacro/urdf), then Mujoco to simulate physics, and OpenAI gym for encapsulating RL algorithms. It seems however, that this stack is only suitable for simulation, but what I would be interested in is a set of tools/libs which are independent of the underlying control mechanism. Meaning, I would like to implement and benchmark RL algorithms, and swap out simulated or real robot as needed, without the changes in algos. I would prefer the RL lib to be in python, but I'm open to all your suggestions. Thanks P.S. If this is the wrong place for the question, please point me to the write direction or move the thread to a dedicated subforum.
I doubt that there is an "out of the box" RL stack that supports various types of hardware and provides the corresponding simulation model (mcjf urdf or whatever). I always find hardware-settings to impose very problem specific constraints on the software side. However just to give you an idea. We in our research group tend to have a very accurately modeled twin of our real hardware and the control interface (e.g. the pd+ controller that produces joint torques from a deviation to a reference joint-position trajectory). Now both mock and real hardware share a common command and measurement interface. Both real hardware and mock "subscribe" to a common actuation message and "publish" their measurements on a respective topic. I hope this helps ...