Reinforcement Learning - OpenCat Gym

Hi there,

in the recent years there has been a lot of progress concerning deep reinforcement learning and many publications are available that prove that machine learning can create stable gaits for robots. Mainly interesting and relatively understandable papers are for example https://arxiv.org/abs/1804.10332, where the authors created a walking and also a galloping gait based on training in simulation and a later application on the robot. In a later publication they further advanced and made it possible to learn new gaits via reinforcement directly on the robot without simulation in less than 2 hours https://arxiv.org/abs/1812.11103 There are a lot more examples and different approaches to this.

Nevertheless, this is very remarkable and this made me thinking, whether we can get there also with Nybble and Bittle.

But let's slow down a little. What's reinforcement learning in particular? When you look at the graph below you can get a basic understanding how this works: The Agent, in our case Nybble/Bittle is set in an Environment (e.g. flat floor). There it performs Actions like moving the limbs somehow and trying to get a Reward from the Programmer. The Reward is only given when the State is what we actually want, e.g. moving forward. Trapped in this loop our robot will try to maximize the Reward in every iteration becoming better and better in the movement.

Source: https://en.wikipedia.org/wiki/Reinforcement_learning#/media/File:Reinforcement_learning_diagram.svg

So what I tried to do now, is to use a simulation environment with a flat ground together with a simulation model of Nybble and wanted to make it move forward. I implemented this in the gym training environment in PyBullet with the reinforcement library Stable-Baselines3 https://stable-baselines3.readthedocs.io/en/master/index.html# . There are a lot of functional learning algorithms one can use for reinforcement learning. So for training in my case I tried an algorithm called SAC (Soft Actor-Critic) that seems to be the current state-of-the-art algorithm for reinforcement learning and applied it on Nybble to see how it performs. And the results is definitely still more a crawling than a walking gait, but it shows the potential.

This is a result of reinforcement training only without any intervention from my side:

The next steps are to improve training and the resulting gaits. And once the gaits are good in simulation there are two ways. One is trying to get the learning policy running on Nybble/Bittle or learn it directly on them. I think there I have to use an additional set of hardware to make it run.

If you want to train a walking gait, you can find the link to my repository below, where I will provide further updates. Make sure to install all the necessary python libraries in the import section of the code.

https://github.com/ger01d/opencat-gym