The overall objective of this project is to improve flexibility and robustness of Model Base Reinforcement Learning agents. Not only do we train and evaluate models in simulation, but we also deploy and evaluate them on the field. We have chosen a challenging robot, a Clearpath Kingfisher (previous version of the HERON). On Unmanned Surface Vessels, the dynamics is really challenging, making any navigation task even more difficult.
The following work has already been published:
How To Train Your HERON:
In this paper we apply Deep Reinforcement Learning (Deep RL) and Domain Randomization to solve a navigation task in a natural environment relying solely on a 2D laser scanner. We train a model-based RL agent in simulation to follow lake and river shores and apply it on a real Unmanned Surface Vehicle in a zero-shot setup. We demonstrate that even though the agent has not been trained in the real world, it can fulfill its task successfully and adapt to changes in the robot’s environment and dynamics. Finally, we show that the RL agent is more robust, faster, and more accurate than a state-aware Model-Predictive-Controller.
- Richard, A., Aravecchia, S., Schillaci, T., Geist, M., & Pradalier, C. (2021). How To Train Your HERON. IEEE Robotics and Automation Letters, 6(3), 5247-5252.