Abstract
Reinforcement Learning (RL) agents trained in simulated environments and then deployed in the real world are often sensitive to the differences in dynamics presented, commonly termed the sim-to-real gap. With the goal of minimizing this gap on resource-constrained embedded systems, we train and live-adapt agents on quadrotors built from off-the-shelf hardware. In achieving this we developed three novel contributions. (i) SwaNNFlight, an open-source firmware enabling wireless data capture and transfer of agents' observations. Fine-tuning agents with new data, and receiving and swapping onboard NN controllers -- all while in flight. We also design SwaNNFlight System (SwaNNFS) allowing new research in training and live-adapting learning agents on similar systems. (ii) Multiplicative value composition, a technique for preserving the importance of each policy optimization criterion, improving training performance and variability in learnt behavior. And (iii) anchor critics to help stabilize the fine-tuning of agents during sim-to-real transfer, online learning from real data while retaining behavior optimized in simulation. We train consistently flight-worthy control policies in simulation and deploy them on real quadrotors. We then achieve live controller adaptation via over-the-air updates of the onboard control policy from a ground station. Our results indicate that live adaptation unlocks a near-50\% reduction in power consumption, attributed to the sim-to-real gap. Finally, we tackle the issues of catastrophic forgetting and controller instability, showing the effectiveness of our novel methods. Project Website: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2301.06987
