On Training Flexible Robots using Deep Reinforcement Learning
This addresses the problem of controlling flexible robots in uncertain real-world environments for robotics applications, though it is incremental as it applies existing DRL methods to this domain.
The paper tackled training flexible robots for complex tasks using deep reinforcement learning (DRL), finding that DRL can learn efficient and robust policies across varying flexibility levels, but noted sensitivity to sensor choices.
The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical models of their hardware have proven very effective. However, in many real-world settings, the uncertainties of the environment, the safety requirements and generalized capabilities that are expected of robots make rigid industrial robots unsuitable. This created great research interest into developing control strategies for flexible robot hardware for which building dynamical models are challenging. In this paper, inspired by the success of deep reinforcement learning (DRL) in other areas, we systematically study the efficacy of policy search methods using DRL in training flexible robots. Our results indicate that DRL is successfully able to learn efficient and robust policies for complex tasks at various degrees of flexibility. We also note that DRL using Deep Deterministic Policy Gradients can be sensitive to the choice of sensors and adding more informative sensors does not necessarily make the task easier to learn.