Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning
This work addresses the challenge of smooth robot control for impaired individuals or search and rescue, but it is incremental as it builds on existing DDPG methods without reporting concrete performance numbers.
The paper tackled the problem of controlling flippers on multi-terrain robots using deep reinforcement learning, specifically DDPG, to enable continuous position control in partially observable environments, with potential applications in assistive and rescue scenarios.
In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach. The work is based on the deep deterministic policy gradient (DDPG) algorithm, proven to be very successful in simple simulation environments. The algorithm works in an end-to-end fashion in order to control the continuous position of the flippers. This end-to-end approach makes it easy to apply the controller to a wide array of circumstances, but the huge flexibility comes to the cost of an increased difficulty of solution. The complexity of the task is enlarged even more by the fact that real multi-terrain robots move in partially observable environments. Notwithstanding these complications, being able to smoothly control a multi-terrain robot can produce huge benefits in impaired people daily lives or in search and rescue situations.