Learning and Control of Active Perception Policy in Autonomous Robots

Abstract

We consider how to control behaviors of the sensor’s perceptual policy during the training in our earlier work that simultaneously learns the robot’s movement policy and the sensor’s perceptual policy. In our algorithm, we simultaneously train two agents which control the robot and its sensor on the robot to achieve a task. Since the exploration space changes greatly depending on the action of the sensor, we aim to achieve efficient exploration behavior by introducing a new parameter to control the sensor action in a training process. We conducted experiments on navigation tasks in a 2D and a 3D environment. The experimental results show that our algorithm can achieve higher success rates than conventional reinforcement learning algorithms and we found that the introduced parameters worked effectively in controlling the behavior of the sensor.