Adaptive Perception Control for Aerial Robots with Twin Delayed DDPG

Robotic perception is commonly assisted by convo-lutional neural networks. However, these networks are static in nature and do not adjust to changes in the environment. Additionally, these are computationally complex and impose latency in inference. We propose an adaptive perception system that changes in response to the robot's requirements. The perception controller has been designed using a recently proposed reinforcement learning technique called Twin Delayed DDPG (TD3). Our proposed method outperformed the baseline approaches.