Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments

2018/04/27 by Chen, Xi, Ghadirzadeh, Ali, Folkesson, John +1
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Robotics (cs.RO)

paper · doi:10.48550/arxiv.1804.10500

Abstract

Mobile robot navigation in complex and dynamic environments is a challenging but important problem. Reinforcement learning approaches fail to solve these tasks efficiently due to reward sparsities, temporal complexities and high-dimensionality of sensorimotor spaces which are inherent in such problems. We present a novel approach to train action policies to acquire navigation skills for wheel-legged robots using deep reinforcement learning. The policy maps height-map image observations to motor commands to navigate to a target position while avoiding obstacles. We propose to acquire the multifaceted navigation skill by learning and exploiting a number of manageable navigation behaviors. We also introduce a domain randomization technique to improve the versatility of the training samples. We demonstrate experimentally a significant improvement in terms of data-efficiency, success rate, robustness against irrelevant sensory data, and also the quality of the maneuver skills.

Related

• Advanced Skills by Learning Locomotion and Local Navigation End-to-End
• Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning
• Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning
• Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments
• PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning