Slope Handling for Quadruped Robots Using Deep Reinforcement Learning and Toe Trajectory Planning

摘要

Quadrupedal locomotion skills are challenging to develop. In recent years, deep Reinforcement Learning promises to automate the development of locomotion controllers and map sensory observations to low-level actions. Moreover, the full robot dynamics model can be exploited, but no model-based simplifications are to be made. In this work, a method for developing controllers for the Laelaps II robot is presented and applied to motions on slopes up to 15°. Combining deep reinforcement learning with trajectory planning at the toe level, reduces complexity and training time. The proposed control scheme is extensively tested in a Gazebo environment similar to the treadmill-robot environment at the Control Systems Lab of NTUA. The learned policies produced promising results.