Advanced Skills by Learning Locomotion and Local Navigation End-to-End

Local navigation and locomotion of legged robots are commonly split into separate modules. In this work, we propose to combine them by training an end-to-end policy with deep reinforcement learning. Training a policy in this way opens up a larger set of possible solutions, which allows the robot to learn more complex behaviors. IROS 2022 work by Nikita Rudin, David Hoeller, Marko Bjelonic, and Marco Hutter paper: project website: Credits for outdoor video: “Learning robust perceptive locomotion for quadrupedal robots in the wild“ by Takahiro Miki, Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, Marco Hutter