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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: https://arxiv.org/abs/2209.12827 project website: https://sites.google.com/leggedroboti... 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