2021
DOI: 10.48550/arxiv.2104.01662
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Learning Linear Policies for Robust Bipedal Locomotion on Terrains with Varying Slopes

Abstract: In this paper, with a view toward deployment of light-weight control frameworks for bipedal walking robots, we realize end-foot trajectories that are shaped by a single linear feedback policy. We learn this policy via a model-free and a gradient free learning algorithm, Augmented Random Search (ARS), in the two robot platforms Rabbit and Digit. Our contributions are two-fold: a) By using torso and support plane orientation as inputs, we achieve robust walking on slopes of upto 20 • in simulation. b) We demonst… Show more

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