Robust Quadrupedal Locomotion on Sloped Terrains
Abstract
In this paper, with a view toward fast deployment of locomotion gaits in low-cost hardware, we use a linear policy for realizing end-foot trajectories in the quadruped robot, Stoch2. In particular, the parameters of the end-foot trajectories are shaped via a linear feedback policy that takes the torso orientation and the terrain slope as inputs. The corresponding desired joint angles are obtained via an inverse kinematics solver, and tracked via a PID control law. Augmented Random Search, a model-free and a gradient-free learning algorithm, is used to train this linear policy. Simulation results show that the resulting walking is robust to external pushes and terrain slope variations. This methodology is not only computationally light-weight, but also uses minimal sensing and actuation capabilities in the robot, thereby justifying the approach.
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Citation
@misc{paigwar2020robust,
title={Robust Quadrupedal Locomotion on Sloped Terrains: A Linear Policy Approach},
author={Kartik Paigwar and Lokesh Krishna and Sashank Tirumala and Naman Khetan and Aditya Sagi and Ashish Joglekar and Shalabh Bhatnagar and Ashitava Ghosal and Bharadwaj Amrutur and Shishir Kolathaya},
year={2020},
eprint={2010.16342},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
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2020 November |
Robust Quadrupedal Locomotion on Sloped Terrains - A Linear Policy Approach
4th Conference on Robot Learning (CoRL 2020), Cambridge MA, USA |
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