Robust Quadrupedal Locomotion on Sloped Terrains: A Linear Policy Approach

Paigwar, K and Krishna, L and Tirumala, S and Khetan, N and Sagi, A and Joglekar, A and Bhatnagar, S and Ghosal, A and Amrutur, B and Kolathaya, S (2020) Robust Quadrupedal Locomotion on Sloped Terrains: A Linear Policy Approach. In: UNSPECIFIED, pp. 2257-2267.

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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, Stoch 2. 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 terrain slope variations and external pushes. This methodology is not only computationally light-weight but also uses minimal sensing and actuation capabilities in the robot, thereby justifying the approach. © 2020 Proceedings of Machine Learning Research. All rights reserved.

Item Type:	Conference Paper
Publication:	Proceedings of Machine Learning Research
Publisher:	ML Research Press
Additional Information:	The copyright for this article belongs to ML Research Press.
Keywords:	Inverse kinematics; Inverse problems; Learning algorithms; Three term control systems, Fast deployments; Locomotion gaits; Low cost hardware; Policy approach; Quadruped Robots; Quadrupedal walking; Random searches; Reinforcement learnings; Sloped terrains; Terrain slope, Reinforcement learning
Department/Centre:	Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems
Date Deposited:	04 Mar 2024 09:53
Last Modified:	04 Mar 2024 09:53
URI:	https://eprints.iisc.ac.in/id/eprint/84416

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