Tuning Legged Locomotion Controllers via Safe Bayesian OptimizationDownload PDF

Published: 30 Aug 2023, Last Modified: 03 Jul 2024CoRL 2023 PosterReaders: Everyone
Keywords: Legged Robotics, Bayesian Optimization, Controller Tuning, Locomotion, Machine Learning, Safe Learning
TL;DR: A safe learning approach to tune legged locomotion controllers via safe bayesian optimization to address the mismatch between the simplified model used in the control formulation and the real system.
Abstract: This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms. Our approach leverages a model-free safe learning algorithm to automate the tuning of control gains, addressing the mismatch between the simplified model used in the control formulation and the real system. This method substantially mitigates the risk of hazardous interactions with the robot by sample-efficiently optimizing parameters within a probably safe region. Additionally, we extend the applicability of our approach to incorporate the different gait parameters as contexts, leading to a safe, sample-efficient exploration algorithm capable of tuning a motion controller for diverse gait patterns. We validate our method through simulation and hardware experiments, where we demonstrate that the algorithm obtains superior performance on tuning a model-based motion controller for multiple gaits safely.
Student First Author: yes
Supplementary Material: zip
Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)
Video: https://youtu.be/zDBouUgegrU
Website: https://donghok.me/tuning-legged-locomotion-controllers/
Code: https://github.com/lasgroup/gosafeopt
Publication Agreement: pdf
Poster Spotlight Video: mp4
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/tuning-legged-locomotion-controllers-via-safe/code)
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