2024-06-11-naish24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Reinforcement learning-driven parametric curve fitting for snake robot gait design	Snake-inspired robots demonstrate exceptional versatility through challenging terrains such as sand, rubble, and ice. However, their high-dimensional continuous action spaces make analytical gait design challenging. Early works by Hirose (1994) showed that gait parameterization over low-dimensional spatially and temporally varying sine waves can serve as basis functions for the shape-space or central pattern generators (CPGs). Recent approaches to designing CPGs have combined annealed chain-fitting, which solves for joint angles that fit a snake robot to a desired backbone curve, and keyframe extraction, which then fits analytic shape functions to the resulting optimized joint angles. However, the non-convex optimization associated with these methods is fraught with local optima exacerbated by constraints such as actuator limits. Reinforcement Learning has emerged as a promising alternative for searching over such spaces. However, end-to-end RL approaches trained purely in simulation are vulnerable to reality distribution shifts, lack safety guarantees, and don’t yield an intuitive representation of the learned gait. We propose a method that translates a gait found via policy search into a parametric representation of its component sinusoidal equations thus leveraging the strengths of both learning-based and classical approaches. Simulation and hardware experiments show that the proposed pipeline can generate parametric gaits where classical curve fitting-based approaches fail.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	naish24a	0	Reinforcement learning-driven parametric curve fitting for snake robot gait design	1715	1727	1715-1727	1715	false	Naish, Jack and Rodriguez, Jacob and Zhang, Jenny and Jones, Bryson and Daddi, Guglielmo and Orekhov, Andrew and Royce, Rob and Paton, Michael and Choset, Howie and Ono, Masahiro and Thakker, Rohan	given family Jack Naish given family Jacob Rodriguez given family Jenny Zhang given family Bryson Jones given family Guglielmo Daddi given family Andrew Orekhov given family Rob Royce given family Michael Paton given family Howie Choset given family Masahiro Ono given family Rohan Thakker	2024-06-11		Proceedings of the 6th Annual Learning for Dynamics & Control Conference	242	inproceedings	date-parts 2024 6 11	https://proceedings.mlr.press/v242/naish24a/naish24a.pdf