2023-12-02-kasaei23a.md

title	section	openreview	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
A Data-efficient Neural ODE Framework for Optimal Control of Soft Manipulators	Poster	PalhNjBJqv	This paper introduces a novel approach for modeling continuous forward kinematic models of soft continuum robots by employing Augmented Neural ODE (ANODE), a cutting-edge family of deep neural network models. To the best of our knowledge, this is the first application of ANODE in modeling soft continuum robots. This formulation introduces auxiliary dimensions, allowing the system’s states to evolve in the augmented space which provides a richer set of dynamics that the model can learn, increasing the flexibility and accuracy of the model. Our methodology achieves exceptional sample efficiency, training the continuous forward kinematic model using only 25 scattered data points. Additionally, we design and implement a fully parallel Model Predictive Path Integral (MPPI)-based controller running on a GPU, which efficiently manages a non-convex objective function. Through a set of experiments, we showed that the proposed framework (ANODE+MPPI) significantly outperforms state-of-the-art learning-based methods such as FNN and RNN in unseen-before scenarios and marginally outperforms them in seen-before scenarios.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	kasaei23a	0	A Data-efficient Neural ODE Framework for Optimal Control of Soft Manipulators	2700	2713	2700-2713	2700	false	Kasaei, Mohammadreza and Babarahmati, Keyhan Kouhkiloui and Li, Zhibin and Khadem, Mohsen	given family Mohammadreza Kasaei given family Keyhan Kouhkiloui Babarahmati given family Zhibin Li given family Mohsen Khadem	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/kasaei23a/kasaei23a.pdf