Safe Control of Soft Robots: Bridging Physics and Learned Models: Rising Star Abstract

Maximilian Stölzle

Safe Control of Soft Robots: Bridging Physics and Learned Models: Rising Star Abstract

Maximilian Stölzle

Learning & Autonomous Control

Research output: Contribution to conference › Abstract › Scientific

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Abstract

The contributions presented in this abstract equip soft robots with the motor intelligence they need to function effectively in human-centric environments. We accomplish this by combining advanced machine learning techniques with physical priors and stability guarantees. By incorporating physical structure into learned models, we enable the use of well-established model-based control methods, ensuring effective, stable, and computationally efficient control.

Original language	English
Number of pages	2
Publication status	Accepted/In press - Feb 2025
Event	8th IEEE-RAS International Conference on Soft Robotics (2025) - Lausanne, Switzerland Duration: 23 Apr 2025 → 26 Apr 2025 https://robosoft2025.org/

Conference

Conference	8th IEEE-RAS International Conference on Soft Robotics (2025)
Abbreviated title	RoboSoft 2025
Country/Territory	Switzerland
City	Lausanne
Period	23/04/25 → 26/04/25
Internet address	https://robosoft2025.org/

Bibliographical note

Abstract accepted in the Rising Stars track

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rising_stars_robosoft2025_maximilian_stoelzleAccepted author manuscript, 3.82 MBLicence: CC BY

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abstract = "The contributions presented in this abstract equip soft robots with the motor intelligence they need to function effectively in human-centric environments. We accomplish this by combining advanced machine learning techniques with physical priors and stability guarantees. By incorporating physical structure into learned models, we enable the use of well-established model-based control methods, ensuring effective, stable, and computationally efficient control.",

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PY - 2025/2

Y1 - 2025/2

N2 - The contributions presented in this abstract equip soft robots with the motor intelligence they need to function effectively in human-centric environments. We accomplish this by combining advanced machine learning techniques with physical priors and stability guarantees. By incorporating physical structure into learned models, we enable the use of well-established model-based control methods, ensuring effective, stable, and computationally efficient control.

AB - The contributions presented in this abstract equip soft robots with the motor intelligence they need to function effectively in human-centric environments. We accomplish this by combining advanced machine learning techniques with physical priors and stability guarantees. By incorporating physical structure into learned models, we enable the use of well-established model-based control methods, ensuring effective, stable, and computationally efficient control.

M3 - Abstract

Y2 - 23 April 2025 through 26 April 2025

ER -

Safe Control of Soft Robots: Bridging Physics and Learned Models: Rising Star Abstract

Abstract

Conference

Bibliographical note

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