Machine Learning Best Practices for Soft Robot Proprioception

Annan Zhang*, Tsun Hsuan Wang, Ryan L. Truby, Lillian Chin, Daniela Rus

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Machine learning-based approaches for soft robot proprioception have recently gained popularity, in part due to the difficulties in modeling the relationship between sensor signals and robot shape. However, to date, there exists no systematic analysis of the required design choices to set up a machine learning pipeline for soft robot proprioception. Here, we present the first study examining how design choices on different levels of the machine learning pipeline affect the performance of a neural network for predicting the state of a soft robot. We address the most frequent questions researchers face, such as how to choose the appropriate sensor and actuator signals, process input and output data, deal with time series, and pick the best neural network architecture. By testing our hypotheses on data collected from two vastly different systems-an electrically actuated robotic platform and a pneumatically actuated soft trunk-we seek conclusions that may generalize beyond one specific type of soft robot and hope to provide insights for researchers to use machine learning for soft robot proprioception.

Original languageEnglish (US)
Title of host publication2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2564-2571
Number of pages8
ISBN (Electronic)9781665491907
DOIs
StatePublished - 2023
Event2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023 - Detroit, United States
Duration: Oct 1 2023Oct 5 2023

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
Country/TerritoryUnited States
CityDetroit
Period10/1/2310/5/23

Funding

This work was supported by the National Science Foundation, EFRI grant #1830901, and by the GIST–MIT Research Collaboration grant funded by the Gwangju Institute of Science and Technology in 2023. Correspondence: [email protected]

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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