Know your limits! Optimize the robot's behavior through self-awareness

Esteve Valls Mascaró, Dongheui Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As humanoid robots transition from labs to realworld environments, it is essential to democratize robot control for non-expert users. Recent human-robot imitation algorithms focus on following a reference human motion with high precision, but they are susceptible to the quality of the reference motion and require the human operator to simplify its movements to match the robot's capabilities. Instead, we consider that the robot should understand and adapt the reference motion to its own abilities, facilitating the operator's task. For that, we introduce a deep-learning model that anticipates the robot's performance when imitating a given reference. Then, our system can generate multiple references given a highlevel task command, assign a score to each of them, and select the best reference to achieve the desired robot behavior. Our Self-AWare model (SAW) ranks potential robot behaviors based on various criteria, such as fall likelihood, adherence to the reference motion, and smoothness. We integrate advanced motion generation, robot control, and SAW in one unique system, ensuring optimal robot behavior for any task command. For instance, SAW can anticipate falls with 99.29% accuracy.

Original languageEnglish
Title of host publication2024 IEEE-RAS 23rd International Conference on Humanoid Robots, Humanoids 2024
PublisherIEEE Computer Society
Pages258-265
Number of pages8
ISBN (Electronic)9798350373578
DOIs
StatePublished - 2024
Externally publishedYes
Event23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024 - Nancy, France
Duration: 22 Nov 202424 Nov 2024

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Conference

Conference23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024
Country/TerritoryFrance
CityNancy
Period22/11/2424/11/24

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