Verifiable Learned Behaviors via Motion Primitive Composition: Applications to Scooping of Granular Media

A robotic behavior model that can reliably generate behaviors from natural language inputs in real time would substantially expedite the adoption of industrial robots due to enhanced system flexibility. To facilitate these efforts, we construct a framework in which learned behaviors, created by a na...

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Bibliographic Details
Published in2024 IEEE International Conference on Robotics and Automation (ICRA) pp. 2549 - 2555
Main Authors Benton, Andrew, Solowjow, Eugen, Akella, Prithvi
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.05.2024
Subjects
Directed graphs
Industrial robots
Manipulators
Media
Natural languages
Probabilistic logic
Service robots
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Summary:A robotic behavior model that can reliably generate behaviors from natural language inputs in real time would substantially expedite the adoption of industrial robots due to enhanced system flexibility. To facilitate these efforts, we construct a framework in which learned behaviors, created by a natural language abstractor, are verifiable by construction. Leveraging recent advancements in motion primitives and probabilistic verification, we construct a natural-language behavior abstractor that generates behaviors by synthesizing a directed graph over the provided motion primitives. If these component motion primitives are constructed according to the criteria we specify, the resulting behaviors are probabilistically verifiable. We demonstrate this verifiable behavior generation capacity in both simulation on an exploration task and on hardware with a robot scooping granular media.
DOI:10.1109/ICRA57147.2024.10611279