Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Probabilistic Programs

Or Wertheim; Dan R. Suissa; Ronen I. Brafman

doi:10.1109/LRA.2023.3334682

Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Probabilistic Programs

Or Wertheim, Dan R. Suissa, Ronen I. Brafman

Department of Computer Science

Ben-Gurion University of the Negev

Research output: Contribution to journal › Article › peer-review

Abstract

We describe AOS, the first general-purpose system for model-based control of autonomous robots using AI planning that fully supports partial observability and noisy sensing. The AOS provides a code-based language for specifying a generative model of the system, making model specification easier and model sampling efficient. It provides a language for specifying the relation between the model and the code, using which it auto-generates all required integration code. This allows Plug'n Play behavior, which facilitates incremental and modular system design. Extensive experiments on real and simulated robotic platforms demonstrate these advantages.

Original language	American English
Pages (from-to)	587-594
Number of pages	8
Journal	IEEE Robotics and Automation Letters
Volume	9
Issue number	1
DOIs	https://doi.org/10.1109/LRA.2023.3334682
State	Published - 1 Jan 2024

Keywords

AI-enabled robotics
autonomous agents
integrated planning and control
planning under uncertainty
software architecture for robotic and automation

All Science Journal Classification (ASJC) codes

Mechanical Engineering
Control and Optimization
Artificial Intelligence
Human-Computer Interaction
Control and Systems Engineering
Computer Vision and Pattern Recognition
Biomedical Engineering
Computer Science Applications

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10.1109/LRA.2023.3334682

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abstract = "We describe AOS, the first general-purpose system for model-based control of autonomous robots using AI planning that fully supports partial observability and noisy sensing. The AOS provides a code-based language for specifying a generative model of the system, making model specification easier and model sampling efficient. It provides a language for specifying the relation between the model and the code, using which it auto-generates all required integration code. This allows Plug'n Play behavior, which facilitates incremental and modular system design. Extensive experiments on real and simulated robotic platforms demonstrate these advantages.",

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Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Probabilistic Programs

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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