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

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...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 9; no. 1; pp. 587 - 594
Main Authors: Wertheim, Or, Suissa, Dan R., Brafman, Ronen I.
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
AI-enabled robotics
Artificial intelligence
Autonomous agents
Autonomy
Centralized control
Codes
integrated planning and control
Modular design
Modular systems
Motion control
Noise measurement
Planning
planning under uncertainty
Plugs
Robot control
Robot sensing systems
Robotics
Sensors
software architecture for robotic and automation
Systems design
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Description
Summary: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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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2023.3334682