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

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

Research output: Contribution to journalArticlepeer-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 languageEnglish
Pages (from-to)587-594
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume9
Issue number1
DOIs
StatePublished - 1 Jan 2024

Keywords

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

ASJC Scopus subject areas

  • 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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