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

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

RA-L 20244 citations

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

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

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.

BibTeX

@inproceedings{ral2024_plugnplaytasklev,
  title = {Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Probabilistic Programs},
  author = {Or Wertheim and Dan R. Suissa and Ronen I. Brafman},
  booktitle = {RA-L 2024},
  year = {2024}
}