A-EMS: An Adaptive Emergency Management System for Autonomous Agents in Unforeseen Situations

Autor: Glenn Maguire, Nicholas Ketz, Praveen K. Pilly, Jean-Baptiste Mouret
Přispěvatelé: Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Hughes Research Laboratories (HRL), Hughes Aircraft Company, DARPA L2M STELLAR
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Lecture Notes in Computer Science
TAROS 2022-Towards Autonomous Robotic Systems
TAROS 2022-Towards Autonomous Robotic Systems, 2022, Abingdon, United Kingdom. pp.266-281, ⟨10.1007/978-3-031-15908-4_21⟩
Towards Autonomous Robotic Systems ISBN: 9783031159077
Popis: International audience; Reinforcement learning agents are unable to respond effectively when faced with novel, out-of-distribution events until they have undergone a significant period of additional training. For lifelong learning agents, which cannot be simply taken offline during this period, suboptimal actions may be taken that can result in unacceptable outcomes. This paper presents the Autonomous Emergency Management System (A-EMS)-an online, data-driven, emergency-response method that aims to provide autonomous agents the ability to react to unexpected situations that are very different from those it has been trained or designed to address. The proposed approach devises a customized response to the unforeseen situation sequentially, by selecting actions that minimize the rate of increase of the reconstruction error from a variational autoencoder. This optimization is achieved online in a data-efficient manner (on the order of 30 to 80 data-points) using a modified Bayesian optimization procedure. The potential of A-EMS is demonstrated through emergency situations devised in a simulated 3D car-driving application.
Databáze: OpenAIRE
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