POMDP Based Action Planning and Human Error Detection

Abstract

Part 4: Smart Environments, Agents, and RobotsInternational audienceThis paper presents a Partially Observable Markov Decision Process (POMDP) model for action planning and human errors detection, during Activities of Daily Living (ADLs). This model is integrated into a sub-component of an assistive system designed for stroke survivors; it is called the Artificial Intelligent Planning System (AIPS). Its main goal is to monitor the user’s history of actions during a specific task, and to provide meaningful assistance when an error is detected in his/her sequence of actions. To do so, the AIPS must cope with the ambiguity in the outputs of the other system’s components. In this paper, we first give an overview of the global assistive system where the AIPS is implemented, and explain how it interacts with the user to guide him/her during tea-making. We then define the POMDP models and the Monte Carlo Algorithm used to learn how to retrieve optimal prompts, and detect human errors under uncertainty

Intelligent prompting system to assist stroke survivors

“…In such a case, one solution is to implement a Partially Observable MDP [54], which will model these uncertainties, and enable the assistive system to act even if the user's state is only partially known from its point of view. Such a system has already been implemented in CogWatch [55,49,56], and was tested with success via simulation. The next step is to let stroke survivors interact with the POMDP-based assistive system and evaluate its performance under uncertainty.…”

Shared Autonomy Systems with Stochastic Operator Models

“…The autonomous systems that attempt to create a realistic model of the humans in the environment must consider the probabilities associated with humans making mistakes [Wu et al, 2017;Jean-Baptiste et al, 2015;Charles et al, 2018]. These systems use Markov chains (MCs) to model the stochastic behaviour of humans, such as their likelihood to miss a piece of litter in a picking exercise [Junges et al, 2018].…”

Designing Smart Objects to Support Affording Situations: Exploiting Affordance Through an Understanding of Forms of Engagement

“…Having some knowledge of where the object is being used could also influence the definition of appropriate actions, through Cultural Engagement. Combining inferences drawn from Motor and Morphological Engagement, the object could infer the most likely intention of the person, and use this inference to provide additional cues and guidance (Jean-Baptiste et al, 2015 ).…”