Understanding human intentions via hidden markov models in autonomous mobile robots

Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant for situations that involve collaboration among agents or detection of situations that can pose a threat. In this paper, we...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI) S. 367 - 374
Hauptverfasser: Kelley, Richard, Tavakkoli, Alireza, King, Christopher, Nicolescu, Monica, Nicolescu, Mircea, Bebis, George
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 12.03.2008
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
Computational modeling
Computer systems organization > Embedded and cyber-physical systems > Robotics > External interfaces for robotics
Computer systems organization > Embedded and cyber-physical systems > Robotics > Robotic autonomy
Computer systems organization > Embedded and cyber-physical systems > Sensors and actuators
Hidden Markov models
Human-robot interaction
Humans
intention modeling
Robot kinematics
Robot sensing systems
Theory of Mind
Training
vision-based methods
intention modeling
human-robot interaction
hidden markov models
theory of mind
vision-based methods
ISBN:1605580171, 9781605580173
ISSN:2167-2121
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant for situations that involve collaboration among agents or detection of situations that can pose a threat. In this paper, we propose an approach that allows a robot to detect intentions of others based on experience acquired through its own sensory-motor capabilities, then using this experience while taking the perspective of the agent whose intent should be recognized. Our method uses a novel formulation of Hidden Markov Models designed to model a robot's experience and interaction with the world. The robot's capability to observe and analyze the current scene employs a novel vision-based technique for target detection and tracking, using a non-parametric recursive modeling approach. We validate this architecture with a physically embedded robot, detecting the intent of several people performing various activities.
ISBN:1605580171
9781605580173
ISSN:2167-2121
DOI:10.1145/1349822.1349870