Collaborative interface agents
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A framework for recognizing multi-agent action from visual evidence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
It knows what you're going to do: adding anticipation to a Quakebot
Proceedings of the fifth international conference on Autonomous agents
Extreme work teams: using SWAT teams as a model for coordinating distributed robots
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Theory of Mind for a Humanoid Robot
Autonomous Robots
Techniques for Plan Recognition
User Modeling and User-Adapted Interaction
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Imitation in animals and artifacts
Natural methods for robot task learning: instructive demonstrations, generalization and practice
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
The Development of Gaze Following as a Bayesian Systems Identification Problem
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
A Developmental Approach Accelerates Learning of Joint Attention
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Protocols from perceptual observations
Artificial Intelligence - Special volume on connecting language to the world
Children and robots learning to play hide and seek
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Untethered robotic play for repetitive physical tasks
Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology
The affordance-based concept
Perspective taking: an organizing principle for learning in human-robot interaction
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Discriminating animate from inanimate visual stimuli
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Enabling effective human-robot interaction using perspective-taking in robots
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A modular design of Bayesian networks using expert knowledge: Context-aware home service robot
Expert Systems with Applications: An International Journal
Accommodating human variability in human-robot teams through theory of mind
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Manipulating mental states through physical action
ICSR'12 Proceedings of the 4th international conference on Social Robotics
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Future applications for personal robots motivate research into developing robots that are intelligent in their interactions with people. Toward this goal, in this paper we present an integrated socio-cognitive architecture to endow an anthropomorphic robot with the ability to infer mental states such as beliefs, intents, and desires from the observable behavior of its human partner. The design of our architecture is informed by recent findings from neuroscience and embodies cognition that reveals how living systems leverage their physical and cognitive embodiment through simulation-theoretic mechanisms to infer the mental states of others. We assess the robot's mindreading skills on a suite of benchmark tasks where the robot interacts with a human partner in a cooperative scenario and a learning scenario. In addition, we have conducted human subjects experiments using the same task scenarios to assess human performance on these tasks and to compare the robot's performance with that of people. In the process, our human subject studies also reveal some interesting insights into human behavior.