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» Planning, Execution and Learning in a Robotic Agent
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ROBOCUP
2001
Springer
126views Robotics» more  ROBOCUP 2001»
14 years 1 months ago
Recognizing Probabilistic Opponent Movement Models
In multiagent adversarial domains, team agents should adapt to the environment and opponent. We introduce a model representation as part of a planning process for a simulated socce...
Patrick Riley, Manuela M. Veloso
ATAL
2008
Springer
13 years 10 months ago
Teaching sequential tasks with repetition through demonstration
For robots to become prevalent in human environments, the robots need to be able to perform complex tasks often involving sequential repetition of actions. In this work, we presen...
Harini Veeraraghavan, Manuela M. Veloso
IJCAI
2001
13 years 10 months ago
Executing Reactive, Model-based Programs through Graph-based Temporal Planning
In the future, webs of unmanned air and space vehicles will act together to robustly perform elaborate missions in uncertain environments. We coordinate these systems by introduci...
Phil Kim, Brian C. Williams, Mark Abramson
CORR
2011
Springer
217views Education» more  CORR 2011»
13 years 10 days ago
Interactive Execution Monitoring of Agent Teams
There is an increasing need for automated support for humans monitoring the activity of distributed teams of cooperating agents, both human and machine. We characterize the domain...
Pauline Berry, Thomas J. Lee, David E. Wilkins
AAAI
2010
13 years 9 months ago
Task Space Behavior Learning for Humanoid Robots using Gaussian Mixture Models
In this paper a system was developed for robot behavior acquisition using kinesthetic demonstrations. It enables a humanoid robot to imitate constrained reaching gestures directed...
Kaushik Subramanian