Communications of the ACM
Planning for conjunctive goals
Artificial Intelligence
SPAR: a planner that satisfies operational and geometric goals in uncertain environments
AI Magazine - Special issue on robotic assembly and task planning
Finding new rules for incomplete theories: explicit biases for induction with contextual information
Proceedings of the sixth international workshop on Machine learning
A General Framework for Induction and a Study of Selective Induction
Machine Learning
A Geometric Approach to Error Detection and Recovery for Robot Motion Planning With Uncertainty
A Geometric Approach to Error Detection and Recovery for Robot Motion Planning With Uncertainty
Motion Planning with Six Degrees of Freedom
Motion Planning with Six Degrees of Freedom
On Motion Planning with Uncertainty
On Motion Planning with Uncertainty
An Integrated Approach of Learning, Planning, and Execution
Journal of Intelligent and Robotic Systems
Learning by knowledge sharing in autonomous intelligent systems
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
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Our system, GINKO, is fully implemented and integrates aspects of Learning, Planning, Execution, Perception, and Robotics In short, from Robotics we take our domains and we borrow the notion of planning in configuration space. We use Machine Learning techniques to classify regions of configuration space according to their qualitative behaviors. This corresponds to learning the conditional effects of robot operators. Our planner generates an abstract plan of transitions between regions of qualitative behavior in the configuration space. A concrete plan is generated by augmenting the abstract plan with execution monitors that sense critical aspects of the situations to ensure that a particular plan is being followed and that the regions from which the plan was derived are accurately characterized.