Active shape models—their training and application
Computer Vision and Image Understanding
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Using Abstract Models of Behaviours to Automatically Generate Reinforcement Learning Hierarchies
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Coping with the Grasping Uncertainties in Force-closure Analysis
International Journal of Robotics Research
Shape Registration in Implicit Spaces Using Information Theory and Free Form Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Learning Local Objective Functions for Robust Face Model Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and performing place-based mobile manipulation
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
Learning object-specific grasp affordance densities
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
Learning planning rules in noisy stochastic worlds
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Abstract reasoning for planning and coordination
Journal of Artificial Intelligence Research
Refining the execution of abstract actions with learned action models
Journal of Artificial Intelligence Research
Learning partially observable deterministic action models
Journal of Artificial Intelligence Research
Generality and legibility in mobile manipulation
Autonomous Robots
Learning and generalization of motor skills by learning from demonstration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Manipulation planning with workspace goal regions
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
3D model selection from an internet database for robotic vision
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Action-related place-based mobile manipulation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Addressing pose uncertainty in manipulation planning using task space regions
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Learning situation dependent success rates of actions in a RoboCup scenario
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
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We propose the concept of Action-Related Place (ARPLACE) as a powerful and flexible representation of task-related place in the context of mobile manipulation. ARPlace represents robot base locations not as a single position, but rather as a collection of positions, each with an associated probability that the manipulation action will succeed when located there. ARPLACES are generated using a predictive model that is acquired through experience-based learning, and take into account the uncertainty the robot has about its own location and the location of the object to be manipulated. When executing the task, rather than choosing one specific goal position based only on the initial knowledge about the task context, the robot instantiates an ARPLACE, and bases its decisions on this ARPLACE, which is updated as new information about the task becomes available. To show the advantages of this least-commitment approach, we present a transformational planner that reasons about ARPLACE in order to optimize symbolic plans. Our empirical evaluation demonstrates that using ARPLACE leads to more robust and efficient mobile manipulation in the face of state estimation uncertainty on our simulated robot.