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
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
Generality and legibility in mobile manipulation
Autonomous Robots
3D model selection from an internet database for robotic vision
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Learning situation dependent success rates of actions in a RoboCup scenario
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Automatically composing and parameterizing skills by evolving Finite State Automata
Robotics and Autonomous Systems
Learning and reasoning with action-related places for robust mobile manipulation
Journal of Artificial Intelligence Research
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In mobile manipulation, the position to which the robot navigates has a large influence on the ease with which a subsequent manipulation action can be performed. Whether a manipulation action succeeds depends on many factors, such as the robot's hardware configuration, the controllers the robot uses to achieve navigation and manipulation, the task context, and uncertainties in state estimation. In this paper, we present 'ARPLACE', an action-related place which takes these factors, and the context in which the actions are performed into account. Through experience-based learning, the robot first learns a so-called generalized success model, which discerns between positions from which manipulation succeeds or fails. On-line, this model is used to compute a ARPLACE, a probability distribution that maps positions to a predicted probability of successful manipulation, and takes the uncertainty in the robot and object's position into account. In an empirical evaluation, we demonstrate that using ARPLACEs for least-commitment navigation improves the success rate of subsequent manipulation tasks substantially.