TY - GEN
T1 - Online-generation of task-dependent search heuristics to execute learned planning models in Programming by Demonstration
AU - Jakel, Rainer
AU - Xie, Yi
AU - Meissner, Pascal
AU - Dillmann, Rudiger
PY - 2012/12/1
Y1 - 2012/12/1
N2 - A service robot has to be flexible and fast in order to solve a manipulation task in the human environment with differing start configurations, objects, obstacles and a restricted work space. Based on a sophisticated task model, which captures the relevant constraints and goals of a task, constrained motion planning can be used to generate robot trajectories autonomously with high generalization capabilities. The major drawbacks are high planning times and non-repeatability of the results. In this work, search heuristics, which restrict the search space during motion planning, are learned incrementally whenever the robot uses the task model to plan a solution. The number of learned search heuristics is restricted by using a combination of constrained motion planning and a fast local control algorithm to increase the number of situations, in which the search heuristic can be applied. The approach combines two major approaches in Programming by Demonstration (PbD), i.e. learning and goal-directed planning with a general task description and learning efficient encodings of low-level trajectories, in a consistent way.
AB - A service robot has to be flexible and fast in order to solve a manipulation task in the human environment with differing start configurations, objects, obstacles and a restricted work space. Based on a sophisticated task model, which captures the relevant constraints and goals of a task, constrained motion planning can be used to generate robot trajectories autonomously with high generalization capabilities. The major drawbacks are high planning times and non-repeatability of the results. In this work, search heuristics, which restrict the search space during motion planning, are learned incrementally whenever the robot uses the task model to plan a solution. The number of learned search heuristics is restricted by using a combination of constrained motion planning and a fast local control algorithm to increase the number of situations, in which the search heuristic can be applied. The approach combines two major approaches in Programming by Demonstration (PbD), i.e. learning and goal-directed planning with a general task description and learning efficient encodings of low-level trajectories, in a consistent way.
UR - http://www.scopus.com/inward/record.url?scp=84891131990&partnerID=8YFLogxK
U2 - 10.1109/HUMANOIDS.2012.6651525
DO - 10.1109/HUMANOIDS.2012.6651525
M3 - Published conference contribution
AN - SCOPUS:84891131990
SN - 9781467313698
T3 - IEEE-RAS International Conference on Humanoid Robots
SP - 228
EP - 233
BT - 2012 12th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2012
T2 - 2012 12th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2012
Y2 - 29 November 2012 through 1 December 2012
ER -