Representation and constrained planning of manipulation strategies in the context of Programming by Demonstration
ICRA(2010)
摘要
In Programming by Demonstration, a flexible representation of manipulation motions is necessary to learn and generalize from human demonstrations. In contrast to subsymbolic representations of trajectories, e.g. based on a Gaussian Mixture Model, a partially symbolic representation of manipulation strategies based on a temporal satisfaction problem with domain constraints is developed. By using constrained motion planning and a geometric constraint representation, generalization to different robot systems and new environments is achieved. In order to plan learned manipulation strategies the RRT-based algorithm by Stilman et al. is extended to consider, that multiple sets of constraints are possible during the extension of the search tree.
更多查看译文
关键词
strategic planning,search tree,path planning,motion planning,optimization,gaussian mixture model,planning,programming,gaussian processes,trajectory
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络