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Computer Science > Robotics

Title: Generalized Task-Parameterized Movement Primitives

Abstract: Programming by demonstrations has recently gained much attention due to its user-friendly and natural way to transfer human skills to robots. In order to facilitate the learning of multiple demonstrations and meanwhile generalize to new situations, a task-parameterized movement learning has been recently developed [4], which has achieved reliable performance in areas such as human-robot collaboration and robot bimanual operation. However, the crucial task frames and associated task parameters in this learning framework are often set based on human experience, which renders three problems that have not been addressed yet: (i) task frames are treated equally without considering the task priorities; (ii) task parameters are defined without considering additional task constraints, e.g., robot joint limits and motion smoothness; (iii) a fixed number of task frames are pre-defined regardless some of them are redundant or even irrelevant for the task at hand. In this paper, we generalize the task-parameterized learning by addressing the aforementioned problems. Moreover, we provide an alternative way to refine and adapt previously learned robot skills, which allows us to work on a low dimensional space. Several examples are studied in simulated and real robotic systems, showing the applicability of our approach.
Comments: 16 pages
Subjects: Robotics (cs.RO)
Cite as: arXiv:1707.01696 [cs.RO]
  (or arXiv:1707.01696v1 [cs.RO] for this version)

Submission history

From: Yanlong Huang [view email]
[v1] Thu, 6 Jul 2017 09:08:08 GMT (3748kb,D)