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Title: Do what i want, not what i did: Imitation of skills by planning sequences of actions
We propose a learning‐from‐demonstration approach for grounding actions from expert data and an algorithm for using these actions to perform a task in new environments. Our approach is based on an application of sampling‐based motion planning to search through the tree of discrete, high‐level actions constructed from a symbolic representation of a task. Recursive sampling‐based planning is used to explore the space of possible continuous‐space instantiations of these actions. We demonstrate the utility of our approach with a magnetic structure assembly task, showing that the robot can intelligently select a sequence of actions in different parts of the workspace and in the presence of obstacles. This approach can better adapt to new environments by selecting the correct high‐level actions for the particular environment while taking human preferences into account.  more » « less
Award ID(s):
1637949
NSF-PAR ID:
10047466
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
IROS
Page Range / eLocation ID:
3778 to 3785
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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