Towards Robot Task Planning From Probabilistic Models of Human Skills
release_gjjv4z6ybjg2dcknt3bqcyzkxq
by
Chris Paxton, Marin Kobilarov, Gregory D. Hager
2016
Abstract
We describe an algorithm for motion planning based on expert demonstrations
of a skill. In order to teach robots to perform complex object manipulation
tasks that can generalize robustly to new environments, we must (1) learn a
representation of the effects of a task and (2) find an optimal trajectory that
will reproduce these effects in a new environment. We represent robot skills in
terms of a probability distribution over features learned from multiple expert
demonstrations. When utilizing a skill in a new environment, we compute feature
expectations over trajectory samples in order to stochastically optimize the
likelihood of a trajectory in the new environment. The purpose of this method
is to enable execution of complex tasks based on a library of probabilistic
skill models. Motions can be combined to accomplish complex tasks in hybrid
domains. Our approach is validated in a variety of case studies, including an
Android game, simulated assembly task, and real robot experiment with a UR5.
In text/plain
format
Archived Files and Locations
application/pdf 1.9 MB
file_7or2u4eyzjgm3izpegdpquzkwe
|
arxiv.org (repository) web.archive.org (webarchive) |
1602.04754v1
access all versions, variants, and formats of this works (eg, pre-prints)