Learning User-Preferred Mappings for Intuitive Robot Control
- URL: http://arxiv.org/abs/2007.11627v1
- Date: Wed, 22 Jul 2020 18:54:35 GMT
- Title: Learning User-Preferred Mappings for Intuitive Robot Control
- Authors: Mengxi Li, Dylan P. Losey, Jeannette Bohg, and Dorsa Sadigh
- Abstract summary: We propose a method for learning the human's preferred or preconceived mapping from a few robot queries.
We make this approach data-efficient by recognizing that human mappings have strong priors.
Our simulated and experimental results suggest that learning the mapping between inputs and robot actions improves objective and subjective performance.
- Score: 28.183430654834307
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: When humans control drones, cars, and robots, we often have some preconceived
notion of how our inputs should make the system behave. Existing approaches to
teleoperation typically assume a one-size-fits-all approach, where the
designers pre-define a mapping between human inputs and robot actions, and
every user must adapt to this mapping over repeated interactions. Instead, we
propose a personalized method for learning the human's preferred or
preconceived mapping from a few robot queries. Given a robot controller, we
identify an alignment model that transforms the human's inputs so that the
controller's output matches their expectations. We make this approach
data-efficient by recognizing that human mappings have strong priors: we expect
the input space to be proportional, reversable, and consistent. Incorporating
these priors ensures that the robot learns an intuitive mapping from few
examples. We test our learning approach in robot manipulation tasks inspired by
assistive settings, where each user has different personal preferences and
physical capabilities for teleoperating the robot arm. Our simulated and
experimental results suggest that learning the mapping between inputs and robot
actions improves objective and subjective performance when compared to manually
defined alignments or learned alignments without intuitive priors. The
supplementary video showing these user studies can be found at:
https://youtu.be/rKHka0_48-Q.
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