Paper Info
Title
Learning preferences for manipulation tasks from online coactive feedback
Abstract
AbstractWe consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than simple geometric constraints on trajectories; they are rather governed by the surrounding context of various objects and human interactions in the environment. We propose a coactive online learning framework for teaching preferences in contextually rich environments. The key novelty of our approach lies in the type of feedback expected from the user: the human user does not need to demonstrate optimal trajectories as training data, but merely needs to iteratively provide trajectories that slightly improve over the trajectory currently proposed by the system. We argue that this coactive preference feedback can be more easily elicited than demonstrations of optimal trajectories. Nevertheless, theoretical regret bounds of our algorithm match the asymptotic rates of optimal trajectory algorithms. We implement our algorithm on two high-degree-of-freedom robots, PR2 and Baxter, and present three intuitive mechanisms for providing such incremental feedback. In our experimental evaluation we consider two context rich settings, household chores and grocery store checkout, and show that users are able to train the robot with just a few feedbacks (taking only a few minutes).
Year
DOI
Venue
2015
10.1177/0278364915581193
Periodicals
Keywords
Field
DocType
Manipulation planning, online learning, user feedback
Online learning,Training set,Optimal trajectory,Regret,Computer science,Personal robot,Artificial intelligence,Novelty,Robot,Trajectory
Journal
Volume
Issue
ISSN
34
10
0278-3649
Citations 
PageRank 
References 
22
0.90
45
Authors
4
Name
Order
Citations
PageRank
ashesh jain11004.72
Shikhar Sharma2526.28
Thorsten Joachims3173871254.06
Ashutosh Saxena44575227.88