13 citations · 13 across the 1 of their papers we have counts for
3 papers
cs.RO2020
Active Preference Learning using Maximum Regret
Nils Wilde, Dana Kulic, Stephen L. Smith
We study active preference learning as a framework for intuitively specifying the behaviour of autonomous robots. In active preference learning, a user chooses the preferred behavi…
cs.RO2019
Improving User Specifications for Robot Behavior through Active Preference Learning: Framework and Evaluation
Nils Wilde, Alexandru Blidaru, Stephen L. Smith +1
An important challenge in human-robot interaction (HRI) is enabling non-expert users to specify complex tasks for autonomous robots. Recently, active preference learning has been a…
cs.RO2019★ 13 cited
Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions
Nils Wilde, Dana Kulic, Stephen L. Smith
Specifying complex task behaviours while ensuring good robot performance may be difficult for untrained users. We study a framework for users to specify rules for acceptable behavi…