13 citations · 19 across the 6 of their papers we have counts for
6 papers · 1 filter
Approximation Algorithms for Robot Tours in Random Fields with Guaranteed Estimation Accuracy
Shamak Dutta, Nils Wilde, Pratap Tokekar +1
We study the sample placement and shortest tour problem for robots tasked with mapping environmental phenomena modeled as stationary random fields. The objective is to minimize the…
Scheduling Operator Assistance for Shared Autonomy in Multi-Robot Teams
Yifan Cai, Abhinav Dahiya, Nils Wilde +1
In this paper, we consider the problem of allocating human operator assistance in a system with multiple autonomous robots. Each robot is required to complete independent missions,…
Learning Reward Functions from Scale Feedback
Nils Wilde, Erdem Bıyık, Dorsa Sadigh +1
Today's robots are increasingly interacting with people and need to efficiently learn inexperienced user's preferences. A common framework is to iteratively query the user about wh…
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…
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…
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…