242 citations · 391 across the 47 of their papers we have counts for
7 papers · 1 filter
LAMP: Learning a Motion Policy to Repeatedly Navigate in an Uncertain Environment
Florence Tsang, Tristan Walker, Ryan A. MacDonald +2
Mobile robots are often tasked with repeatedly navigating through an environment whose traversability changes over time. These changes may exhibit some hidden structure, which can…
Learning Control Barrier Functions with High Relative Degree for Safety-Critical Control
Chuanzheng Wang, Yinan Li, Yiming Meng +2
Control barrier functions have shown great success in addressing control problems with safety guarantees. These methods usually find the next safe control input by solving an onlin…
Joint Estimation of Expertise and Reward Preferences From Human Demonstrations
Pamela Carreno-Medrano, Stephen L. Smith, Dana Kulic
When a robot learns from human examples, most approaches assume that the human partner provides examples of optimal behavior. However, there are applications in which the robot lea…
Approximation Algorithms for Distributed Multi-Robot Coverage in Non-Convex Environments
Armin Sadeghi, Ahmad Bilal Asghar, Stephen L. Smith
In this paper, we revisit the distributed coverage control problem with multiple robots on both metric graphs and in non-convex continuous environments. Traditionally, the solution…
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…
Continuous Motion Planning with Temporal Logic Specifications using Deep Neural Networks
Chuanzheng Wang, Yinan Li, Stephen L. Smith +1
In this paper, we propose a model-free reinforcement learning method to synthesize control policies for motion planning problems with continuous states and actions. The robot is mo…