4.8k citations · 5.1k across the 44 of their papers we have counts for
6 papers · 1 filter
R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations
Connor Mattson, Varun Raveendra, Ellen Novoseller +3
Imitation Learning (IL) is a natural way for humans to teach robots, particularly when high-quality demonstrations are easy to obtain. While IL has been widely applied to single-ro…
EDEN: Entorhinal Driven Egocentric Navigation Toward Robotic Deployment
Mikolaj Walczak, Romina Aalishah, Wyatt Mackey +5
Deep reinforcement learning agents are often fragile while humans remain adaptive and flexible to varying scenarios. To bridge this gap, we present EDEN, a biologically inspired na…
Learning Flight Control Systems from Human Demonstrations and Real-Time Uncertainty-Informed Interventions
Prashant Ganesh, J. Humberto Ramos, Vinicius G. Goecks +4
This paper describes a methodology for learning flight control systems from human demonstrations and interventions while considering the estimated uncertainty in the learned models…
Mobile Manipulation Leveraging Multiple Views
David Watkins, Peter K Allen, Henrique Maia +4
While both navigation and manipulation are challenging topics in isolation, many tasks require the ability to both navigate and manipulate in concert. To this end, we propose a mob…
Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation
David Watkins-Valls, Jingxi Xu, Nicholas Waytowich +1
We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real e…
Inferring and Learning Multi-Robot Policies by Observing an Expert
Pietro Pierpaoli, Harish Ravichandar, Nicholas Waytowich +3
We present a technique for learning how to solve a multi-robot mission that requires interaction with an external environment by observing an expert system executing the same missi…