121 citations · 251 across the 23 of their papers we have counts for
6 papers · 1 filter
Arbitrarily Scalable Environment Generators via Neural Cellular Automata
Yulun Zhang, Matthew C. Fontaine, Varun Bhatt +2
We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effe…
Multi-Robot Coordination and Layout Design for Automated Warehousing
Yulun Zhang, Matthew C. Fontaine, Varun Bhatt +2
With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have studied how MAPF algorithms can be deployed to coordinate hundreds of robots in large automated warehou…
Surrogate Assisted Generation of Human-Robot Interaction Scenarios
Varun Bhatt, Heramb Nemlekar, Matthew C. Fontaine +4
As human-robot interaction (HRI) systems advance, so does the difficulty of evaluating and understanding the strengths and limitations of these systems in different environments an…
Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing
Bryon Tjanaka, Matthew C. Fontaine, David H. Lee +2
Pre-training a diverse set of neural network controllers in simulation has enabled robots to adapt online to damage in robot locomotion tasks. However, finding diverse, high-perfor…
On the Importance of Environments in Human-Robot Coordination
Matthew C. Fontaine, Ya-Chuan Hsu, Yulun Zhang +2
When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks. However, less emp…
A Quality Diversity Approach to Automatically Generating Human-Robot Interaction Scenarios in Shared Autonomy
Matthew Fontaine, Stefanos Nikolaidis
The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applic…