121 citations · 654 across the 70 of their papers we have counts for
10 papers · 1 filter
Quality-Diversity Generative Sampling for Learning with Synthetic Data
Allen Chang, Matthew C. Fontaine, Serena Booth +2
Generative models can serve as surrogates for some real data sources by creating synthetic training datasets, but in doing so they may transfer biases to downstream tasks. We focus…
Density Descent for Diversity Optimization
David H. Lee, Anishalakshmi V. Palaparthi, Matthew C. Fontaine +2
Diversity optimization seeks to discover a set of solutions that elicit diverse features. Prior work has proposed Novelty Search (NS), which, given a current set of solutions, seek…
Signal Temporal Logic-Guided Apprenticeship Learning
Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis
Apprenticeship learning crucially depends on effectively learning rewards, and hence control policies from user demonstrations. Of particular difficulty is the setting where the de…
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…
BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning
Tianle Huang, Nitish Sontakke, K. Niranjan Kumar +4
Domain randomization (DR), which entails training a policy with randomized dynamics, has proven to be a simple yet effective algorithm for reducing the gap between simulation and t…
Multi-Robot Geometric Task-and-Motion Planning for Collaborative Manipulation Tasks
Hejia Zhang, Shao-Hung Chan, Jie Zhong +6
We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple rob…
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
Sumeet Batra, Bryon Tjanaka, Matthew C. Fontaine +3
Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning. Quality Diversity Reinforcement…
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…
pyribs: A Bare-Bones Python Library for Quality Diversity Optimization
Bryon Tjanaka, Matthew C. Fontaine, David H. Lee +6
Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a branch of optimization that seeks to find a collection of diverse, high-performing solutio…
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…