40 citations · 71 across the 12 of their papers we have counts for
12 papers
Red-Teaming Vision-Language-Action Models via Quality Diversity Prompt Generation for Robust Robot Policies
Siddharth Srikanth, Freddie Liang, Ya-Chuan Hsu +9
Vision-Language-Action (VLA) models have significant potential to enable general-purpose robotic systems for a range of vision-language tasks. However, the performance of VLA-based…
Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces
Bryon Tjanaka, Henry Chen, Matthew C. Fontaine +1
Quality diversity (QD) optimization searches for a collection of solutions that optimize an objective while attaining diverse outputs of a user-specified, vector-valued measure fun…
Quality Diversity for Robot Learning: Limitations and Future Directions
Sumeet Batra, Bryon Tjanaka, Stefanos Nikolaidis +1
Quality Diversity (QD) has shown great success in discovering high-performing, diverse policies for robot skill learning. While current benchmarks have led to the development of po…
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…
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…
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…
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…
Deep Surrogate Assisted Generation of Environments
Varun Bhatt, Bryon Tjanaka, Matthew C. Fontaine +1
Recent progress in reinforcement learning (RL) has started producing generally capable agents that can solve a distribution of complex environments. These agents are typically test…
Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning
Bryon Tjanaka, Matthew C. Fontaine, Julian Togelius +1
Consider the problem of training robustly capable agents. One approach is to generate a diverse collection of agent polices. Training can then be viewed as a quality diversity (QD)…
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…
Scalable Hierarchical Agglomerative Clustering
Nicholas Monath, Avinava Dubey, Guru Guruganesh +9
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods…