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Found 9 papers, 6 papers with code
Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing
Pre-training a diverse set of neural network controllers in simulation has enabled robots to adapt online to damage in robot locomotion tasks.
pyribs: A Bare-Bones Python Library for Quality Diversity Optimization
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 solutions to a given problem.
Scalable Hierarchical Agglomerative Clustering
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability.
Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning
Training can then be viewed as a quality diversity (QD) optimization problem, where we search for a collection of performant policies that are diverse with respect to quantified behavior.
On the Importance of Environments in Human-Robot Coordination
When studying robots collaborating with humans, much of the focus has been on robot policies that coordinate fluently with human teammates in collaborative tasks.
Surrogate Assisted Generation of Human-Robot Interaction Scenarios
In the shared control teleoperation domain and a more complex shared workspace collaboration task, we show that surrogate assisted scenario generation efficiently synthesizes diverse datasets of challenging scenarios.
Deep Surrogate Assisted Generation of Environments
Results in two benchmark domains show that DSAGE significantly outperforms existing QD environment generation algorithms in discovering collections of environments that elicit diverse behaviors of a state-of-the-art RL agent and a planning agent.
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning.
Density Descent for Diversity Optimization
We propose Density Descent Search (DDS), an algorithm that explores the feature space via gradient descent on a continuous density estimate of the feature space that also provides stronger stability guarantee.
