Abstract Purpose of Review Current real-world interaction between humans and robots is
extremely limited. We present challenges that, if addressed, will enable humans and robots …

Collaborating with humans requires rapidly adapting to their individual strengths,
weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement …

Training generally-capable agents with reinforcement learning (RL) remains a significant
challenge. A promising avenue for improving the robustness of RL agents is through the use …

Quality diversity (QD) is a growing branch of stochastic optimization research that studies the
problem of generating an archive of solutions that maximize a given objective function but …

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 …

Recent progress in reinforcement learning (RL) has started producing generally capable
agents that can solve a distribution of complex environments. These agents are typically …

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) …

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 effective …

As large language models (LLMs) become increasingly prevalent across many real-world
applications, understanding and enhancing their robustness to user inputs is of paramount …

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 …