The use of simulators in robotics research is widespread, underpinning the majority of recent
advances in the field. There are now more options available to researchers than ever before …

We focus on the challenge of finding a diverse collection of quality solutions on complex
continuous domains. While quality diversity (QD) algorithms like Novelty Search with Local …

In quality-diversity algorithms, the behavioral diversity metric is a key design choice that
determines the quality of the evolved archives. Although behavioral diversity is traditionally …

Agents in a multiagent system must learn diverse policies that allow them to express
complex inter-agent relationships required to optimize a single team objective. Multiagent …

The MAP-Elites quality-diversity algorithm has been successful in robotics because it can
create a behaviorally diverse set of solutions that later can be used for adaptation, for …

If robots are to become ubiquitous, they will need to be able to adapt to complex and
dynamic environments. Robots that can adapt their bodies while deployed might be flexible …

One of the core functions in most Evolutionary Algorithms is mutation. In complex search
spaces, which are common in Evolutionary Robotics, mutation is often used both for …

Quality-diversity (QD) algorithms evolve behaviorally diverse and high-performing solutions.
To illuminate the elite solutions for a space of behaviors, QD algorithms require the definition …

Quality diversity algorithms are evolutionary algorithms that aim to evolve diverse repertoires
of high-quality solutions. Quality diversity has recently been used with considerable success …

Evolutionary optimization is difficult in domains that require heterogeneous agents to
coordinate on diverse tasks as agents often converge to a limited set of" acceptable" …