Enhancing Adaptability in Embodied Agents: A Multi-Quality-Diversity Approach

On the path towards truly autonomous robots, embodied agents will require to be adaptable to unforeseen circumstances. Yet, most robotic agents still suffer from significant performance degradation when scenarios change slightly, with many even failing their tasks entirely. In contrast, organisms in nature exhibit strong adaptability, largely due to bio-diversity, which has prevented the extinction of life throughout severe environmental changes. The concept of quality-diversity aims to emulate this natural resilience, yielding robust results through diversification of embodied agents in the behavior space. However, in nature, diversity occurs simultaneously at multiple levels: body, brain, and behavior. This study on the body-brain optimization of virtual embodied agents spans two brain representations—an Artificial Neural Network (ANN) and a graph—and investigates these levels to determine the most critical scope for diversity in fostering performance, generality, and robustness. We start by optimizing for a simple locomotion task, and then evaluate generality through transfer to a diverse set of tasks, including locomotion in new environments and interaction with objects. Our findings confirm the importance of simultaneously considering multiple axes of diversity for achieving good performance and adaptability—demonstrating zero-shot transfer on 18 new tasks. Moreover, we observe that the graph controller performs on par with the ANN, offering greater interpretability.