Social Learning Strategies for Evolved Virtual Soft Robots

TL;DR

This paper presents a social learning framework for virtual soft robots, demonstrating that leveraging peer-optimized parameters accelerates brain optimization and improves performance across tasks.

Contribution

Introducing a social learning approach for soft robot optimization that exploits peer knowledge to enhance control parameter learning efficiency.

Findings

Social learning outperforms learning from scratch within the same computational budget.

Inheriting experience from similar morphologically robots improves optimization.

Combining multiple teachers yields more robust and consistent improvements.

Abstract

Optimizing the body and brain of a robot is a coupled challenge: the morphology determines what control strategies are effective, while the control parameters influence how well the morphology performs. This joint optimization can be done through nested loops of evolutionary and learning processes, where the control parameters of each robot are learned independently. However, the control parameters learned by one robot may contain valuable information for others. Thus, we introduce a social learning approach in which robots can exploit optimized parameters from their peers to accelerate their own brain optimization. Within this framework, we systematically investigate how the selection of teachers, deciding which and how many robots to learn from, affects performance, experimenting with virtual soft robots in four tasks and environments. In particular, we study the effect of inheriting experience from morphologically similar robots due to the tightly coupled body and brain in robot optimization. Our results confirm the effectiveness of building on others' experience, as social learning clearly outperforms learning from scratch under equivalent computational budgets. In addition, while the optimal teacher selection strategy remains open, our findings suggest that incorporating knowledge from multiple teachers can yield more consistent and robust improvements.