Social Learning Strategies for Evolved Virtual Soft Robots
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...
| Authors | K. Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 April 2026 |
| Last updated at source | 17 April 2026 |
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