# Social Learning Strategies for Evolved Virtual Soft Robots

arXiv: https://arxiv.org/abs/2604.12482 · HTML: https://mlsanity.com/paper/2604.12482/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 14 Apr 2026.

## Builds on (most cited first, 16 of 16)

- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [Bayesian Optimization in a Billion Dimensions via Random Embeddings](https://mlsanity.com/paper/1301.1942/) (2013)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [High Dimensional Bayesian Optimisation and Bandits via Additive Models](https://mlsanity.com/paper/1503.01673/) (2015)
- [Quality and Diversity Optimization: A Unifying Modular Framework](https://mlsanity.com/paper/1708.09251/) (2017)
- [Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots](https://mlsanity.com/paper/2201.09863/) (2022)
- [Scalable Co-Optimization of Morphology and Control in Embodied Machines](https://mlsanity.com/paper/1706.06133/) (2017)
- [A survey on policy search algorithms for learning robot controllers in a handful of trials](https://mlsanity.com/paper/1807.02303/) (2018)
- [The Effects of Learning in Morphologically Evolving Robot Systems](https://mlsanity.com/paper/2111.09851/) (2021)
- [Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots](https://mlsanity.com/paper/2402.09231/) (2024)
- [Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robots](https://mlsanity.com/paper/2306.09358/) (2023)
- [Evolutionary Brain-Body Co-Optimization Consistently Fails to Select for Morphological Potential](https://mlsanity.com/paper/2508.17464/) (2025)
- [Controller Distillation Reduces Fragile Brain-Body Co-Adaptation and Enables Migrations in MAP-Elites](https://mlsanity.com/paper/2504.06523/) (2025)
- [Towards Multi-Morphology Controllers with Diversity and Knowledge Distillation](https://mlsanity.com/paper/2404.14625/) (2024)
- [Generational Replacement and Learning for High-Performing and Diverse Populations in Evolvable Robots](https://mlsanity.com/paper/2601.03807/) (2026)
- [Integrating Sample Inheritance into Bayesian Optimization for Evolutionary Robotics](https://mlsanity.com/paper/2601.03813/) (2026)

## Built on by (most cited first, 1 of 1)

- [Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics](https://mlsanity.com/paper/2605.15769/) (2026)

## Abstract (arXiv)

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.