# Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots

arXiv: https://arxiv.org/abs/2503.12406 · HTML: https://mlsanity.com/paper/2503.12406/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 50 references that are in the map.
- First posted on arXiv 16 Mar 2025.

## Builds on (most cited first, 25 of 25)

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [A Study on Overfitting in Deep Reinforcement Learning](https://mlsanity.com/paper/1804.06893/) (2018)
- [Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior](https://mlsanity.com/paper/2212.03238/) (2022)
- [Multi-expert learning of adaptive legged locomotion](https://mlsanity.com/paper/2012.05810/) (2020)
- [Robot Learning from Randomized Simulations: A Review](https://mlsanity.com/paper/2111.00956/) (2021)
- [Policies Modulating Trajectory Generators](https://mlsanity.com/paper/1910.02812/) (2019)
- [Meta Learning Backpropagation And Improving It](https://mlsanity.com/paper/2012.14905/) (2020)
- [Meta-Learning through Hebbian Plasticity in Random Networks](https://mlsanity.com/paper/2007.02686/) (2020)
- [Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks](https://mlsanity.com/paper/1703.10371/) (2017)
- [Investigating Generalisation in Continuous Deep Reinforcement Learning](https://mlsanity.com/paper/1902.07015/) (2019)
- [Towards Self-Assembling Artificial Neural Networks through Neural Developmental Programs](https://mlsanity.com/paper/2307.08197/) (2023)
- [Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization](https://mlsanity.com/paper/1912.05239/) (2019)
- [Evolving and Merging Hebbian Learning Rules: Increasing Generalization by Decreasing the Number of Rules](https://mlsanity.com/paper/2104.07959/) (2021)
- [Learning Robust, Agile, Natural Legged Locomotion Skills in the Wild](https://mlsanity.com/paper/2304.10888/) (2023)
- [Meta-Reinforcement Learning with Self-Modifying Networks](https://mlsanity.com/paper/2202.02363/) (2022)
- [Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems](https://mlsanity.com/paper/2004.12846/) (2020)
- [Testing the Genomic Bottleneck Hypothesis in Hebbian Meta-Learning](https://mlsanity.com/paper/2011.06811/) (2020)

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

- [Emergent Heterogeneous Swarm Control Through Hebbian Learning](https://mlsanity.com/paper/2507.11566/) (2025)

## Abstract (arXiv)

Artificial neural networks can be used to solve a variety of robotic tasks. However, they risk failing catastrophically when faced with out-of-distribution (OOD) situations. Several approaches have employed a type of synaptic plasticity known as Hebbian learning that can dynamically adjust weights based on local neural activities. Research has shown that synaptic plasticity can make policies more robust and help them adapt to unforeseen changes in the environment. However, networks augmented with Hebbian learning can lead to weight divergence, resulting in network instability. Furthermore, such Hebbian networks have not yet been applied to solve legged locomotion in complex real robots with many degrees of freedom. In this work, we improve the Hebbian network with a weight normalization mechanism for preventing weight divergence, analyze the principal components of the Hebbian's weights, and perform a thorough evaluation of network performance in locomotion control for real 18-DOF dung beetle-like and 16-DOF gecko-like robots. We find that the Hebbian-based plastic network can execute zero-shot sim-to-real adaptation locomotion and generalize to unseen conditions, such as uneven terrain and morphological damage.