# Morphological Wobbling Can Help Robots Learn

arXiv: https://arxiv.org/abs/2205.02811 · HTML: https://mlsanity.com/paper/2205.02811/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 9 of 9)

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [How morphological development can guide evolution](https://mlsanity.com/paper/1711.07387/) (2017)
- [A Minimal Developmental Model Can Increase Evolvability in Soft Robots](https://mlsanity.com/paper/1706.07296/) (2017)
- [Interoceptive robustness through environment-mediated morphological development](https://mlsanity.com/paper/1804.02257/) (2018)
- [An Experiment in Morphological Development for Learning ANN Based Controllers](https://mlsanity.com/paper/2003.07195/) (2020)
- [Morphological Development at the Evolutionary Timescale: Robotic Developmental Evolution](https://mlsanity.com/paper/2010.14894/) (2020)
- [Some Experiments on the influence of Problem Hardness in Morphological Development based Learning of Neural Controllers](https://mlsanity.com/paper/2003.05817/) (2020)

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

- [Informational Embodiment: Computational role of information structure in codes and robots](https://mlsanity.com/paper/2408.12950/) (2024)

## Abstract (arXiv)

We propose to make the physical characteristics of a robot oscillate while it learns to improve its behavioral performance. We consider quantities such as mass, actuator strength, and size that are usually fixed in a robot, and show that when those quantities oscillate at the beginning of the learning process on a simulated 2D soft robot, the performance on a locomotion task can be significantly improved. We investigate the dynamics of the phenomenon and conclude that in our case, surprisingly, a high-frequency oscillation with a large amplitude for a large portion of the learning duration leads to the highest performance benefits. Furthermore, we show that morphological wobbling significantly increases exploration of the search space.