# Robots that can adapt like animals

arXiv: https://arxiv.org/abs/1407.3501 · HTML: https://mlsanity.com/paper/1407.3501/

## Key facts

- Cited by 204 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2014.

## Built on by (most cited first, 30 of 204)

- [Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior](https://mlsanity.com/paper/2212.03238/) (2022)
- [Human-Timescale Adaptation in an Open-Ended Task Space](https://mlsanity.com/paper/2301.07608/) (2023)
- [Motif: Intrinsic Motivation from Artificial Intelligence Feedback](https://mlsanity.com/paper/2310.00166/) (2023)
- [Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap](https://mlsanity.com/paper/2401.10034/) (2024)
- [LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization](https://mlsanity.com/paper/2306.01102/) (2023)
- [Differentiable Quality Diversity](https://mlsanity.com/paper/2106.03894/) (2021)
- [Robot Learning from Randomized Simulations: A Review](https://mlsanity.com/paper/2111.00956/) (2021)
- [Deep Surrogate Assisted Generation of Environments](https://mlsanity.com/paper/2206.04199/) (2022)
- [Modelling Behavioural Diversity for Learning in Open-Ended Games](https://mlsanity.com/paper/2103.07927/) (2021)
- [Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning](https://mlsanity.com/paper/2202.03666/) (2022)
- [On the Importance of Environments in Human-Robot Coordination](https://mlsanity.com/paper/2106.10853/) (2021)
- [Hierarchical Few-Shot Imitation with Skill Transition Models](https://mlsanity.com/paper/2107.08981/) (2021)
- [Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality](https://mlsanity.com/paper/2205.13521/) (2022)
- [Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased](https://mlsanity.com/paper/2302.01605/) (2023)
- [Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning](https://mlsanity.com/paper/2308.14284/) (2023)
- [Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization](https://mlsanity.com/paper/2103.04564/) (2021)
- [Illuminating Diverse Neural Cellular Automata for Level Generation](https://mlsanity.com/paper/2109.05489/) (2021)
- [Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2206.07505/) (2022)
- [Unsupervised Behaviour Discovery with Quality-Diversity Optimisation](https://mlsanity.com/paper/2106.05648/) (2021)
- [pyribs: A Bare-Bones Python Library for Quality Diversity Optimization](https://mlsanity.com/paper/2303.00191/) (2023)
- [Evolutionary Reinforcement Learning: A Survey](https://mlsanity.com/paper/2303.04150/) (2023)
- [Human irrationality: both bad and good for reward inference](https://mlsanity.com/paper/2111.06956/) (2021)
- [Covariance Matrix Adaptation MAP-Annealing](https://mlsanity.com/paper/2205.10752/) (2022)
- [Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems](https://mlsanity.com/paper/2102.07659/) (2021)
- [Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization](https://mlsanity.com/paper/2204.02246/) (2022)
- [Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery](https://mlsanity.com/paper/2210.03516/) (2022)
- [Surrogate modeling for Bayesian optimization beyond a single Gaussian process](https://mlsanity.com/paper/2205.14090/) (2022)
- [Policy Manifold Search: Exploring the Manifold Hypothesis for Diversity-based Neuroevolution](https://mlsanity.com/paper/2104.13424/) (2021)
- [Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires](https://mlsanity.com/paper/2109.08522/) (2021)
- [Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning](https://mlsanity.com/paper/2305.13795/) (2023)

## Abstract (arXiv)

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury.