# Damage Adaptation in Seconds for Architected Materials

arXiv: https://arxiv.org/abs/2606.17394 · HTML: https://mlsanity.com/paper/2606.17394/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 17 of 17)

- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [DayDreamer: World Models for Physical Robot Learning](https://mlsanity.com/paper/2206.14176/) (2022)
- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects](https://mlsanity.com/paper/1911.06283/) (2019)
- [Optimal construction of Koopman eigenfunctions for prediction and control](https://mlsanity.com/paper/1810.08733/) (2018)
- [Reset-free Trial-and-Error Learning for Robot Damage Recovery](https://mlsanity.com/paper/1610.04213/) (2016)
- [Maximum diffusion reinforcement learning](https://mlsanity.com/paper/2309.15293/) (2023)
- [Automated shapeshifting for function recovery in damaged robots](https://mlsanity.com/paper/1905.09264/) (2019)
- [Automated Gait Generation For Walking, Soft Robotic Quadrupeds](https://mlsanity.com/paper/2310.00498/) (2023)
- [MUKCa: Accurate and Affordable Cobot Calibration Without External Measurement Devices](https://mlsanity.com/paper/2503.12584/) (2025)
- [LyTimeT: Towards Robust and Interpretable State-Variable Discovery](https://mlsanity.com/paper/2510.19716/) (2025)
- [Bridging Hard and Soft: Mechanical Metamaterials Enable Rigid Torque Transmission in Soft Robots](https://mlsanity.com/paper/2412.02650/) (2024)
- [Force and Speed in a Soft Stewart Platform](https://mlsanity.com/paper/2504.13127/) (2025)
- [Spring-Brake! Handed Shearing Auxetics Improve Efficiency of Hopping and Standing](https://mlsanity.com/paper/2505.22898/) (2025)
- [Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates](https://mlsanity.com/paper/2509.08241/) (2025)
- [Real-Time Reinforcement Learning for Dynamic Tasks with a Parallel Soft Robot](https://mlsanity.com/paper/2509.19525/) (2025)
- [Zero-Shot Adaptation to Robot Structural Damage via Natural Language-Informed Kinodynamics Modeling](https://mlsanity.com/paper/2602.12385/) (2026)

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

- [Ergodic Control and Controlled Diffusion for Robot Learning: Review and Tutorial](https://mlsanity.com/paper/2609.13295/) (2026)

## Abstract (arXiv)

Adaptation to damages and in-situ physical repairs is essential for long-term robot autonomy, yet challenging outside of narrowly defined and well-anticipated bounds. In this work we proprioceptively adapt to catastrophic damage in soft-actuated systems in under one minute. Architected materials are well equipped for adaptation: actuator failure occurs gradually rather than acutely, and damage can be described in a low-dimensional, discrete coordinate space. Surprisingly, latent damage representations plus a simple yet robust ensemble method is sufficient for adapting to unseen damage in real-time. Moreover, we identify conditions under which exponential sample complexity collapses to linear sample complexity for learned representations of architected materials, a concrete advantage over rigid components or continuum soft mechanisms. We demonstrate LEAP, our method for adaptive proprioception, via a tracing task for a 6DoF soft wrist based on Handed Shearing Auxetic (HSA) actuators. Our algorithm is able to adapt to cuts, burns, and actuator repairs, enabling simulation-free real-time adaptation that is critical for realizing the promise of soft robots outside the lab. Videos and more information are available at https://murpheylab.github.io/leap.