# When Your Robot Breaks: Active Learning During Plant Failure

arXiv: https://arxiv.org/abs/1912.08116 · HTML: https://mlsanity.com/paper/1912.08116/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

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

- [Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms](https://mlsanity.com/paper/2407.15283/) (2024)

## Abstract (arXiv)

Detecting and adapting to catastrophic failures in robotic systems requires a robot to learn its new dynamics quickly and safely to best accomplish its goals. To address this challenging problem, we propose probabilistically-safe, online learning techniques to infer the altered dynamics of a robot at the moment a failure (e.g., physical damage) occurs. We combine model predictive control and active learning within a chance-constrained optimization framework to safely and efficiently learn the new plant model of the robot. We leverage a neural network for function approximation in learning the latent dynamics of the robot under failure conditions. Our framework generalizes to various damage conditions while being computationally light-weight to advance real-time deployment. We empirically validate within a virtual environment that we can regain control of a severely damaged aircraft in seconds and require only 0.1 seconds to find safe, information-rich trajectories, outperforming state-of-the-art approaches.