Mitchell McIntire

IROS Conference 2023 Conference Paper

Discovering Adaptable Symbolic Algorithms from Scratch

• Stephen Kelly
• Daniel S. Park
• Xingyou Song
• Mitchell McIntire
• Pranav Nashikkar
• Ritam Guha
• Wolfgang Banzhaf
• Kalyanmoy Deb

Autonomous robots deployed in the real world will need control policies that rapidly adapt to environmental changes. To this end, we propose AutoRobotics-Zero (ARZ), a method based on AutoML-Zero that discovers zero-shot adaptable policies from scratch. In contrast to neural network adaption policies, where only model parameters are optimized, ARZ can build control algorithms with the full expressive power of a linear register machine. We evolve modular policies that tune their model parameters and alter their inference algorithm on-the-fly to adapt to sudden environmental changes. We demonstrate our method on a realistic simulated quadruped robot, for which we evolve safe control policies that avoid falling when individual limbs suddenly break. This is a challenging task in which two popular neural network baselines fail. Finally, we conduct a detailed analysis of our method on a novel and challenging non-stationary control task dubbed Cataclysmic Cartpole. Results confirm our findings that ARZ is significantly more robust to sudden environmental changes and can build simple, interpretable control policies.

AAMAS Conference 2016 Conference Paper

Iterated Multi-Robot Auctions for Precedence-Constrained Task Scheduling

• Mitchell McIntire
• Ernesto Nunes
• Maria Gini

UAI Conference 2016 Conference Paper

Sparse Gaussian Processes for Bayesian Optimization

• Mitchell McIntire
• Daniel Ratner
• Stefano Ermon

Bayesian optimization schemes often rely on Gaussian processes (GP). GP models are very flexible, but are known to scale poorly with the number of training points. While several efficient sparse GP models are known, they have limitations when applied in optimization settings. We propose a novel Bayesian optimization framework that uses sparse online Gaussian processes. We introduce a new updating scheme for the online GP that accounts for our preference during optimization for regions with better performance. We apply this method to optimize the performance of a free-electron laser, and demonstrate empirically that the weighted updating scheme leads to substantial improvements to performance in optimization.