Welcome to the Autonomous Control & Exploration (ACE) Lab at Johns Hopkins University! Our lab is focused on developing safe and computationally efficient algorithms for robot decision making. In particular, we leverage tools and theory from machine learning, optimization, and control that enable autonomous systems to operate safely under sensing and action uncertainty and by leveraging multiple modalities of information such as language and vision. Our work is broadly applied to a host of systems, including surface rover vehicles, satellite rendezvous-and-proximity operations, aerial robots, among others.
News
- [Dec 2025] Our work on running neural network-based optimal control on the International Space Station was featured in the news!
- [Sep 2025] We had two papers accepted at this year’s iSpaRo conference!
- [Aug 2025] Our paper on Principled Stochastic Trajectory Planning for Asteroid Reconnaissance was accepted into Journal of Guidance, Control, and Dynamics (JGCD)!
- [May 2025] Our paper on Constraint-Informed Learning for Warm Starting Trajectory Optimization was accepted into Journal of Guidance, Control, and Dynamics (JGCD)!
- [Mar 2025] Our paper on Transformer-Based Tight Constraint Prediction for Efficient Powered Descent Guidance was accepted into Journal of Guidance, Control, and Dynamics (JGCD)!
- [Oct 2024] Congratulations to Kazu Echigo for being a AIAA SciTech GNC Graduate Paper competition finalist for our paper on stochastic optimal control!
- [Jun 2024] Excited to announce that I’ll be joining Johns Hopkins Mechanical Engineering as an assistant professor starting in July 2025!
- [May 2023] Our paper on testing gecko adhesives on the ISS was awarded the IEEE Robotics and Automation Magazine Best Paper Award!