Martina Stadler Kurtz

I’m a roboticist working on something new in the Boston area!

Previously, I was a PhD student (and, briefly, a robotics researcher) at the Robust Robotics Group at MIT CSAIL, advised by Prof. Nicholas Roy. My research developed uncertainty-aware models and planners that used implicit and explicit environmental structure to improve robot planning efficiency and quality in large, structured and/or unknown outdoor environments. Most recently, I developed and deployed collaborative multiagent planning algorithms that explicitly considered the team costs and benefits of taking sensing actions in stochastic environments when an agent had access to stale environmental data.

In the past, my collaborators and I used geometric and explicit object-level information to learned sampling distributions for sampling-based motion planners which enabled efficient planning at longer horizons in partially known environments. I also proposed a hierarchical planning representation for multi-query robot navigation which used previous planning experience to coarsely capture implicit environmental structure and prune regions of the environment which were unlikely to lead to low cost solutions for hierarchical, multi-query robot navigation.

Publications

Conference Papers:

Y. Veys, M. S. Kurtz, N. Roy, “Generating Sparse Probabilistic Graphs for Efficient Planning in Uncertain Environments.” International Conference on Robotics and Automation (ICRA), 2024. [PDF]

M. Stadler, J. Banfi, N. Roy, “Approximating the Value of Collaborative Team Actions for Efficient Multiagent Navigation in Uncertain Graphs.” International Conference on Planning and Scheduling (ICAPS), 2023. [PDF]

A. Messing*, J. Banfi*, M. Stadler, E. Stump, H. Ravichandar, N. Roy, S. Hutchinson, “A Sampling-Based Approach for Heterogeneous Coalition Scheduling with Temporal Uncertainty”, Robotics: Science and Systems (RSS), 2023. [PDF]

M. Stadler, K. Liu, N. Roy. “Online High-Level Model Estimation for Efficient Hierarchical Robot Navigation.” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021. [PDF]

K. Liu*, M. Stadler*, and N. Roy. “Learned Sampling Distributions for Efficient Planning in Hybrid Geometric and Object-Level Representations.” International Conference on Robotics and Automation (ICRA), 2020. [PDF] [Video]

Workshop Papers:

M. S. Kurtz*, S. Prentice*, Y. Veys, L. Quang, C. Nieto-Granda, M. Novitzky, E. Stump, N. Roy, “Real-World Deployment of a Hierarchical Uncertainty-Aware Collaborative Multiagent Planning System.” ICRA Workshop on Field Robotics, 2024. [PDF]

Theses:

M. S, Kurtz, “Towards Efficient Planning for Navigation using Global Information in Large and Uncertain Environments.” PhD thesis, 2024.

M. Stadler, “Learned Functions for Perceptually Informed Robot Navigation.” Master’s thesis, 2020. [PDF]