Welcome!
I am a sixth-year PhD student in the Robust Robotics Group at MIT CSAIL, advised by Prof. Nicholas Roy.
My research develops uncertainty-aware models and planners which use implicit and explicit environmental structure to improve robot planning efficiency and quality in structured and/or unknown environments. In my current work, I am developing collaborative multiagent planning algorithms which explicitly consider the team costs and benefits of taking sensing actions in stochastic environments when an agent has 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:
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]
Theses:
M. Stadler, “Learned Functions for Perceptually Informed Robot Navigation.” Master’s thesis, 2020. [PDF]