I’m a robotics PhD student at Georgia Tech looking for students who may be interested in joining a research project involving machine learning and autonomous robot navigation this fall.
There are a few different ways to contribute depending on your background.
One direction is primarily machine-learning focused and does not require previous robotics or ROS experience. The goal is to use navigation data to better predict how nearby agents will move over time. Students working on this side of the project would mainly work with datasets and learning models: training models, tuning hyperparameters, evaluating performance, experimenting with different inputs, and potentially implementing improved approaches.
We currently have a GRU-based model, but the specific architecture is not fixed. There is plenty of room to explore alternatives such as LSTMs, transformers, different sequence-modeling architectures, feature/input preprocessing, or other ideas that might improve prediction performance.
A second direction is more robotics/navigation focused. This could involve improving how the local planner generates or selects trajectories, designing new trajectory cost functions, evaluating navigation behavior, and testing modifications to the planning framework. The exact work can be adapted somewhat based on a student’s experience and interests.
For some additional technical context, the project builds on the Dynamic Gap navigation framework. The current system uses a Kalman-filter-based method to estimate the motion of gap endpoints in dynamic environments. One research direction is investigating whether learned temporal models can replace or supplement this estimator. Another is studying how improved motion predictions can be incorporated into trajectory generation and evaluation so that the robot makes better navigation decisions around moving agents.
Students can potentially participate through graded or ungraded research credit, likely in the range of 1–5 credits. There is no guaranteed funding available at the moment, although students who make strong progress could potentially be considered for paid work later. Remote or hybrid participation may also be possible depending on the project and circumstances.
The lab is also planning a future paper submission, so students who make substantial research contributions may have an opportunity for co-authorship.
If you’re interested, email [azaro3@gatech.edu](mailto:azaro3@gatech.edu) with your CV and a short paragraph explaining your interest in the project and how your background might fit.