The goal of this project was to build an assistive tool that could help Football coaches to simulate the actions of an “optimally behaving” defensive team. We developed a cost function that optimizes both Pitch Control and Pass Interceptions for the defensive team.
In order to assess the feasibility of active inference as a framework for synthetic agents in a real-world setting, we developed a ground-based robot that needs to learn to navigate to an undisclosed parking location. The robot can only learn where to park through situated interactions with a human observer who is aware of the target location.
ForneyLab is a novel Julia package that allows the user to specify a probabilistic model as an FFG and pose inference problems on this FFG. ForneyLab is especially potent when applied to time-series data, where it attains comparable performance to Stan and Edward in significantly less computation time.
The primary mission of the BIASlab team is to develop in-situ trainable Bayesian Intelligent Agents for applications to wearable technology.
How do you solve a problem that is only vaguely described? We describe here an engineering approach that guides our research on solving vaguely defined problems such as hearing impairment.
Signal processing: develop a probabilistic model for acoustic source separation.
Control systems: develop a controller to make a four-legged robot learn to walk.
Software: develop a multi-core computation procedure for our toolbox ReactiveMP.jl