Deep learning, multi-agent systems and computer vision.
Papers and Presentations
- Ghalamzan, A., Foster, J., and Gudelis, M. (2021). Perception in Agri-Food Manipulation: A Review. ICRA Task-Informed Grasping Workshop – III, online. Watch here.
- Ayris, K., Foster, J., Kihara, S.W., Slattery, T., and Sochacki G. (2021). Visual Serving for Human Tracking and Counting. AgriFoRwArdS CDT Annual Conference 2021, online.
- Member of the AgriFoRwArdS CDT Advisory Board.
Hi I’m Jack, I started out studying Computer Science at Keele before moving to the University of Birmingham to complete my master’s in Robotics. After that, I joined the CDT in 2020. So far, I have really enjoyed working with the cohort, its far more engaging and fun to attend talks or training days with a big group of people you know and are friends with. I’m mainly interested in the autonomy and decision making behind robotics, particularly long-term autonomy, and anything to do with neural nets, genetic algorithms, or multi-agent systems. In my spare time, I enjoy playing the guitar, photography, or nipping into town on the electric scooters dotted around Cambridge!
Lifelong Learning for Sensor-based Precipitation Regression
This project seeks to improve the crop yield of a small farm by optimising the soil-moisture content via precipitation regression. A CNN or RNN, will use wind speed/direction, humidity, temperature, and air pressure to predict future precipitation. Once the network is trained, lifelong learning will be applied to improve the network’s performance when localised within the small farm, using data collected in real-time from a range of sensors and an Arduino. The future precipitation will be combined with the current moisture levels to create predictions on whether the crop requires watering today.