AgriVision 2.0: Vision Language Action Robotics in Agriculture

With the advent of machine learning, purely modeless data driven robotic planning and control becomes a reality. Advanced capabilities are shown to be possible with self-supervised learning from data.

Techniques such as Reinforcement Learning, world modelling, generative AI and language models have been successfully applied to robotics to solve a range of complex tasks. I intend to develop advanced robotic learning techniques in the specific context of agriculture applications.

Innovation can be derived from the novel applications and push the boundaries of robotic capabilities and deployment in agriculture.