Speaker: Abhinav Valada
The research of the Robot Learning Lab lies at the intersection of robotics, machine learning and computer vision with a focus on tackling fundamental robot perception, state estimation and planning problems using learning approaches to enable robots to reliably operate in more complex domains and diverse environments. The overall goal of this research is to develop scalable lifelong robot learning systems that continuously learn multiple tasks from what they perceive and experience by interacting with the real-world. The groups approach is to design deep learning algorithms that facilitate transfer of information through self-supervised multimodal and multitask learning by exploiting complementary features and cross-modal interdependencies. These techniques in turn enable robots to perceive more robustly and reason about the environment more effectively.