Interactive Robot Perception and Learning for Mobile Manipulation

The long-standing ambition for autonomous, intelligent service robots that are seamlessly integrated into our everyday environments is yet to become a reality. Humans develop comprehension of their embodiments by interpreting their actions within the world and acting reciprocally to perceive it —- the environment affects our actions, and our actions simultaneously affect our environment. Besides great advances in robotics and Artificial Intelligence (AI), e.g., through better hardware designs or algorithms incorporating advances in Deep Learning in robotics, we are still far from achieving robotic embodied intelligence. The challenge of attaining artificial embodied intelligence — intelligence that originates and evolves through an agent’s sensorimotor interaction with its environment — is a topic of substantial scientific investigation and is still an open challenge. In this talk, I will walk you through our recent research works for endowing robots with spatial intelligence through perception and interaction to coordinate and acquire skills that are necessary for their promising real-world applications. In particular, we will see how we can use robotic priors for learning to coordinate mobile manipulation robots, how neural representations can allow for learning policies and safe interactions, and, at the crux, how we can leverage those representations to allow the robot to understand and interact with a scene, or guide it to acquire more “information” while acting in a task-oriented manner.

Online

Lifelong Learning