This talk will describe the STAIR home assistant robot project, and several satellite projects that led to key STAIR components such as (i) robotic grasping of previously unknown objects, (ii) depth perception from a single still image, and (iii) apprenticeship learning for control.

Since its birth in 1956, the AI dream has been to build systems that exhibit broad-spectrum competence and intelligence. STAIR revisits this dream, and seeks to integrate onto a single robot platform tools drawn from all areas of AI including learning, vision, navigation, manipulation, planning, and speech/NLP. This is in distinct contrast to, and also represents an attempt to reverse, the 30 year old trend of working on fragmented AI sub-fields. STAIR's goal is a useful home assistant robot, and over the long term, we envision a single robot that can perform tasks such as tidying up a room, using a dishwasher, fetching and delivering items, and preparing meals.

STAIR is still a young project, and in this talk I'll report on our progress so far on having STAIR fetch items from around the office. Specifically, I'll describe: (i) learning to grasp previously unseen objects (including its application to unloading items from a dishwasher); (ii) probabilistic multi-resolution maps, which enable the robot to open/use doors; (iii) a robotic foveal+peripheral vision system for object recognition and tracking. I'll also outline some of the main technical ideas - such as learning 3-d reconstructions from a single still image, and reinforcement learning algorithms for robotic control - that played key roles in enabling these STAIR components.