Structural bootstrapping for 24/7 humanoids

Humanoid robotics has made significant progress and will continue to play a central role in robotics research and many applications of the 21st century. Ambitious goals have been set for future humanoid robotics. They are expected to serve as companions and assistants for humans in daily life and as ultimate helpers in man-made and natural disasters. Although current humanoids are technologically advanced, they are still limited in their actuation, sensing, prediction, interaction, and learning capabilities. Versatile humanoid robot systems integrating perception, action, prediction, planning, and lifelong learning to carry out tasks in 24/7 manner in the real world are still missing.

The first part of the talk will present recent progress towards building 24/7 humanoid robots able to predict and act in the real world, to interact and collaborate with humans in human-centered environments, to autonomously acquire knowledge about objects and their own bodies through active visuo-haptic exploration, and to learn and imitate human actions. The capabilities will be demonstrated on the humanoid robots ARMAR-IIIa and ARMAR-IIIb. The second part of the talk will introduce and discuss the concept of Structural Bootstrapping, an idea taken from child language acquisition research, for building generative models, leveraging existing experience to predict unexplored action effects, and to focus the hypothesis space for learning novel concepts. Structural bootstrapping makes use of two different kinds of information about concepts, words or actions to speed up learning. Specifically, structural bootstrapping leverages information about how concepts, words, or actions are used (their syntax) against the concept, word, or action’s meaning (its semantics) to learn new concepts, words, and actions.