Sensorimotor Learning in AI

In this Brains@Bay meetup, we are focusing on how sensorimotor learning (i.e. learning through interaction with the environment with a closed-loop between action and perception) can lead to more flexible and robust machine learning systems.

Speaker Lineup:
➤ Richard Sutton, DeepMind and University of Alberta
➤ Clément Moulin-Frier, Flowers Laboratory
➤ Viviane Clay, Numenta and University of Osnabrück

The talks will be followed by a discussion panel and Q&A.

We look forward to seeing you there!

➤ Richard Sutton, The Increasing Role of Sensorimotor Experience in Artificial Intelligence

Abstract: We receive information about the world through our sensors and influence the world through our effectors. Such low-level data has gradually come to play a greater role in AI during its 70-year history. I see this as occurring in four steps, two of which are mostly past and two of which are in progress or yet to come. Today we are seeing more calls for knowledge to be predictive and grounded in experience. After reviewing the history and prospects of the four steps, I propose a minimal architecture for an intelligent agent that is entirely grounded in experience.

➤ Clément Moulin-Frier, Open-ended Skill Acquisition in Humans and Machines: An Evolutionary and Developmental Perspective

Abstract: In this talk, I will propose a conceptual framework sketching a path toward open-ended skill acquisition through the coupling of environmental, morphological, sensorimotor, cognitive, developmental, social, cultural and evolutionary mechanisms. I will illustrate parts of this framework through computational experiments highlighting the key role of intrinsically motivated exploration in the generation of behavioral regularity and diversity. Firstly, I will show how some forms of language can self-organize out of generic exploration mechanisms without any functional pressure to communicate. Secondly, we will see how language — once invented — can be recruited as a cognitive tool that enables compositional imagination and bootstraps open-ended cultural innovation.

➤ Viviane Clay, The Effect of Sensorimotor Learning on the Learned Representations in Deep Neural Networks

Abstract: Most current deep neural networks learn from a static data set without active interaction with the world. We take a look at how learning through a closed loop between action and perception affects the representations learned in a DNN. We demonstrate how these representations are significantly different from DNNs that learn supervised or unsupervised from a static dataset without interaction. These representations are much sparser and encode meaningful content in an efficient way. Even an agent who learned without any external supervision, purely through curious interaction with the world, acquires encodings of the high dimensional visual input that enable the agent to recognize objects using only a handful of labeled examples. Our results highlight the capabilities that emerge from letting DNNs learn more similar to biological brains, though sensorimotor interaction with the world.