Robots thinking fast and slow: on dual process theory and metacognition in embodied AI

Conference item

Recent progress in AI technology has been breathtaking. However, many of the advances have played to the strengths of virtual environments: infinite training data is available, risk-free exploration is possible, acting is essentially free. In contrast, we require our robots to robustly operate in real-time, to learn from a limited amount of data, take mission- and sometimes safety-critical decisions and increasingly even display a knack for creative problem solving. To bridge this gap, here we offer an alternative view of recent advances in AI. In particular, we posit that, for the first time, roboticists can draw meaningful functional parallels between AI technology and components identified in the cognitive sciences as pivotal to robust operation in the real world: Dual Process Theory and metacognition. Revisiting recent work in robot learning, we establish the building blocks of a Dual Process Theory for robots and highlight potentially fruitful future research directions towards delivering robust, versatile and safe embodied AI.

Authors: Posner, H

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: OpenReview
• Publication date: 2020-06-05
• Acceptance date: 2020-06-25
• Event title: RSS Workshop on Robotics Retrospectives
• Event location: Virtual Workshop
• Event website: https://sites.google.com/stanford.edu/roboticsretrospectives/home
• Event start date: 2019-07-12
• Event end date: 2020-07-12
• Language: English
• Keywords: FFR