Inference Through Embodied Simulation in Cognitive Robots

• Published in: Cognitive computation Vol. 5; no. 3; pp. 355 - 382
• Main Authors: Mohan, Vishwanathan, Morasso, Pietro, Sandini, Giulio, Kasderidis, Stathis
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.09.2013; Springer Nature B.V
• Subjects: Artificial Intelligence; Biomedical and Life Sciences; Biomedicine; Brain research; Cerebral cortex; Cognition & reasoning; Computation by Abstract Devices; Computational Biology/Bioinformatics; Hypotheses; Inference; Information flow; Inverse problems; Learning; Machine learning; Neurosciences; Perception; Reasoning; Robotics; Robots; Simulation; Substrates; Embodied simulation; Brain guidance; Cognitive robotics; Learning and reasoning; Small worlds; DARWIN project; Body schema
• ISSN: 1866-9956; 1866-9964
• DOI: 10.1007/s12559-013-9205-4

In Professor Taylor’s own words, the most striking feature of any cognitive system is its ability to “learn and reason” cumulatively throughout its lifetime, the structure of its inferences both emerging and constrained by the structure of its bodily experiences. Understanding the computational/neural basis of embodied intelligence by reenacting the “developmental learning” process in cognitive robots and in turn endowing them with primitive capabilities to learn, reason and survive in “unstructured” environments (domestic and industrial) is the vision of the EU-funded DARWIN project, one of the last adventures Prof. Taylor embarked upon. This journey is about a year old at present, and our article describes the first developments in relation to the learning and reasoning capabilities of DARWIN robots. The novelty in the computational architecture stems from the incorporation of recent ideas firstly from the field of “connectomics” that attempts to explore the large-scale organization of the cerebral cortex and secondly from recent functional imaging and behavioral studies in support of the embodied simulation hypothesis. We show through the resulting behaviors’ of the robot that from a computational viewpoint, the former biological inspiration plays a central role in facilitating “functional segregation and global integration,” thus endowing the cognitive architecture with “small-world” properties. The latter on the other hand promotes the incessant interleaving of “top-down” and “bottom-up” information flows (that share computational/neural substrates) hence allowing learning and reasoning to “cumulatively” drive each other. How the robot learns about “objects” and simulates perception, learns about “action” and simulates action (in this case learning to “push” that follows pointing, reaching, grasping behaviors’) are used to illustrate central ideas. Finally, an example of how simulation of perception and action lead the robot to reason about how its world can change such that it becomes little bit more conducive toward realization of its internal goal (an assembly task) is used to describe how “object,” “action,” and “body” meet in the Darwin architecture and how inference emerges through embodied simulation.