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Inference Through Embodied Simulation in Cognitive Robots

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Abstract

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.

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Notes

  1. DARWIN stands for Dexterous Assembler Robots Working with embodied INtelligence (www.darwin-project.eu).

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Acknowledgments

The research presented in this article is supported by IIT (Istituto Italiano di Tecnologia, RBCS dept) and by the EU FP7 project DARWIN (http://www.darwin-project.eu, Grant No: FP7-270138). We are indebted to the anonymous reviewers for their detailed analysis and suggestions to make the draft sharp and more reader friendly. The authors also acknowledge the support of all teams involved in the DARWIN consortium.

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  1. Robotics, Brain and Cognitive Science Department, Istituto Italiano di Tecnologia, Via Company Morego 30, 16163, Genoa, Italy

    Vishwanathan Mohan, Pietro Morasso & Giulio Sandini

  2. Novocaptis Cognitive Systems and Robotics, Thessaloniki, Greece

    Stathis Kasderidis

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  1. Vishwanathan Mohan
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Mohan, V., Morasso, P., Sandini, G. et al. Inference Through Embodied Simulation in Cognitive Robots. Cogn Comput 5, 355–382 (2013). https://doi.org/10.1007/s12559-013-9205-4

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