Tag Archives: Cognitive Architecture

A developmental architecture for sensory-motor skills based on predictors, and a nice state-of-the-art in cognitive architectures for sensory-motor skill learning

E. Wieser and G. Cheng, A Self-Verifying Cognitive Architecture for Robust Bootstrapping of Sensory-Motor Skills via Multipurpose Predictors, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 4, pp. 1081-1095, DOI: 10.1109/TCDS.2018.2871857.

The autonomous acquisition of sensory-motor skills along multiple developmental stages is one of the current challenges in robotics. To this end, we propose a new developmental cognitive architecture that combines multipurpose predictors and principles of self-verification for the robust bootstrapping of sensory-motor skills. Our architecture operates with loops formed by both mental simulation of sensory-motor sequences and their subsequent physical trial on a robot. During these loops, verification algorithms monitor the predicted and the physically observed sensory-motor data. Multiple types of predictors are acquired through several developmental stages. As a result, the architecture can select and plan actions, adapt to various robot platforms by adjusting proprioceptive feedback, predict the risk of self-collision, learn from a previous interaction stage by validating and extracting sensory-motor data for training the predictor of a subsequent stage, and finally acquire an internal representation for evaluating the performance of its predictors. These cognitive capabilities in turn realize the bootstrapping of early hand-eye coordination and its improvement. We validate the cognitive capabilities experimentally and, in particular, show an improvement of reaching as an example skill.

A cognitive architecture for self-development in robots that interact with humans, with a nice state-of-the-art of robot cognitive architectures

C. Moulin-Frier et al., DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 4, pp. 1005-1022, DOI: 10.1109/TCDS.2017.2754143.

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both human and robot. The framework, based on a biologically grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the-art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users.

A robot architecture for humanoids able to coordinate different cognitive processes (perception, decision-making, etc.) in a hierarchical fashion

J. Hwang and J. Tani, Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 2, pp. 345-358 DOI: 10.1109/TCDS.2017.2714170.

This paper investigates how adequate coordination among the different cognitive processes of a humanoid robot can be developed through end-to-end learning of direct perception of visuomotor stream. We propose a deep dynamic neural network model built on a dynamic vision network, a motor generation network, and a higher-level network. The proposed model was designed to process and to integrate direct perception of dynamic visuomotor patterns in a hierarchical model characterized by different spatial and temporal constraints imposed on each level. We conducted synthetic robotic experiments in which a robot learned to read human’s intention through observing the gestures and then to generate the corresponding goal-directed actions. Results verify that the proposed model is able to learn the tutored skills and to generalize them to novel situations. The model showed synergic coordination of perception, action, and decision making, and it integrated and coordinated a set of cognitive skills including visual perception, intention reading, attention switching, working memory, action preparation, and execution in a seamless manner. Analysis reveals that coherent internal representations emerged at each level of the hierarchy. Higher-level representation reflecting actional intention developed by means of continuous integration of the lower-level visuo-proprioceptive stream.

A summary of the Clarion cognitive architecture

Ron Sun, Anatomy of the Mind: a Quick Overview, Cognitive Computation, February 2017, Volume 9, Issue 1, pp 1–4, DOI: 10.1007/s12559-016-9444-2.

The recently published book, “Anatomy of the Mind,” explains psychological (cognitive) mechanisms, processes, and functionalities through a comprehensive computational theory of the human mind—that is, a cognitive architecture. The goal of the work has been to develop a unified framework and then to develop process-based mechanistic understanding of psychological phenomena within the unified framework. In this article, I will provide a quick overview of the work.