Knowledge acquisition and learning in unstructured robotic assembly environments

A Multiview Recognition Method of Predefined Objects for Robot Assembly Using Deep Learning and Its Implementation on an FPGA

The process of recognizing manufacturing parts in real time requires fast, accurate, small, and low-power-consumption sensors. Here, we describe a method to extract descriptors from several objects observed from a wide range of angles in a three-dimensional space. These descriptors define the dataset, which allows for the training and further validation of a convolutional neural network. The classification is implemented in reconfigurable hardware in an embedded system with an RGB sensor and the processing unit. The system achieved an accuracy of 96.67% and a speed 2.25× faster than the results reported for state-of-the-art solutions. Our proposal is 655 times faster than implementation on a PC. The presented embedded system meets the criteria of real-time video processing and it is suitable as an enhancement for the hand of a robotic arm in an intelligent manufacturing cell.

ART, cognitive science, and technology transfer

Three computational examples illustrate how cognitive science can introduce new approaches to the analysis of large datasets. The first example addresses the question: how can a neural system learning from one example at a time absorb information that is inconsistent but correct, as when a family pet is called Spot and dog and animal, while rejecting similar incorrect information, as when the same pet is called wolf? How does this system transform such scattered information into the knowledge that dogs are animals, but not conversely? The second example asks: how can a real-time system, initially trained with a few labeled examples and a limited feature set, continue to learn from experience when confronted with oceans of additional information, without eroding reliable early memories? How can such individual systems adapt to their unique application contexts? The third example asks: how can a neural system that has made an error refocus attention on environmental features that it had initially ignored? Three models that address these questions, each based on the distributed adaptive resonance theory (dART) neural network, are applied to a spatial testbed created from multimodal remotely sensed data. The article summarizes key design elements of ART models, and provides links to open-source code for each system and the testbed dataset. WIREs Cogn Sci 2013, 4:707-719. doi: 10.1002/wcs.1260 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.