Journal ArticleDOI
Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning
Pin-Chu Yang,Kazuma Sasaki,Kanata Suzuki,Kei Kase,Shigeki Sugano,Tetsuya Ogata +5 more
- Vol. 2, Iss: 2, pp 397-403
TLDR
A practical state-of-the-art method to develop a machine-learning-based humanoid robot that can work as a production line worker and exhibits the following characteristics: task performing capability, task reiteration ability, generalizability, and easy applicability.Abstract:
We propose a practical state-of-the-art method to develop a machine-learning-based humanoid robot that can work as a production line worker. The proposed approach provides an intuitive way to collect data and exhibits the following characteristics: task performing capability, task reiteration ability, generalizability, and easy applicability. The proposed approach utilizes a real-time user interface with a monitor and provides a first-person perspective using a head-mounted display. Through this interface, teleoperation is used for collecting task operating data, especially for tasks that are difficult to be applied with a conventional method. A two-phase deep learning model is also utilized in the proposed approach. A deep convolutional autoencoder extracts images features and reconstructs images, and a fully connected deep time delay neural network learns the dynamics of a robot task process from the extracted image features and motion angle signals. The “Nextage Open” humanoid robot is used as an experimental platform to evaluate the proposed model. The object folding task utilizing with 35 trained and 5 untrained sensory motor sequences for test. Testing the trained model with online generation demonstrates a 77.8% success rate for the object folding task.read more
Citations
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Journal ArticleDOI
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends
William G. Hatcher,Wei Yu +1 more
TL;DR: A thorough investigation of deep learning in its applications and mechanisms is sought, as a categorical collection of state of the art in deep learning research, to provide a broad reference for those seeking a primer on deep learning and its various implementations, platforms, algorithms, and uses in a variety of smart-world systems.
Journal ArticleDOI
Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges
Arzoo Miglani,Neeraj Kumar +1 more
TL;DR: Various deep learning models for traffic flow prediction in autonomous vehicles are explored and compared with respect to their applicability in modern smart transportation systems.
Journal ArticleDOI
Deep reinforcement learning with smooth policy update: Application to robotic cloth manipulation
TL;DR: Two sample efficient DRL algorithms are proposed that combine the nature of smooth policy update with the capability of automatic feature extraction in deep neural networks to enhance the sample efficiency and learning stability with fewer samples.
Journal ArticleDOI
Self-Supervised Correspondence in Visuomotor Policy Learning
TL;DR: Self-supervised dense visual correspondence training enables visuomotor policy learning with surprisingly high generalization performance with modest amounts of data, and simulated imitation learning experiments suggest that correspondence training offers sample complexity and generalization benefits compared to autoencoding and end-to-end training.
Journal ArticleDOI
Sound source localization using deep learning models
TL;DR: This study shows that with end-to-end training and generic preprocessing, the performance of deep residual networks not only surpasses the block level accuracy of linear models on nearly clean environments but also shows robustness to challenging conditions by exploiting the time delay on power information.
References
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Domain-adversarial training of neural networks
Yaroslav Ganin,Evgeniya Ustinova,Hana Ajakan,Pascal Germain,Hugo Larochelle,François Laviolette,Mario Marchand,Victor Lempitsky +7 more
TL;DR: In this article, a new representation learning approach for domain adaptation is proposed, in which data at training and test time come from similar but different distributions, and features that cannot discriminate between the training (source) and test (target) domains are used to promote the emergence of features that are discriminative for the main learning task on the source domain.