Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning

By Pin-Chu Yang, Kazuma Sasaki, Kanata Suzuki, Kei Kase, Shigeki Sugano, Tetsuya Ogata. 2017

196 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

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In Biomedical Engineering

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Citations in the corpus

Self-Supervised Correspondence in Visuomotor Policy Learning (2019, background): “Sometimes polices are learned with a frozen encoder [2], [9], [10], other times in conjunction with end-to-end training [13].”
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018, background): “[146] developed a deep learning pipeline for generalized non-backdrivable humanoid robots through teleoperation training.”
Using Human Gaze to Improve Robustness Against Irrelevant Objects in Robot Manipulation Tasks (2020, background): “The successful applications of behavior cloning to robotics include [6], [13], [26], [27].”
Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation (2020, methodology): “Incorporating automation into teleoperation has been a key method for improving operator efficiency [6]; using deep learning has enabled automated grasping [8], knot tying [9], and cloth folding [10].”
Deep Imitation Learning of Sequential Fabric Smoothing Policies (2019, background): “Robot manipulation of fabric has applications in senior care and dressing assistance [11], [12], [13], sewing [41], ironing [21], laundry folding [22], [27], [44], [54], fabric upholstery manufacturing [32], [50], and handling gauze in robotic surgery [48].”
Predictive processing as a scalable computational principle for embodied multitask intelligence (2026, unknown): “This perspective has increasingly inspired research in artificial intelligence and robotics, where predictive processing–based neural architectures are being explored as a means of endowing robots with adaptive, brain-like learning capabilities across diverse tasks ( 23 – 32 ).”