Details

Autor(en) / Beteiligte

• Pin-Chu Yang
• Sasaki, Kazuma
• Suzuki, Kanata
• Kase, Kei
• Sugano, Shigeki
• Ogata, Tetsuya

Titel

Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning

Ist Teil von

• IEEE robotics and automation letters, 2017-04, Vol.2 (2), p.397-403

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Quelle

IEL

Beschreibungen/Notizen

• 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.