155 citations · 374 across the 20 of their papers we have counts for
20 papers
Binary Neural Network in Robotic Manipulation: Flexible Object Manipulation for Humanoid Robot Using Partially Binarized Auto-Encoder on FPGA
Satoshi Ohara, Tetsuya Ogata, Hiromitsu Awano
A neural network based flexible object manipulation system for a humanoid robot on FPGA is proposed. Although the manipulations of flexible objects using robots attract ever increa…
How to select and use tools? : Active Perception of Target Objects Using Multimodal Deep Learning
Namiko Saito, Tetsuya Ogata, Satoshi Funabashi +2
Selection of appropriate tools and use of them when performing daily tasks is a critical function for introducing robots for domestic applications. In previous studies, however, ad…
Embodying Pre-Trained Word Embeddings Through Robot Actions
Minori Toyoda, Kanata Suzuki, Hiroki Mori +2
We propose a promising neural network model with which to acquire a grounded representation of robot actions and the linguistic descriptions thereof. Properly responding to various…
Spatial Attention Point Network for Deep-learning-based Robust Autonomous Robot Motion Generation
Hideyuki Ichiwara, Hiroshi Ito, Kenjiro Yamamoto +2
Deep learning provides a powerful framework for automated acquisition of complex robotic motions. However, despite a certain degree of generalization, the need for vast amounts of…
In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning
Kanata Suzuki, Momomi Kanamura, Yuki Suga +2
In this study, we report the successful execution of in-air knotting of rope using a dual-arm two-finger robot based on deep learning. Owing to its flexibility, the state of the ro…
Stable deep reinforcement learning method by predicting uncertainty in rewards as a subtask
Kanata Suzuki, Tetsuya Ogata
In recent years, a variety of tasks have been accomplished by deep reinforcement learning (DRL). However, when applying DRL to tasks in a real-world environment, designing an appro…