50 citations · 135 across the 5 of their papers we have counts for
5 papers
Learning Multimodal Attention for Manipulating Deformable Objects with Changing States
Namiko Saito, Mayu Tatsumi, Ayuna Kubo +4
To support humans in their daily lives, robots are required to autonomously learn, adapt to objects and environments, and perform the appropriate actions. We tackled on the task of…
Multi-Fingered In-Hand Manipulation with Various Object Properties Using Graph Convolutional Networks and Distributed Tactile Sensors
Satoshi Funabashi, Tomoki Isobe, Fei Hongyi +4
Multi-fingered hands could be used to achieve many dexterous manipulation tasks, similarly to humans, and tactile sensing could enhance the manipulation stability for a variety of…
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…
Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability
Zhihao Li, Toshiyuki Motoyoshi, Kazuma Sasaki +2
Current end-to-end deep learning driving models have two problems: (1) Poor generalization ability of unobserved driving environment when diversity of training driving dataset is l…
Detecting Features of Tools, Objects, and Actions from Effects in a Robot using Deep Learning
Namiko Saito, Kitae Kim, Shingo Murata +2
We propose a tool-use model that can detect the features of tools, target objects, and actions from the provided effects of object manipulation. We construct a model that enables r…