38 citations · 58 across the 8 of their papers we have counts for
4 papers · 1 filter
Visual Spatial Attention and Proprioceptive Data-Driven Reinforcement Learning for Robust Peg-in-Hole Task Under Variable Conditions
André Yuji Yasutomi, Hideyuki Ichiwara, Hiroshi Ito +2
Anchor-bolt insertion is a peg-in-hole task performed in the construction field for holes in concrete. Efforts have been made to automate this task, but the variable lighting and h…
Real-time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changes
Kenjiro Yamamoto, Hiroshi Ito, Hideyuki Ichiwara +2
While deep learning enables real robots to perform complex tasks had been difficult to implement in the past, the challenge is the enormous amount of trial-and-error and motion tea…
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…
Deep Predictive Learning: Motion Learning Concept inspired by Cognitive Robotics
Kanata Suzuki, Hiroshi Ito, Tatsuro Yamada +2
Bridging the gap between motion models and reality is crucial by using limited data to deploy robots in the real world. Deep learning is expected to be generalized to diverse situa…