15 citations · 15 across the 6 of their papers we have counts for
8 papers · 1 filter
Achieving Faster and More Accurate Operation of Deep Predictive Learning
Masaki Yoshikawa, Hiroshi Ito, Tetsuya Ogata
Achieving both high speed and precision in robot operations is a significant challenge for social implementation. While factory robots excel at predefined tasks, they struggle with…
Sensorimotor Attention and Language-based Regressions in Shared Latent Variables for Integrating Robot Motion Learning and LLM
Kanata Suzuki, Tetsuya Ogata
In recent years, studies have been actively conducted on combining large language models (LLM) and robotics; however, most have not considered end-to-end feedback in the robot-moti…
A Peg-in-hole Task Strategy for Holes in Concrete
André Yuji Yasutomi, Hiroki Mori, Tetsuya Ogata
A method that enables an industrial robot to accomplish the peg-in-hole task for holes in concrete is proposed. The proposed method involves slightly detaching the peg from the wal…
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…
Interactively Robot Action Planning with Uncertainty Analysis and Active Questioning by Large Language Model
Kazuki Hori, Kanata Suzuki, Tetsuya Ogata
The application of the Large Language Model (LLM) to robot action planning has been actively studied. The instructions given to the LLM by natural language may include ambiguity an…
Force Map: Learning to Predict Contact Force Distribution from Vision
Ryo Hanai, Yukiyasu Domae, Ixchel G. Ramirez-Alpizar +2
When humans see a scene, they can roughly imagine the forces applied to objects based on their experience and use them to handle the objects properly. This paper considers transfer…