Tetsuya Sawanobori
Papers
5
Total Citations
18
H-Index
2
About
Tetsuya Sawanobori’s research lies at the intersection of robotic manipulation, motion planning, and automation for real-world service applications. His work addresses critical challenges in enabling robots to operate autonomously in unstructured environments, particularly commercial kitchens. Sawanobori has made significant contributions to data-driven motion planning, surveying deep neural networks, reinforcement learning, and large language models for trajectory generation under constraints like collision avoidance. He also pioneered automatic data collection methods for object detection and grasp-position estimation using mobile robots and invisible markers, reducing the time and cost of training deep learning models. His applied work is exemplified by the development of a collaborative robotic dishwasher cell system and the “Dishflipper” mechanism for rinsing and removing food debris, integrated into a fully automated dishwashing system for a pilot soba noodle stand. Additionally, he proposed a single suction grasp detection method using shallow networks trained with synthetic data, optimizing for resource-constrained commercial deployment. With over a dozen citations across his most-cited papers, Sawanobori’s impact is evident in bridging theoretical advances in motion planning and grasping with practical, deployable robotic solutions for the service industry.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5