Max Robotics — Global Service Robot Directory + US Certification

About

Takeyuki Kotaka is a robotics researcher focused on automating laboratory workflows, particularly the manipulation and recognition of test tubes—a critical bottleneck in clinical and biomedical settings. His work bridges task and motion planning, computer vision, and deep learning to enable robots to handle delicate labware with precision. Kotaka’s most impactful contribution, “Arranging test tubes in racks using combined task and motion planning” (2021, 16 citations), demonstrates a system that integrates high-level task sequencing with low-level motion control, reducing human labor in high-volume clinical labs. He further advanced automated data generation in “Automatically Prepare Training Data for YOLO Using Robotic In-Hand Observation and Synthesis” (2023, 10 citations), tackling the labor-intensive labeling bottleneck by having robots autonomously collect and annotate training images. His 2025 work on zero-shot test tube type recognition eliminates the need for pre-labeled datasets entirely, using clustering and global prediction. Kotaka’s research has consistently pushed toward fully autonomous lab robotics, with recent extensions into reinforcement learning for multi-class rearrangement (2024). His cumulative work, cited over 30 times, is shaping the next generation of intelligent laboratory automation, promising safer, faster, and more scalable clinical workflows.