Max Robotics — Global Service Robot Directory + US Certification

About

Koki Yamane is a robotics researcher advancing the frontier of dexterous manipulation and perception, with a focus on enabling robots to handle both soft and rigid objects with human-like precision. His work integrates imitation learning, bilateral control, and novel hardware design to tackle long-standing challenges in robotic grasping and insertion tasks. Yamane’s most cited paper, “Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learning” (2023, 23 citations), demonstrates how AI-driven algorithms can learn force-sensitive manipulation from human demonstrations—critical for grasping unknown objects or using tools. He has also made notable contributions to perception, such as in “SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects” (2024, 8 citations), addressing the difficult problem of depth sensing for transparent objects using neural radiance fields. His hardware innovations include the “Four-Axis Adaptive Fingers Hand for Object Insertion: FAAF Hand” (2024, 2 citations), which incorporates compliance mechanisms for fine-grained assembly tasks. Yamane’s cross-disciplinary approach—combining learning, control, and mechanical design—positions him as a rising figure in robotics, with his work directly impacting applications in manufacturing, service robotics, and human-robot collaboration.