Max Robotics — Global Service Robot Directory + US Certification

About

Daichi Saito is a robotics researcher advancing the frontier of robot teaching and manipulation, with a focus on how machines can learn from human demonstration. His work centers on three interconnected challenges: task-grasping, where a robot must select a grasp that supports the entire task context rather than just picking up an object; multimodal robot teaching, which integrates visual, textual, and haptic cues; and mixed reality (MR) interfaces for intuitive human-robot interaction. Saito’s most influential paper, “Task-grasping from a demonstrated human strategy” (2022, 11 citations), introduces a Learning-from-Observation framework that captures the implicit knowledge in human grasps, enabling robots to perform context-aware manipulation. He has also pioneered the use of object affordance—the prior distribution of grasp types for each object—to guide grasp-type recognition, and developed a Contact Web Status Presentation system for freehand grasping in MR-based teaching. His task-sequencing simulator (2023) integrates machine learning with execution simulation, bridging the gap between training and real-world performance. With a growing citation record and a clear trajectory toward more intuitive, context-aware robotics, Saito is shaping how robots learn from and collaborate with humans.