Tokyo University developed a limb control robot
As Japan's leading national university, the University of Tokyo has long relied heavily on government funding for its operations. Between 1979 and 1980, the Japanese government allocated a significant 80 billion yen to the university, and this financial support has continued to grow over the years. The institution now maintains an annual budget exceeding 100 billion yen, allowing it to invest in cutting-edge research and development across multiple disciplines.
Thanks to this strong governmental backing and its world-class academic reputation, the University of Tokyo has made remarkable contributions to the field of robotics. One of its earliest breakthroughs came in 2001 when it developed the world’s first animal-like robot — a wing-cutting robot — with the government investing 3.4 million pounds in the project. This marked a major milestone in bio-inspired robotics.
In 2003, researchers from the Graduate School of Information Science and Technology at the University of Tokyo created a humanoid robot capable of autonomously transitioning from a lying position to a standing one — a feat that was unprecedented at the time. Then, in 2006, Takao Someya and his team introduced an electronic skin embedded in a flexible plastic film, enabling robots to sense pressure and temperature in a way similar to human touch.
From the HRP-2 housekeeping robot, which efficiently delivers tea, to robots capable of picking apples, the University of Tokyo has consistently captured the attention of both the industry and the public. These innovations highlight the university's leadership in intelligent robotics and its commitment to pushing technological boundaries.
Recently, the University of Tokyo once again became a hot topic in the robotics community. Engineer Tsuyoshi Horo developed a groundbreaking control system that uses gestures and body language to guide robotic movements. According to reports, the system relies on a network of cameras placed around the environment to track and interpret user actions, making the interaction more intuitive and natural. This advancement could significantly impact future applications in human-robot collaboration and automation.
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