Skip to main content

WABOT-1

The very first humanoid robot called WABOT-1 was developed by a team of people at Waseda University in Japan in 1973. The evolution of robots was going through a new transformation now-a-robot was made to look and function almost like a human being. WABOT-1 consisted of a head, torso, arms, and legs, and could walk, move its arms, and pick up objects with its hands.

While the other robotic creations of the time were sort of elementary in comparison, WABOT-1 could almost perform like a human: Motor abilities helped it navigate through obstacles undetected; it also listened and responded to simple verbal commands, making it one of a few that has, in a certain sense, interacted with humans. Developed in the infant days of robotics and controlled by an arsenal of motors combined with primitive seeds of artificial intelligence, WABOT-1, too, could operate rudimentarily on an independent level.

If seen in contrast to the present-day robots, WABOT-1 appears primitive, except that it was another base for future robot development, particularly in human robotics. This was a breakthrough for robots passing around walking and moving and behaving humanly; it nurtured more advanced and interactive designs of robots in the future. In the past few decades, AI semiconductor, sensor, and robotics technology has arrived at a stage that enables the birth of human-like machines, such as ASIMO from Honda and Sophia from Hanson Robotics, which perform much more complex acts (for example, voice recognition and facial expression). 

In short, WABOT-1 was the first artificial representation of a mechanical beam of light for human-machine interaction in modern times.

Comments

Popular posts from this blog

Infosys Springboard Internship 6.0

Infosys Springboard Internship 6.0 – A Move towards Practicum Learning Infosys Springboard Internship 6.0 is a cutting-edge initiative to bridge the gap between learning at school and industry needs. This online, project-based internship is geared towards undergraduate students and is a perfect platform for acquiring real-time exposure to technology and digital innovation. The program runs for approximately eight weeks and is aimed at creating technical, problem-solving, as well as professional skills through mentorship and hands-on projects. One of the key features of Internship 6.0 is its domain flexibility. Students have a variety of currently popular domains such as Artificial Intelligence and Machine Learning, Java Development, Web Development, Python Programming, and Business Intelligence through Data Visualization to choose from. This allows the students to customize the internship based on their professional ambitions and personal interests, which enhances the relevance and int...

Git and Github

Git and GitHub are basic tools for modern software development, providing a means of implementing version control and collaboration and facilitating the whole development process. It's a distributed version control system that lets developers see and manage the changes that happened in the codebase over time. It tracks all changes made to the codebase, allowing developers to roll back previous versions, work more efficiently, and record their project history. This works locally on a developer's computer, allowing the person to work separately and synchronize their changes with a central repository afterward.  On the other hand, GitHub is a cloud service hosting Git repositories, where developers can collaborate on their work more easily and share code with others. It offers a social layer over Git where developers can create public or private repositories, manage issues, process pull requests, and work with other people on open-source projects. Besides this, it also provides wi...

The Transformative Power of Generative AI in Medical Chatbots

The Transformative Power of Generative AI in Medical Chatbots Generative AI, specifically large language models (LLMs), are currently changing healthcare chatbots in ways that allow for catching up the limitations of traditional cartable based systems. LLMs excel at understanding complex language, something necessary in healthcare, since patients' questions are often vague and ambiguous. Unlike rigid rule-based chatbots, LLMs - trained on very large datasets - understand how to interprete human language, even when patients speak in non-medical, colloquial terms. Comparatively, using this improved understanding enables chatbots to respond more effectively and meaningfully without the responses being pre-scripted and is used, medical, information of the given inquiry. LLMs can review and analyze all of the large amounts of medical knowledge, as well as, the vast amounts of patient interaction data used to enable LLMs to respond in a specific manner for uniquely wanting to yield the m...