Robohub.org
 

ShanghAI Lectures: Hisato Kobayashi “AI-ish approach for complicated robot control”


by
05 December 2013



share this:

HisatoKobayashiGuest talk in the ShanghAI Lectures, 2009-11-12

If we try to describe any kinds of robot by mathematical models, they must be very complicated equations. Even, such robotic systems are categorized as a conventional dynamic system consist of positive definite inertia matrix, Coriolis’ force term, friction term, gravity term and so on, we cannot derive any authentic control scheme from such complicated nonlinear systems.

Of course, we can make a servo system for a joint control but we do not have any systematic control scheme for whole robots.

https://www.youtube.com/watch?v=6-n6meJXyQo

Hisato Kobayashi was born in Japan 1951. He graduated from Waseda University, he received both master degree and doctoral degree in electrical engineering from Waseda University in 1975 and 1978 respectively. During 1977-1982, he was a research associate of Tokyo University Agriculture and Technology. He joined Hosei University in 1982. He spent almost one year at Stuttgart University Germany 1988-1989 as an invited visiting researcher of Alexander Humboldt Foundatiion. He is now professor of Hosei University Tokyo, department of art and technology,he was president of Hosei University Research Institute, California. During 1998-2001, he was a visiting scholar of Stanford University and doing a joint project entitled “Sleep Smart Project.” He was Editor in Chief of Advanced Robotics, which was issued by Robotics Society in Japan and published by Brill Publisher in Holland. He was elected as fellow of IEEE in 2002. His research interests cover control theory, mechatronics systems, robotics and health care systems.

The ShanghAI Lectures are a videoconference-based lecture series on Embodied Intelligence run by Rolf Pfeifer and organized by me and partners around the world.

The ShanghAI lectures have brought us a treasure trove of guest lectures by experts in robotics. You can find the whole series from 2012 here. Now, we’re bringing you the guest lectures you haven’t yet seen from previous years, starting with the first lectures from 2009 and releasing a new guest lecture every Thursday until all the series are complete. Enjoy!



tags: ,


Nathan Labhart Co-organizing the ShanghAI Lectures since 2009.
Nathan Labhart Co-organizing the ShanghAI Lectures since 2009.

            AUAI is supported by:



Subscribe to Robohub newsletter on substack



Related posts :

These tiny drones are powered by sound

  24 Aug 2026
EPFL engineers have designed acoustic cavities that convert sound waves into thrust, propelling small robots and ultralight aerial vehicles without on-board actuators or electronics.

#AAMAS2026 blue sky award winner: Foundation world models for agents in changing environments

and   21 Aug 2026
Hear from the AAMAS 2026 Best Blue Sky Paper Award winner.

Robotics roadmaps from around the world spotlight of the month: Japan

Robots have been a prolific theme in Japanese pop culture and media since the 1950s

Intermittent swimming promotes the energy efficiency of fish-like robot movements

How zebrafish-inspired robots save energy by swimming in bursts.

What does it take for a robot to hold a conversation with a room, not just a person?

and   14 Aug 2026
Find out about a summer school held at Imperial College London.

Robotics roadmaps from around the world

We embark on a tour into some of the recent and prominent robotics roadmaps from around the world.

A ‘5-in-1’ seed-sized surgical robot

Mini robot can move, cut tissue, release drugs, grip and store samples, and generate heat wirelessly

AI agents create virtual playgrounds to help robots get crucial training data

  07 Aug 2026
“SceneSmith” system uses collaborative AI agents to create realistic 3D environments of places like kitchens, hotels, and living rooms, where robots can simulate everyday chores.



AUAI is supported by:







Subscribe to Robohub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence