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Abstract: Currently, we have
been conducting a project to develop artistic robots. In the
first half of my talk, I will overview our previous project on
dancing humanoid to learn how to dance through observing human
performance. Human dance motions are recorded using optical or
magnetic motion-capture systems. These captured motions are
segmented into tasks using motion analysis, music information,
and task-and-skill models. We can characterize personal
differences of dance using task-and-skill models. Then, we can
map these motion models onto robot motions by considering
dynamic and structural differences between human and robot
bodies. As a demonstration of our system, |
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I will show a video in which a humanoid robot performs two
Japanese folk dances, Jongara-bushi and Aizu-bandaisan-odori.
and then, examine what were the missing sciences toward artistic
robots. In the second half of my talk, I will present our
on-going painter robot project to solve these issues.
Reference:
Learning-from-observation: Ikeuchi and Suehiro (1994)
“Toward an assembly plan from observation,” IEEE Trans Robotics
and Automation, 10(3): 368-385.
Lower body task models: S. Nakaoka et. al. (2007)
“Learning from Observation Paradigm: Leg Task Models for
Enabling a Biped Humanoid Robot to Imitate Human Dances" The
International Journal of Robotics Research, Vol. 26, No. 8, pp.
829-844, 2007.
Upper body task models: Shiratori et. al. (2006)
“Dancing-to-music character animation,” Eurographics2006.
Painting robots: Kudho et. al. (2006) “Painting robot
with multi-fingered hand and stereo vision,” IEEE Conf. MFI.
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Biography:
Dr. Katsushi Ikeuchi is a Professor at the
University of Tokyo. He received a Ph.D. degree in Information
Engineering from the University of Tokyo in 1978. After working at
MIT’s AI Lab for two years, ETL, Japan for five years, and CMU for ten
years, he joined the university in 1996. His research interest spans
computer vision, robotics, and computer graphics. He has received
several awards, including ICCV David Marr award and IEEE R&A K-S Fu
Memorial Best Transaction Paper award. He has been elected as a fellow
of IEEE since 1998. He is a distinguished lecturer of IEEE RAS this
year.
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