Christopher G. Atkeson
Also known as Chris Atkeson
Christopher G. Atkeson is a professor in the Robotics Institute at Carnegie Mellon University, where he studies how robots learn tasks from instruction, web searches, observation, and practice. He earned a Harvard degree in 1981 and an MIT doctorate in brain and cognitive science in 1986, then held faculty posts at MIT and Georgia Tech before joining Carnegie Mellon in 2000. His 1997 paper on robot learning from demonstration appeared in the proceedings of ICML '97. His soft-robotics work helped influence the 2014 Disney film Big Hero 6, and he consulted on the design of Baymax.
- Works
- 228
- Citations
- 19,467
- h-index
- 66
Publication numbers from OpenAlex, as of 15 October 2026.
Timeline
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1959–1981
Early life and Harvard degrees
He is born in Washington, D.C., in 1959. He graduates summa cum laude from Harvard in 1981 with an A.B. in biochemistry.
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1986
An MIT doctorate and faculty post
He completes an MIT doctorate in brain and cognitive science in 1986, advised by Emilio Bizzi, and joins the MIT faculty.
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1988–1991
Young investigator and Sloan awards
He holds a National Science Foundation Presidential Young Investigator Award from 1988 to 1993 and an Alfred P. Sloan Research Fellowship from 1989 to 1991.
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1994–2000
Associate professor at Georgia Tech
He becomes an associate professor in the College of Computing at Georgia Institute of Technology in 1994, and he holds that post until 2000.
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1997
Learning from demonstration
In 1997 he and S. Schaal publish 'Robot learning from demonstration' in the proceedings of ICML '97.
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Since 2000
Robotics Institute at Carnegie Mellon
He joins the Robotics Institute at Carnegie Mellon University in 2000, after posts at MIT and Georgia Tech.
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2014
Soft robotics in Big Hero 6
His soft-robotics work helps influence the 2014 Disney film Big Hero 6, and he consults with its production team on the design of Baymax.
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As of October 2026
Robots learning human techniques
He is a professor in the Robotics Institute at Carnegie Mellon, and his RAI Institute work captures human techniques so that robots can watch, understand, and then perform tasks. His current research focus is enabling robots to learn tasks from direct instruction, web searches, observation and practice.
Sources and credits
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