Neural Systems Laboratory

Our research focuses on understanding the brain using computational models and simulations, and applying this knowledge to the task of building intelligent robotic systems and brain-computer interfaces (BCIs). We utilize data and techniques from a variety of fields, ranging from neuroscience and psychology to machine learning and statistics. Current efforts are directed at: (1) understanding probabilistic information processing and learning in the brain, (2) building biologically-inspired robots that can learn through experience and imitation, and (3) developing interfaces for controlling computers and robots using brain- and muscle-related signals.

Press Coverage

Human-Robot Interaction (2010)

Babies and social robots (press release)

o Article in Neural Networks

o Popular Science, Slashdot, Daily Tech

o Mumbai Mirror, USA Today, NY Magazine

o Engadget, io9, Laptop magazine

New York Times (mentions our robot Morphy)

Brain-Computer Interfacing (2010)

Neural Augmentation by Brain-Computer Interfacing (2010)

Brain-Controlled Humanoid Robot (2007)

CBS News (News video)	ABC News (Video)	DISCOVER Magazine
Popular Mechanics	The Telegraph	Discovery Channel
National Public Radio	Seattle Times	King 5 TV News
The Motley Fool

Projects and Publications

Probabilistic models of the brain

Decision making under uncertainty: A neural model based on POMDPs (R. Rao, Frontiers in Computational Neuroscience, 2010)
Neural models of Bayesian belief propagation (R. Rao, Bayesian Brain, 2007)
Hierarchical Bayesian inference in networks of spiking neurons (R. Rao, Advances in NIPS 17, pp. 1113-20, 2005).
Bayesian computation in recurrent neural circuits (R. Rao, Neural Computation, 16(1), 1-38, 2004)
Probabilistic Models of the Brain: Perception and Neural Function
Other publications

Learning and humanoid robotics

Imitation Learning with Hierarchical Actions (A. Friesen and R. Rao, IEEE International Conference on Development and Learning, 2010)
Learning to Imitate Human Actions through Eigenposes (R. Chalodhorn and R. Rao, in From Motor Learning to Interaction Learning in Robots, 357-381, 2010)
Learning Actions through Imitation and Exploration: Towards Humanoid Robots That Learn from Humans (D. Grimes and R. Rao, in Creating Brain-Like Intelligence, 103-138, 2009)
Large margin Boltzmann machines (X. Miao and R. Rao, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'09), 1156-1162, 2009)
Learning nonparametric policies by imitation (D. Grimes and R. Rao, Proceedings of the 2008 IEEE International Conference on Intelligent Robots and Systems (IROS), 2008)
Learning full-body motions from monocular vision: Dynamic imitation in a humanoid robot (J. Cole et al., Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 240-246, 2007)
Learning to walk through imitation (R. Chalodhorn et al., Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'07), 2084-2090, 2007)
Imitation learning using graphical models (D. Verma and R. Rao, Proceedings of the 2007 European Conference on Machine Learning (ECML), 757-764, 2007)
Towards a real-time Bayesian imitation system for a humanoid robot (A. Shon et al., Proceedings of the 2007 International Conference on Robotics and Automation (ICRA), 2847-2852, 2007)
Active imitation learning (A. Shon et al., Proceedings of the 2007 Conference of the American Association for Artificial Intelligence (AAAI), 756-762, 2007)
Learning nonparametric models for probabilistic imitation (D. Grimes et al., Advances in Neural Information Processing Systems 19 (NIPS'06), 521-528, 2007)
Learning the Lie groups of visual invariance (X. Miao and R. Rao, Neural Computation, 19(10):2665-2693, 2007)
Planning and acting in uncertain environments using probabilistic inference (D. Verma and R. Rao, Proceedings of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2006)
Dynamic imitation in a humanoid robot through nonparametric probabilistic inference (D. Grimes et al., Proceedings of the 2006 Robotics Science and Systems Conference, 2006)
A probabilistic model of gaze imitation and shared attention (Hoffman et al., Neural Networks 19(3):299-310, 2006)
A Bayesian model of imitation in infants and robots (Rao et al., Imitation and Social Learning in Robots, Humans, and Animals, Cambridge University Press, 2005)