What follows is a selection of research with some commentary.
I have a longstanding interest in perceptive and aware environments (Classroom 2000, Aware Home, CareMedia, see the biographical slides mentioned above for more information).
Now I am applying what I have learned from building those systems to robot skin. The key idea is to cover the robot with eyeballs (cameras), and have transparent skin. The current prototype (FingerVision) is described in "Implementing Tactile Behaviors Using FingerVision", A. Yamaguchi and C. G. Atkeson, Humanoids 2017. More papers and videos on FingerVision, slides, and an NSF proposal.
I am interested in robots that are inherently safe, even when control computers crash. It helps to make robots lightweight, and one way to do that is to use inflatable structural elements. An important aspect of this work is the goal of building human-scale soft robots that can interact with humans and operate in human environments. Most soft robotics focuses on much smaller robots. This work led to an outreach effort with a large impact, our work in conjunction with the Disney movie Big Hero 6.
Swing leg retraction helps biped walking stability, M. Wisse, C. G. Atkeson, and D. K. Kloimwieder, 5th IEEE-RAS International Conference on Humanoid Robots, 295-300, Humanoids 2005.
"Open Loop Stable Control Strategies for Robot Juggling",
Schaal, S. and C. G. Atkeson,
In: IEEE International Conference on Robotics and Automation,
Vol.3, pp.913-918, Atlanta, Georgia, 1993.
A look at humans doing the task: "One-handed Juggling: Dynamical Approaches to a Rhythmic Movement Task", Schaal, S., D. Sternad and C. G. Atkeson, Journal of Motor Behavior, 28(2):165-183, 1996.
Below are some efforts in this area.
Being able to learn or plan how to do challenging dynamic tasks on high dimensional humanoid robots is a major challenge. I have emphasized the close links between model-based reinforcement learning, optimization, planning, and control for dynamic tasks. Dynamic programming provides a methodology for developing planners and controllers for nonlinear systems as well as highlighting the importance of the value function, which represents the expected lifetime cost starting in any given state. However, general dynamic programming is currently computationally intractable. I introduced differential dynamic programming (DDP) from optimal control to the field reinforcement learning. DDP is a local trajectory-based optimization method that produces local models of the policy and value function, as well as an optimal trajectory. I showed how, using sets of optimized trajectories, a more global (or at least less vulnerable to bad local minima) optimal policy can be found by having neighboring trajectories share local policy and/or value function information. This work naturally leads to the notion of trajectory libraries. These ideas are also seen in iLQR and LQR-Trees.
"Using Local Trajectory Optimizers to Speed Up Global Optimization in Dynamic Programming", C. G. Atkeson, Proceedings, Neural Information Processing Systems, Denver, Colorado, December, 1993, In: Neural Information Processing Systems 6, J. D. Cowan, G. Tesauro, and J. Alspector, eds. Morgan Kaufmann, 1994. Citeseer entry.
Nonparametric Representation of Policies and Value Functions: A Trajectory-Based Approach, C. G. Atkeson, and J. Morimoto, In: Neural Information Processing Systems 15, MIT Press, 2003. Citeseer entry.
Morimoto and Atkeson have developed robust versions of the local trajectory planner. [IROS 2003]
Policies Based on Trajectory Libraries, M. Stolle and C. G. Atkeson, IEEE International Conference on Robotics and Automation, 3344-3349, 2006.
Transfer of policies based on trajectory libraries, M. Stolle, H. Tappeiner, J. Chestnutt, and C. G. Atkeson, IEEE/RSJ International Conference on Intelligent Robots and Systems, 4234-4240, 2007.
Random Sampling of States in Dynamic Programming, C. G. Atkeson and B. Stephens, in IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, Vol. 38, No. 4, pp. 924-929, 2008.
Standing balance control using a trajectory library, Liu, Chenggang; Atkeson, Christopher G.; IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2009, Pages: 3031 - 3036.
Finding and transferring policies using stored behaviors, M. Stolle and C. G. Atkeson, Autonomous Robots, 29(2): 169-200, 2010.
Learning Control in Robotics, S. Schaal and C. G. Atkeson, IEEE Robotics & Automation Magazine, 17, 20-29, 2010.
Trajectory-Based Dynamic Programming, C. G. Atkeson and C. Liu, in Modeling, Simulation and Optimization of Bipedal Walking Cognitive Systems Monographs Volume 18, 2013, pp 1-15.
A first attempt at learning from demonstration, with parametric model learning: Learning Tasks From A Single Demonstration, C. G. Atkeson and S. Schaal, IEEE International Conference on Robotics and Automation, 1706-1712, 1997.
Using model-free learning to compensate for the limitations of model-based learning: Robot Learning From Demonstration, C. G. Atkeson and S. Schaal, Machine Learning: Proceedings of the Fourteenth International Conference (ICML '97).
Using regularization to make nonparametric model-based reinforcement learning work. Nonparametric Model-Based Reinforcement Learning, C. G. Atkeson, In: Neural Information Processing Systems 10, MIT Press, 1998.
Work by Bentivegna explored learning from demonstration by learning which task primitives to select in any situation. For example: Learning Similar Tasks From Observation and Practice, D. C. Bentivegna, C. G. Atkeson, and G. Cheng, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2677-2683, 2006.
An overview of work on local learning algorithms is given by:
Atkeson, C. G., Moore, A. W., & Schaal, S.
"Locally Weighted Learning." Artificial Intelligence Review, 11:11-73, 1997.
An overview of local learning applied to robots is given by:
Atkeson, C. G., Moore, A. W., & Schaal, S.
"Locally Weighted Learning for Control." Artificial Intelligence Review, 11:75-113, 1997.
Looking at local learning from a neural network point of view:
Atkeson, C. G., and S. Schaal,
Memory-Based Neural Networks For Robot Learning, Neurocomputing, 9(3):243-69, 1995.
A mixture of experts approach to local learning is presented in:
Schaal, S., & Atkeson, C. G.
From Isolation to Cooperation: An Alternative View of a System of Experts In: D.S. Touretzky, and M.E. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8. Cambridge, MA: MIT Press. 1996.
Stefan Schaal, Atkeson, and colleagues have explored new approaches to nonparametric learning, Receptive Field Weighted Regression (RFWR) and Locally Weighted Projection Regression (LWPR), in which receptive fields representing local models are created and maintained during learning. These approaches provide an interesting alternative perspective on locally weighted learning. Unlike the original version of locally weighted learning, these approaches maintain local intermediate data structures such as receptive fields. [Applied Intelligence 2002] [ICRA 2000] [Neural Computation 1998] [NIPS 1997]
Applying local learning to robot learning:
Schaal, S., and C. G. Atkeson,
Robot Juggling: An Implementation of Memory-based Learning, Control Systems Magazine, 14(1):57-71, 1994.
Model-Based Control of a Robot Manipulator, C. H. An, C. G. Atkeson, and J. M. Hollerbach, MIT Press, Cambridge, Massachusetts, 1988.