Sage Journals: Discover world-class research

Abstract

Humans are very skilled at learning new control tasks, and in particular, the use of novel tools. In this article we propose a paradigm that utilizes this sensorimotor learning capacity to obtain robot behaviors, which would otherwise require manual programming by experts. The concept is to consider the target robot platform as a tool to be controlled intuitively by a human. The human is therefore provided with an interface designed to make the control of the robot intuitive, and learns to perform a given task using the robot. This is akin to the stage where a beginner learns to drive a car. After human learning, the skilled control of the robot is used to build an autonomous controller so that the robot can perform the task without human guidance. We demonstrate the feasibility of this proposal for humanoid robot skill synthesis by showing how a statically stable reaching skill can be obtained by means of this framework. In addition, we analyze the feedback interface component of this paradigm by examining a dynamics task, in which a human learns to use the motion of the body to control the posture of an inverted pendulum that approximates a humanoid robot, so that it stays upright.

Keywords

Sensorimotor learning humanoid robot balanced reaching posture control

Get full access to this article

View all access options for this article.

References

Atkeson, C. , Hale, J. , Pollick, F. , Riley, M. , Kotosaka, S. , Schaal, S. et al. (2000). Using humanoid robots to study human behavior . IEEE Intelligent Systems, 15, 45-56.

Bentivegna, D. , Atkeson, C. , & Cheng, G. ( 2004). Learning tasks from observation and practice. Robotics and Autonomous Systems, 47, 163-169.

Billard, A. , & Hayes, G. ( 1999). Drama, a connectionist architecture for control and learning in autonomous robots. Adaptive Behavior, 7, 35-63.

Billard, A. , & Siegwart, R. ( 2004). Robot learning from demonstration. Robotics and Autonomous Systems, 47, 65-67.

Boone, D. , & Azen, S. ( 1979). Normal range of motion of joints in male subjects. The Journal of Bone and Joint Surgery, 61, 756-759.

Breazeal, C. , & Scassellati, B. (2002). Robots that imitate humans . Trends in Cognitive Sciences, 6, 481-487.

Chen, J. , Haas, E. , & Barnes, M. ( 2007). Human performance issues and user interface design for teleoperated robots. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37, 1231-1245.

Fitzpatrick, R.C. , Gorman, R.B. , Burke, D. , & Gandevia, S.C. (1992). Postural proprioceptive reflexes in standing human subjects: bandwidth of response and transmission characteristics. Journal of Physiology, 458, 69-83.

Goldberg, G. , & Hagmann, S. ( 1998). Tool use and mechanical problem solving in apraxia. Neuropsychology, 36, 581-589.

10.

Imamizu, H. ( 2010). Mechanisms of human sensorimotor-learning and their implications for brain communication. IEICE Transactions on Communications (Vol E91.B, pp. 2102-2108).

11.

Ito, M. , & Tani, J. ( 2004). On-line imitative interaction with a humanoid robot using a dynamic neural network model of a mirror system. Adaptive Behavior, 12, 93-115.

12.

Kushida, D. , Nakamura, M. , Goto, S. , & Kyura, N. ( 2001). Human direct teaching of industrial articulated robot arms based on force-free control. Artificial Life and Robotics , 5, 26-32.

13.

Oztop, E. , Lin, L.-H. , Kawato, M. , & Cheng, G. ( 2006). Dexterous skills transfer by extending human body schema to a robotic hand. In Paper presented at the IEEE-RAS International Conference on Humanoid Robots. Genova, Italy.

14.

Oztop, E. , Lin, L.-H. , Kawato, M. , & Cheng, G. ( 2007). Extensive human training for robot skill synthesis: Validation on a robotic hand. In Paper presented at the IEEE International Conference on Robotics and Automation. Roma , Italy.

15.

Park, J. , & Sandberg, I.W. (1991). Universal approximation using radial-basis-function networks. Neural Computation, 3(2), 246-257.

16.

Schaal, S. ( 1999). Is imitation learning the route to humanoid robots? Trends in Cognitive Sciences, 3, 233-242.

17.

Shadmehr, R. , & Wise, S. ( 2005). Computational neurobiology of reaching and pointing: A foundation for motor learning. Cambridge, MA: MIT Press.

18.

Shidara, M. , Kawano, K. , Gomi, H. , & Kawato, M. ( 1993). Inverse-dynamics model eye movement control by Purkinje cells in the cerebellum. Nature, 365, 50-52.

19.

Steinfeld, A. , Fong, T. , Kaber, D. , Lewis, M. , Scholtz, J. , Schultz, A. et al. (2006). Common metrics for human-robot interaction . In Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction. Salt Lake City, USA (pp. 33-40).

20.

Ude, A. , Gams, A. , Asfour, T. , & Morimoto, J. ( 2010). Task-specific generalization of discrete and periodic dynamic movement primitives. IEEE Transactions on Robotics , 26(5), 800-815.

21.

Wolpert, D. , & Ghahramani, Z. (2000). Computational principles of movement neuroscience. Nature Neuroscience, 3 Suppl, 1212-1217.

22.

Wolpert, D. , Ghahramani, Z. , & Flanagan, J. ( 2001). Perspectives and problems in motor learning. Trends in Cognitive Sciences, 5(11), 487-494.

Human sensorimotor learning for humanoid robot skill synthesis

Abstract

Keywords

Get full access to this article

References