Found 5 results.




movement imitation with nonlinear dynamical systems in humanoid robots

Author(s): Auke Jan Ijspeert, Jun Nakanishi, Stefan Schaal
Venue: IEEE International Conference on Robotics and Automation (ICRA)
Year Published: 2002
Keywords: probabilistic models, nonlinear systems, dynamical systems, learning from demonstration, humanoid robotics
Expert Opinion: First work that proproses practical movement primitive representation for robotics. Very concise paper: shows how much can be packed into 6 pages.

applied nonlinear control

Author(s): Jean-Jacques E Slotine, Weiping Li
Venue: Book
Year Published: 2001
Keywords: nonlinear systems, optimal control
Expert Opinion: It laid the basis for adaptive nonlinear control commonly used in robotic control.

iterative linearization methods for approximately optimal control and estimation of non-linear stochastic system

Author(s): W. LI, E. TODOROV
Venue: International Journal of Control
Year Published: 2007
Keywords: planning, nonlinear systems, optimal control, dynamical systems, state estimation
Expert Opinion: This paper presents one of the effective and fundamental optimal control framework for nonlinear systems. This framework (including DDP) and its extensions have been widely applied to in motion planning and generation in complex robotic systems. This work is quite influential in the field of motion generation and control of robotic systems. After the publication of this paper, there have been many follow-up studies on the application of this kind of optimal control approaches to robot motion control.

dynamical movement primitives: learning attractor models for motor behaviors

Author(s): Auke Jan Ijspeert, Jun Nakanishi, Heiko Hoffmann, Peter Pastor, Stefan Schaal
Venue: Neural Computation (Volume 25, Issue 2)
Year Published: 2013
Keywords: planning, learning from demonstration, dynamical systems, nonlinear systems
Expert Opinion: Dynamic Movement Primitives (DMPs) specify a way to model goal-directed behaviours as non-linear dynamical system with a learnable attractor behaviour. In this way, the movement trajectory can be of almost arbitrary complexity but remains well-behaved and stable. DMPs are interesting for robot learning as they provide a simple way to learn from demonstrations. The forcing term that shapes the movement trajectory is linear in a set of learnable weights. Any function approximator can be used to learn these parameters. Locally weighted regression has been of particular interest as it is a very easy one-shot learning procedure.

a survey of iterative learning control

Author(s): D.A. Bristow, M. Tharayil, A.G. Alleyne
Venue: IEEE Control Systems Magazine (Volume 26, Issue 3)
Year Published: 2006
Keywords: learning from demonstration, survey, nonlinear systems, gaussians
Expert Opinion: The content of the paper provides the reader with a broad perspective of the important ideas, potential, and limitations of iterative learning control - ILC. Besides the design techniques, it discusses problems in stability, performance, learning transient behavior, and robustness.