Results 161 to 170 of about 1,298,312 (212)
Iterative Learning Control for Motion Trajectory Tracking of a Circular Soft Crawling Robot. [PDF]
Front Robot AI, 2019 Chi H, Li X, Liang W, Cao J, Ren Q.
europepmc +1 more source
3D-Printing and Machine Learning Control of Soft Ionic Polymer-Metal Composite Actuators. [PDF]
Sci Rep, 2019 Carrico JD, Hermans T, Kim KJ, Leang KK.
europepmc +1 more source
The Quarterly Journal of Economics, 2011 Summary: This article examines how a decision maker who is only partially aware of his temptations learns about them over time. In facing temptations, individuals use their experience to forecast future self-control problems and choose the appropriate level of commitment.
openaire +3 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Operations Research, 1994
We study the control of a production process which moves at a random time from an in-control state to an out-of-control state where an increased number of defective units is produced. After each unit is produced, a decision maker has three choices: continue production, invest in routine maintenance that restores the process to control, and invest in a
Maqbool Dada, Richard Marcellus
openaire +1 more source
We study the control of a production process which moves at a random time from an in-control state to an out-of-control state where an increased number of defective units is produced. After each unit is produced, a decision maker has three choices: continue production, invest in routine maintenance that restores the process to control, and invest in a
Maqbool Dada, Richard Marcellus
openaire +1 more source
Learning in movement and control
IEEE Transactions on Systems, Man, and Cybernetics, 1989The authors propose novel knowledge representation and reasoning methods that are sufficient to develop a machine that can learn to control any controlled system in the same way that human beings learn: by observing only the input and output of the controlled systems.
Yoshinori Suganuma, Masami Ito
openaire +1 more source
IEEE Robotics & Automation Magazine, 2010
Recent trends in robot learning are to use trajectory-based optimal control techniques and reinforcement learning to scale complex robotic systems. On the one hand, increased computational power and multiprocessing, and on the other hand, probabilistic reinforcement learning methods and function approximation, have contributed to a steadily increasing ...
Stefan Schaal, Christopher G. Atkeson
openaire +1 more source
Recent trends in robot learning are to use trajectory-based optimal control techniques and reinforcement learning to scale complex robotic systems. On the one hand, increased computational power and multiprocessing, and on the other hand, probabilistic reinforcement learning methods and function approximation, have contributed to a steadily increasing ...
Stefan Schaal, Christopher G. Atkeson
openaire +1 more source
Learning in Respiratory Control
Behavior Modification, 2001In this article, it is argued that learning participates to fulfill the metabolic requirements by adapting respiratory control to changing internal and external states. Recent classical conditioning experiments in newborn mice or adult rats showthe close link between conditioned respiratory and arousal responses.
J, Gallego, E, Nsegbe, E, Durand
openaire +2 more sources
Impact sound control by learning control
Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91, 2002Impact sound is emitted when a collision occurs between objects. The authors propose a robotic control method to reproduce the impact sound emitted by the collision between the endpoint of the robotic manipulator and the object. The feedback control of impact sound is very difficult, because the impact phenomenon occurs in a very short period of time ...
Hiroshi Wada +5 more
openaire +1 more source