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TSMC
2008
95views more  TSMC 2008»
13 years 8 months ago
Natural Movement Generation Using Hidden Markov Models and Principal Components
Recent studies have shown that the perception of natural movements--in the sense of being "humanlike"--depends on both joint and task space characteristics of the movemen...
Junghyun Kwon, Frank C. Park
NN
2010
Springer
125views Neural Networks» more  NN 2010»
13 years 6 months ago
Parameter-exploring policy gradients
We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
Frank Sehnke, Christian Osendorfer, Thomas Rü...
IROS
2006
IEEE
113views Robotics» more  IROS 2006»
14 years 2 months ago
Policy Gradient Methods for Robotics
— The aquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-struc...
Jan Peters, Stefan Schaal
ICML
2009
IEEE
14 years 9 months ago
Robot trajectory optimization using approximate inference
The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to ...
Marc Toussaint
ACHI
2010
IEEE
14 years 3 months ago
A Simulation Framework for Human-Robot Interaction
Abstract—The development of human-robot interaction scenarios is a strongly situation-dependent as well as an extremely dynamic task. Humans interacting with the robot directly r...
Norbert Schmitz, Jochen Hirth, Karsten Berns