no code implementations • NeurIPS 2016 • Boris Belousov, Gerhard Neumann, Constantin A. Rothkopf, Jan R. Peters
In this paper, we show that interception strategies appearing to be heuristics can be understood as computational solutions to the optimal control problem faced by a ball-catching agent acting under uncertainty.
no code implementations • NeurIPS 2015 • Abbas Abdolmaleki, Rudolf Lioutikov, Jan R. Peters, Nuno Lau, Luis Pualo Reis, Gerhard Neumann
Stochastic search algorithms are general black-box optimizers.
no code implementations • NeurIPS 2013 • Alexandros Paraschos, Christian Daniel, Jan R. Peters, Gerhard Neumann
In order to use such a trajectory distribution for robot movement control, we analytically derive a stochastic feedback controller which reproduces the given trajectory distribution.
no code implementations • NeurIPS 2012 • Abdeslam Boularias, Jan R. Peters, Oliver B. Kroemer
We present a new graph-based approach for incorporating domain knowledge in reinforcement learning applications.
no code implementations • NeurIPS 2011 • Oliver B. Kroemer, Jan R. Peters
In this paper, we consider the problem of policy evaluation for continuous-state systems.
no code implementations • NeurIPS 2010 • Mauricio Alvarez, Jan R. Peters, Neil D. Lawrence, Bernhard Schölkopf
Latent force models encode the interaction between multiple related dynamical systems in the form of a kernel or covariance function.
no code implementations • NeurIPS 2010 • Silvia Chiappa, Jan R. Peters
Many time-series such as human movement data consist of a sequence of basic actions, e. g., forehands and backhands in tennis.
no code implementations • NeurIPS 2008 • Jens Kober, Jan R. Peters
We compare this algorithm to alternative parametrized policy search methods and show that it outperforms previous methods.
no code implementations • NeurIPS 2008 • Gerhard Neumann, Jan R. Peters
Recently, fitted Q-iteration (FQI) based methods have become more popular due to their increased sample efficiency, a more stable learning process and the higher quality of the resulting policy.
no code implementations • NeurIPS 2008 • Duy Nguyen-Tuong, Jan R. Peters, Matthias Seeger
Inspired by local learning, we propose a method to speed up standard Gaussian Process regression (GPR) with local GP models (LGP).
no code implementations • NeurIPS 2008 • Silvia Chiappa, Jens Kober, Jan R. Peters
Motor primitives or motion templates have become an important concept for both modeling human motor control as well as generating robot behaviors using imitation learning.