Learning Inverse Dynamics by Gaussian process Begrression under the Multi-Task Learning Framework

By Dit-Yan Yeung, Yu Zhang. 2009

17 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

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Citations in the corpus

A Survey on Multi-Task Learning (2017, background): “In [209], [210], the inverse dynamics problem for robotics is solved from the perspective of MTL.”
Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition (2010, background): “[21] investigated different training sample sizes for GPR.”
On-line regression algorithms for learning mechanical models of robots: A survey (2011, background): “More interestingly, since the inverse dynamics model is unique but may be involved in the realization of diverse tasks at the kinematic level, one can see the problem of learning this inverse dynamics model through a first task and using it for other tasks as a transfer learning problem [22, 120].”
Incremental local online Gaussian Mixture Regression for imitation learning of multiple tasks (2010, methodology): “…methods to which we compared performances on this dataset are: Gaussian Mixture Regression (GMR, [1] and [22]), Gaussian Process Regression (GPR, [17]), Local Gaussian Process Regression (LGP, [19]), support vector regression (v-SVR, [20]) and Locally Weighted Projection Regression (LWPR, [18]).”
R-IAC: Robust Intrinsically Motivated Exploration and Active Learning (2009, methodology): “allow IAC and R-IAC to be successfully applied in robotic sensorimotor spaces where dynamical information is crucial, such as for example for learning the forward and inverse models of a force controlled high-dimensional robot, for which guided exploration has been identified as a key research targe”