Incremental Semiparametric Inverse Dynamics Learning
January 18, 2016 Β· Declared Dead Β· π IEEE International Conference on Robotics and Automation
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Raffaello Camoriano, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, Francesco Nori
arXiv ID
1601.04549
Category
stat.ML: Machine Learning (Stat)
Cross-listed
cs.LG,
cs.RO
Citations
51
Venue
IEEE International Conference on Robotics and Automation
Last Checked
2 months ago
Abstract
This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. This yields to an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Machine Learning (Stat)
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Distilling the Knowledge in a Neural Network
R.I.P.
π»
Ghosted
Layer Normalization
R.I.P.
π»
Ghosted
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
R.I.P.
π»
Ghosted
Domain-Adversarial Training of Neural Networks
R.I.P.
π»
Ghosted
Deep Learning with Differential Privacy
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted