- CLMC Technical Report Number: TR-CLMC-2007-1 Learning an Outlier-Robust Kalman Filter
- 5 Approximate Nearest Neighbor Regression in Very High Dimensions
- Movement generation by learning from demonstration and generalization to new targets
- Predicting EMG Activity from Neural Firing in M1 with Bayesian Backfitting
- Relative Entropy Policy Search March 17, 2007
- Towards Machine Learning of Motor Skills Jan Peters1,2
- Learning Nonlinear Dynamical Systems Models
- Neural Networks ( ) Contents lists available at ScienceDirect
- Inertial Parameter Estimation of Floating Base Humanoid Systems using Partial Force Sensing
- Kernel Carpentry for Online Regression using Randomly Varying Coefficient Narayanan U Edakunni
- UNCERTAIN 3D FORCE FIELDS IN REACHING MOVEMENTS: DO HUMANS FAVOR ROBUST OR AVERAGE PERFORMANCE?
- Using Variational Bayesian Least Squares for EMG Data Prediction from M1 and Premotor Cortex Firing
- Learning an Outlier-Robust Kalman Filter Jo-Anne Ting1
- Automatic Outlier Detection: A Bayesian Approach Jo-Anne Ting, Aaron D'Souza Stefan Schaal
- A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics
- Parametric and Non-parametric approaches for Non-Linear Tracking of Moving Objects
- Computational motor control in humans and robots Stefan Schaal and Nicolas Schweighofer
- Linear and NonLinear Estimation Methods Applied to the Hemodynamic model
- Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares
- LETTER Communicated by Lehel Csato Incremental Online Learning in High Dimensions
- TOWARDS TRACTABLE PARAMETER-FREE STATISTICAL LEARNING Aaron Angelo D'Souza
- Schaal S (2002) Arm and hand movement control. In: Arbib MA (ed) The handbook of brain theory and neural networks, 2nd Edition. MIT Press, Cambridge, MA, pp 110-113
- Submitted to: IEEE International Conference on Humanoid Robotics Nonlinear Dynamical Systems as Movement Primitives
- Vijayakumar, S, Schaal, S (2000). Fast and efficient incremental learning for high-dimensional movement systems, International Conference on Robotics and Automation (ICRA2000). San Francisco, April 2000.
- Schaal, S. (in press). "Nonparametric regression for learning nonlinear transformations." In: Ritter, H., Holland, O., Mhl, B. (eds.). Prerational Intelligence in Strategies, High-Level Processes and Collective Behavior. Kluwer Academic
- Schaal, S., Vijayakumar, S., & Atkeson, C. G. (1998). "Local dimensionality reduction". In: Jor-dan, M. I., Kearns, M. J., & Solla, S. A. (Eds.): Advances in Neural Information Processing Sys-
- Vijayakumar, S., & Schaal, S. (1998). Local adaptive subspace regression. Neural Processing Letters, 7, 139-149.
- Local Dimensionality Reduction For Locally Weighted Sethu Vijayakumaryz and Stefan Schaalxz
- Schaal. S. & Atkeson, C. G. (1996). In: D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (eds.): Ad-vances in Neural Information Processing Systems 8, pp. 605-611. Cambridge, MA: MIT Press.
- In: V. Graefe (Ed.): Intelligent Robots and Systems 1994 (IROS 1994). Amsterdam: Elsevier Science, 1995. Robot Learning By Nonparametric Regression
- To appear in: Proceedings of the Conference on Prerational Intelligence--Adaptive and Learning Behavior, Bielefeld, Germany, April 1994
- In: IEEE International Conference on Robotics and Automation, 3, pp.913-918, Georgia, Atlanta. Open Loop Stable Control Strategies for Robot Juggling
- Neural Process Lett DOI 10.1007/s11063-009-9098-0
- A Bayesian Approach to Empirical Local Linearization For Robotics Jo-Anne Ting, Aaron D'Souza, Sethu Vijayakumar and Stefan Schaal
- PREPRINT May 28, 2010 To appear in Proceedings RSS 2010
- Network: Computation in Neural Systems March 2007; 18(1): 13
- Memory-Based Neural Networks For Robot Learning
- Stefan Schaal and Dagmar Sternad Department of Brain and Cognitive Sciences and the Artificial Intelligence Laboratory,
- Rapid Synchronization and Accurate Phase-locking of Rhythmic Motor Primitives
- Proceedings of the ASME 2010 Summer Bioengineering Conference
- Inverse Dynamics Control with Floating Base and Constraints Jun Nakanishi, Michael Mistry, and Stefan Schaal
- A Kalman Filter for Robust Outlier Detection Jo-Anne Ting1, Evangelos Theodorou1, and Stefan Schaal1,2
- Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning
- Journal of Machine Learning Research 9 (2008) 623-626 Submitted 10/07; Revised 3/08; Published 4/08 A Library for Locally Weighted Projection Regression
- Bayesian Backfitting Aaron D'Souza adsouza@usc.edu
- Locally Weighted Regression for Control L Link-Based Classification
- Bayesian Regression with Input Noise for High Dimensional Data Jo-Anne Ting joanneti@usc.edu
- Graph Matching vs. Mutual Information Maximization for Object Detection
- Neural Networks 24 (2011) 99108 Contents lists available at ScienceDirect
- Auton Robot (2009) 27: 323 DOI 10.1007/s10514-009-9118-y
- Inverse Dynamics Control of Floating Base Systems Using Orthogonal Decomposition
- An Iterative Path Integral Reinforcement Learning Approach Evangelos Theodorou, Jonas Buchli, Freek Stulp and Stefan Schaal
- Locally Weighted Learning Christopher G. Atkeson, Andrew W. Moorey
- Bayesian Kernel Shaping for Learning Control Jo-Anne Ting1
- Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields
- Optimality in Neuromuscular Systems Evangelos Theodorou and Francisco J. Valero-Cuevas
- Learning Policy Improvements with Path Integrals Evangelos Theodorou Jonas Buchli Stefan Schaal
- Locally Weighted Projection Regression : An O(n) Algorithm for Incremental Real Time Learning in High Dimensional Space
- Schaal S, Atkeson CG, Vijayakumar S (2002) Scalable techniques from nonparameteric statistics for real-time robot learning. Applied Intelligence 17: 49-60
- ThisarticleisprotectedbyGermancopyrightlaw.Youmaycopyanddistributethisarticleforyourpersonaluseonly.Otheruseisonlyallowedwithwrittenpermissionbythecopyrightholder. Anwendungen
- ARTICLE Communicated by Kechen Zhang Efficient Learning and Feature Selection
- BAYESIAN METHODS FOR AUTONOMOUS LEARNING SYSTEMS Jo-Anne S. Y. Ting
- In: Cowan, J. , Tesauro, G., & Alspector, J. (Eds.), Advances in Neural Information Processing Systems 6. San Mateo, CA: Morgan Kaufmann.
- Dynamic movement primitives for movement generation motivated by convergent force fields in frog
- A Unifying Methodology for the Control of Robotic Systems Jan Peters, Michael Mistry, Firdaus Udwadia, Rick Cory, Jun Nakanishi, and Stefan Schaal
- ITERATIVE PATH INTEGRAL STOCHASTIC OPTIMAL CONTROL: THEORY AND APPLICATIONS TO MOTOR CONTROL
