- Adaptive Optimal Control for Redundantly Actuated Arms
- Information about present and past stimulus features in human tactile afferents Hannes P Saal1
- Methods for Learning Control Policies from Variable-Constraint
- Overt Visual Attention for a Humanoid Robot Sethu Vijayakumar + , Jorg Conradt Tomohiro Shibata and Stefan Schaal +
- Neural Process Lett (2009) 29:109131 DOI 10.1007/s11063-009-9098-0
- Hierarchical Procrustes Matching for Shape Retrieval Graham McNeill, Sethu Vijayakumar
- H U M A N O I D R O B O T I C S Using Humanoid Robots to
- Part-Based Probabilistic Point Matching Graham McNeill, Sethu Vijayakumar
- 2D Shape Classification and Retrieval Graham McNeill, Sethu Vijayakumar
- Multisensory Oddity Detection as Bayesian Inference Timothy Hospedales*, Sethu Vijayakumar
- Biol Cybern (2009) 100:8195 DOI 10.1007/s00422-008-0266-5
- Training Data Selection for Optimal Generalization with Noise Variance Reduction in Neural Networks
- A gradient based technique for generating sparse representation in function approximation
- TOKYO INSTITUTE OF TECHNOLOGY TOKYO INSTITUTE OF TECHNOLOGY
- The Bayesian Backfitting Relevance Vector Machine Aaron D'Souza adsouza@usc.edu
- Autonomous Robots 12, 5569, 2002 c 2002 Kluwer Academic Publishers. Manufactured in The Netherlands.
- Real-Time Statistical Learning For Robotics and Human Augmentation
- Exploiting Sensorimotor Stochasticity for Learning Control of Variable Impedance Actuators
- Online Learning for Humanoid Robot Systems Jrg Conradt CONRADT@CLMC.USC.EDU
- 10000 100000 1000000 2500000 # Training Data Points
- J Robot Soc Japan Vol. xx No. xx, pp.1!A8, 200x 1 2r @b "+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+"+
- A Computational Model of Limb Impedance Control Based on Principles of Internal Model Uncertainty
- Local Adaptive Subspace Regression Sethu Vijayakumar
- An Approximate Inference Approach to Temporal Optimization in Optimal Control
- Bayesian Kernel Shaping for Learning Control Jo-Anne Ting1
- RKHS based Functional Analysis for Exact Incremental Sethu Vijayakumar #
- 5 Approximate Nearest Neighbor Regression in Very High Dimensions
- Statistical Learning for Humanoid Robots Sethu Vijayakumar (sethu@usc.edu), Aaron D'Souza
- H U M A N O I D R O B O T I C S UsingHumanoidRobots to
- Sequential Support Vector Classifiers and Regression Sethu Vijayakumar and Si Wu
- Latent Spaces for Dynamic Movement Primitives Sebastian Bitzer and Sethu Vijayakumar
- SENSOR-ASSISTED ADAPTIVE MOTOR CONTROL UNDER CONTINUOUSLY VARYING CONTEXT
- Vijayakumar, S, Schaal, S (2000). Fast and efficient incremental learning for highdimensional movement systems, International Conference on Robotics and Automation (ICRA2000). San Francisco, April 2000.
- Local Dimensionality Reduction For Locally Weighted Sethu Vijayakumar yz and Stefan Schaal xz
- Learning Nullspace Policies Chris Towell, Matthew Howard and Sethu Vijayakumar
- Adaptive Optimal Feedback Control with Learned Internal Dynamics Models
- Learning Potential-based Policies from Constrained Motion
- Kernel Carpentry for Online Regression using Randomly Varying Coefficient Narayanan U Edakunni
- Context Estimation and Learning Control through Latent Variable Extraction: From discrete to continuous contexts
- Learning Multiple Models of Non-Linear Dynamics for Control under Varying Contexts
- Exploiting Variable Stiffness in Explosive Movement Tasks
- http://ijr.sagepub.com/ Robotics Research
- Transferring Impedance Control Strategies Between Heterogeneous Systems via Apprenticeship Learning
- A Theory of Impedance Control based on Internal Model Uncertainty Djordje Mitrovic, Stefan Klanke, Adrian Haith and Sethu Vijayakumar
- An Approximate Inference Approach to Temporal Optimization in Optimal Control
- Optimal Feedback Control for Anthropomorphic Manipulators Djordje Mitrovic, Sho Nagashima, Stefan Klanke, Takamitsu Matsubara, Sethu Vijayakumar
- Auton Robot (2009) 27: 105121 DOI 10.1007/s10514-009-9129-8
- Applied Bionics and Biomechanics Vol. 5, No. 4, December 2008, 195211
- Optimal Control with Adaptive Internal Dynamics Models Djordje Mitrovic, Stefan Klanke, Sethu Vijayakumar School of Informatics, The University of Edinburgh, UK
- Reconstructing Null-space Policies Subject to Dynamic Task Constraints in Redundant
- 2006 Workshop on Information-Based Induction Sciences Osaka, Japan, October 31 -November 2, 2006.
- Scaling Reinforcement Learning Paradigms for Motor Control
- Load estimation and control using learned dynamics models Georgios Petkos and Sethu Vijayakumar
- Schaal, S, Atkeson, CG, Vijayakumar, S (2000). Real-time robot learning with locally weighted statistical learning, International Conference on Robotics and Automation (ICRA2000), vol.1, pp.288-293.
- Robustness of VOR and OKR adaptation under kinematics and dynamics transformations
- Bayesian causal inference drives temporal sensorimotor recalibration Luigi Acerbi L.Acerbi@sms.ed.ac.uk , Sethu Vijayakumar sethu.vijayakumar@ed.ac.uk
- We present a novel manipulandum for understanding the sensorimotor processes involved in object grasping. We have developed a
- Interactions between sensory and motor components of adaptation predicted by a Bayesian model
- A Bayesian Model of Multi-modal Visuomotor Adaptation Adrian Haith, Sethu Vijayakumar --School of Informatics, The University of Edinburgh, UK
- Structure Inference for Bayesian Multisensor Scene Understanding
- Active Estimation of Object Dynamics Parameters with Tactile Sensors Hannes P. Saal, Jo-Anne Ting, and Sethu Vijayakumar
- Active Sequential Learning with Tactile Feedback Hannes P. Saal Jo-Anne Ting Sethu Vijayakumar
- Behavioral/Systems/Cognitive Information about Complex Fingertip Parameters in
- Spatiotemporal distribution of tactile information across the human fingertip Hannes P Saal1
- Linear and Nonlinear Generative Probabilistic Class Models for Shape Contours
- Part-based Probabilistic Point Matching using Equivalence Constraints
- Towards Semi-supervised Manifold Learning: UKR with Structural Hints
- Synthesising Novel Movements through Latent Space Modulation of Scalable Control Policies
- Training Data Selection for Optimal Generalization with Noise Variance Reduction in Neural Networks
- Locally Weighted Projection Regression (LLWWPPRR) -a users manual
- Unifying the sensory and motor components of sensorimotor adaptation Adrian Haith1
- Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
- Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
- IEICE TRANS. INF. & SYST., VOL. E00{A, NO. 1 1994 1 PAPER Special Issue on ZZZ
- Local Dimensionality Reduction For Locally Weighted Sethu Vijayakumaryz and Stefan Schaalxz
- Incremental Online Learning in High Dimensions Sethu Vijayakumar, Aaron D'Souza & Stefan Schaal
- An Incremental Approach to Training Data Selection in Neural Sethu Vijayakumar
- Efficient Online Classification using an Ensemble of Bayesian Linear Logistic Regressors
- Using Dimensionality Reduction to Exploit Constraints in Reinforcement Learning
- Value Function Approximation on Non-Linear Manifolds for Robot Motor Control
- Neurocomputing 29 (1999) 85}113 RKHS-based functional analysis for exact
- OPTIMAL CONTROL WITH ADAPTIVE INTERNAL DYNAMICS MODELS
- Learning Utility Surfaces for Movement Selection Matthew Howard, Michael Gienger, Christian Goerick and Sethu Vijayakumar
- Active Filtering for Robot Tactile Learning Hannes P Saal1,2
- Does Dimensionality Reduction Improve the Quality of Motion Interpolation?
- NIPS 21 Preproceedings Version, Nov 2008 Multi-task Gaussian Process Learning of Robot
- A gradient based technique for generating sparse representation in function approximation
- Bayesian causal inference drives temporal sensorimotor recalibration Luigi ACERBI, Sethu VIJAYAKUMAR --School of Informatics, The University of Edinburgh, UK
- Auton Robot DOI 10.1007/s10514-008-9095-6
- Transferring Impedance Control Strategies Between Heterogeneous Systems via Apprenticeship Learning
- A Functional Analytic Approach to Incremental Learning in Optimally Generalizing Neural Networks
- Biomimetic Oculomotor Control 1 TOMOHIRO SHIBATA, 12 SETHU VIJAYAKUMAR, 3 J
- Vijayakumar, S., & Schaal, S. (1998). Local adaptive subspace regression. Neural Processing Letters, 7, 139149.
- Schaal, S, Atkeson, CG, Vijayakumar, S (2000). Realtime robot learning with locally weighted statistical learning, International Conference on Robotics and Automation (ICRA2000). San Francisco, April 2000.
- Structure Inference for Bayesian Multisensory Perception and Tracking Timothy M. Hospedales Joel J. Cartwright Sethu Vijayakumar
- Submitted to: Applied Intelligence Scalable Techniques from Nonparametric
- IEICE TRANS. INF. & SYST., VOL. E00{A, NO. 1 JANUARY 1998 Improving Generalization Ability through Active Learning
- D'Souza, A., Vijayakumar, S., Schaal, S., (submitted). Learning inverse kinematics, International Conference on Intelligence in Robotics and Autonomous Systems (IROS 2001). Maui, Hawaii, Oct 2001.
- Humanoid Oculomotor Control Based on Concepts of Computational Neuroscience
- Exploiting Sensorimotor Stochasticity for Learning Control of Variable Impedance Actuators
- Robust Constraint-consistent Learning Matthew Howard, Stefan Klanke, Michael Gienger, Christian Goerick and Sethu Vijayakumar
- A PROBABILISTIC APPROACH TO ROBUST SHAPE MATCHING Graham McNeill, Sethu Vijayakumar
- A Novel Method for Learning Policies from Constrained Motion Matthew Howard, Stefan Klanke, Michael Gienger, Christian Goerick and Sethu Vijayakumar
- Controlling Humanoid Robots in Topology Coordinates Edmond S.L. Ho, Taku Komura, Subramanian Ramamoorthy, Sethu Vijayakumar
- Realising Dextrous Manipulation with Structured Manifolds using Unsupervised Kernel Regression with Structural Hints
- Constraint-based Equilibrium and Stiffness Control of Variable Stiffness Actuators
