Results 251 to 260 of about 1,813,028 (298)
Advanced Robotics Research, EarlyView.Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar +8 more
wiley +1 more source
Advanced Robotics Research, EarlyView.This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao +4 more
wiley +1 more source
Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models
Advanced Robotics Research, EarlyView.A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki +2 more
wiley +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Sparse Representation With Kernels
IEEE Transactions on Image Processing, 2013Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR).
Shenghua Gao +2 more
openaire +3 more sources
On sparse signal representations
Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), 2002An elementary proof of a basic uncertainty principle concerning pairs of representations of /spl Rscr//sup N/ vectors in different orthonormal bases is provided. The result, slightly stronger than stated before, has a direct impact on the uniqueness property of the sparse representation of such vectors using pairs of orthonormal bases as overcomplete ...
Michael Elad, Alfred M. Bruckstein
openaire +1 more source
Grassmannian sparse representations
Journal of Electronic Imaging, 2015We present Grassmannian sparse representations (GSR), a sparse representation Grassmann learning framework for efficient classification. Sparse representation classification offers a powerful approach for recognition in a variety of contexts. However, a major drawback of sparse representation methods is their computational performance and memory ...
Sherif Azary, Andreas E. Savakis
openaire +1 more source
Sparse Representation in Kernel Machines
IEEE Transactions on Neural Networks and Learning Systems, 2015We study the properties of least square kernel regression with l1 coefficient regularization. The kernels can be flexibly chosen to be either positive definite or indefinite. Asymptotic learning rates are deduced under smoothness condition on the kernel. Sparse representation of the solution is characterized theoretically.
Hongwei Sun, Qiang Wu 0003
openaire +2 more sources
A Sparse Representation for Function Approximation
Neural Computation, 1998We derive a new general representation for a function as a linear combination of local correlation kernels at optimal sparse locations (and scales) and characterize its relation to principal component analysis, regularization, sparsity principles, and support vector machines.
Tomaso A. Poggio, Federico Girosi
openaire +3 more sources
Learning the sparse representation for classification
2011 IEEE International Conference on Multimedia and Expo, 2011In this work, we propose a novel supervised matrix factorization method used directly as a multi-class classifier. The coefficient matrix of the factorization is enforced to be sparse by l 1 -norm regularization. The basis matrix is composed of atom dictionaries from different classes, which are trained in a jointly supervised manner by penalizing ...
Jianchao Yang +2 more
openaire +2 more sources
Sparse Representation for Speaker Identification
2010 20th International Conference on Pattern Recognition, 2010We address the closed-set problem of speaker identification by presenting a novel sparse representation classification algorithm. We propose to develop an over complete dictionary using the GMM mean super vector kernel for all the training utterances. A given test utterance corresponds to only a small fraction of the whole training database.
Imran Naseem +2 more
openaire +2 more sources