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Found 18 papers, 8 papers with code
Long-Term Upper-Limb Prosthesis Myocontrol via High-Density sEMG and Incremental Learning
Noninvasive human-machine interfaces such as surface electromyography (sEMG) have long been employed for controlling robotic prostheses.
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
Our findings also indicate that maintaining a fixed classifier aids in stabilizing the training and learning more discriminative features in cross-device settings.
Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis
This study provides a comprehensive benchmark framework for Source-Free Unsupervised Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical understanding of the complex relationships between multiple key design factors in SF-UDA methods.
TempoRL: laser pulse temporal shape optimization with Deep Reinforcement Learning
Furthermore, DRL aims to find an optimal control policy rather than a static parameter configuration, particularly suitable for dynamic processes involving sequential decision-making.
Key Design Choices for Double-Transfer in Source-Free Unsupervised Domain Adaptation
Fine-tuning and Domain Adaptation emerged as effective strategies for efficiently transferring deep learning models to new target tasks.
PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting
Popular industrial robotic problems such as spray painting and welding require (i) conditioning on free-shape 3D objects and (ii) planning of multiple trajectories to solve the task.
On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning
Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks.
Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models
Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinematics map.
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach
We show that the robot can improve adaptation to novel domains, either by interacting with a human teacher (Active Learning) or with an autonomous supervision (Semi-supervised Learning).
Large-scale Kernel Methods and Applications to Lifelong Robot Learning
In this thesis, we focus on kernel methods, a theoretically sound and effective class of learning algorithms yielding nonparametric estimators.
Derivative-free online learning of inverse dynamics models
This paper discusses online algorithms for inverse dynamics modelling in robotics.
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification.
Generalization Properties and Implicit Regularization for Multiple Passes SGM
We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions.
Incremental Robot Learning of New Objects with Fixed Update Time
We consider object recognition in the context of lifelong learning, where a robotic agent learns to discriminate between a growing number of object classes as it accumulates experience about the environment.
Online semi-parametric learning for inverse dynamics modeling
This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model.
Incremental Semiparametric Inverse Dynamics Learning
This paper presents a novel approach for incremental semiparametric inverse dynamics learning.
NYTRO: When Subsampling Meets Early Stopping
Early stopping is a well known approach to reduce the time complexity for performing training and model selection of large scale learning machines.
Less is More: Nyström Computational Regularization
We study Nystr\"om type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered.
