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Found 9 papers, 0 papers with code
Ground Manipulator Primitive Tasks to Executable Actions using Large Language Models
In order to tackle this challenge, we propose a novel approach to ground the manipulator primitive tasks to robot low-level actions using large language models (LLMs).
Robot Behavior-Tree-Based Task Generation with Large Language Models
To cope with this issue, we propose a novel behavior-tree-based task generation approach that utilizes state-of-the-art large language models.
A Multi-stage Framework with Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation
To project the data from the source and the target domains to a common subspace and manipulate the projected data in real-time, our proposed framework institutes a novel method, called an Incremental Computation of Mean-Subspace (ICMS) technique, which computes an approximation of mean-target subspace on a Grassmann manifold and is proven to be a close approximate to the Karcher mean.
Few-shot Image Recognition with Manifolds
In this paper, we extend the traditional few-shot learning (FSL) problem to the situation when the source-domain data is not accessible but only high-level information in the form of class prototypes is available.
Multi-step Online Unsupervised Domain Adaptation
The traditional methods on the OUDA problem mainly focus on transforming each arriving target data to the source domain, and they do not sufficiently consider the temporal coherency and accumulative statistics among the arriving target data.
A Two-Stage Approach to Few-Shot Learning for Image Recognition
This paper proposes a multi-layer neural network structure for few-shot image recognition of novel categories.
Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration
Secondly, we propose test-time domain adaptation to adapt the semantic embedding of the unseen classes to the test data.
Unsupervised Domain Adaptation using Regularized Hyper-graph Matching
Domain adaptation (DA) addresses the real-world image classification problem of discrepancy between training (source) and testing (target) data distributions.
Sample-to-Sample Correspondence for Unsupervised Domain Adaptation
The procedure of tackling this discrepancy between the training (source) and testing (target) domains is known as domain adaptation.
