Search Results for author:
Found 15 papers, 5 papers with code
A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches
Task and Motion Planning (TAMP) integrates high-level task planning and low-level motion planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic tasks.
Adjustable Molecular Representation for Unified Pre-training Strategy
For the pre-training process, we designed a task for molecular canonicalization, which involves transforming ltiple generic molecular representations into canonical representations.
Automatic nodule identification and differentiation in ultrasound videos to facilitate per-nodule examination
Ultrasound is a vital diagnostic technique in health screening, with the advantages of non-invasive, cost-effective, and radiation free, and therefore is widely applied in the diagnosis of nodules.
ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A*
Effectively performing object rearrangement is an essential skill for mobile manipulators, e. g., setting up a dinner table or organizing a desk.
TrTr: A Versatile Pre-Trained Large Traffic Model based on Transformer for Capturing Trajectory Diversity in Vehicle Population
In this study, we apply the Transformer architecture to traffic tasks, aiming to learn the diversity of trajectories within vehicle populations.
Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations
While state-of-the-art NLP models have demonstrated excellent performance for aspect based sentiment analysis (ABSA), substantial evidence has been presented on their lack of robustness.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds
Each situation corresponds to a state instance wherein a robot is potentially unable to complete a task using a solution that normally works.
Robot Task Planning and Situation Handling in Open Worlds
This paper introduces a novel algorithm (COWP) for open-world task planning and situation handling that dynamically augments the robot's action knowledge with task-oriented common sense.
A Cascaded Multi-Task Generative Framework for Detecting Aortic Dissection on 3D Non-contrast-enhanced Computed Tomography
Experiments also showed that the proposed framework could achieve an average accuracy of 0. 831, a sensitivity of 0. 938, and an F1-score of 0. 847 in comparison with seven state-of-the-art classification models used by three radiologists with junior, intermediate, and senior experiences, respectively.
SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking
Joint detection and embedding (JDE) based methods usually estimate bounding boxes and embedding features of objects with a single network in Multi-Object Tracking (MOT).
Ranked #12 on
Multi-Object Tracking
on MOT20
(using extra training data)
A Structure Feature Algorithm for Multi-modal Forearm Registration
Augmented reality technology based on image registration is becoming increasingly popular for the convenience of pre-surgery preparation and medical education.
Task and Situation Structures for Service Agent Planning
Everyday tasks are characterized by their varieties and variations, and frequently are not clearly specified to service agents.
Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules
The RGCN model achieved an overall accuracy of 0. 872, an AUROC of 0. 919 and an AUPRC of 0. 838 for the testing dataset with the drug-protein interactions and the Mordred descriptors as the input.
A Global to Local Double Embedding Method for Multi-person Pose Estimation
Both of the two types of methods involve two stages, namely, person detection and joints detection.
Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment
Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction.
