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Works28 from public data
- Temporal Sentence Grounding in Videos: A Survey and Future Directions85
A summary of fundamental concepts in TSGV and current research status, as well as future research directions are provided, and the techniques for multimodal understanding and interaction, which is the key focus of TSGV for effective alignment between the two modalities are reviewed.
- Multi-UAV Coverage Path Planning for the Inspection of Large and Complex Structures80
The proposed sampling-based coverage path planning method outperforms previous methods, by reducing the length of the planned inspection path by up to 48%.
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- KOVIS: Keypoint-based Visual Servoing with Zero-Shot Sim-to-Real Transfer for Robotics Manipulation58
KOVIS, a novel learning-based, calibration-free visual servoing method for fine robotic manipulation tasks with eye-in-hand stereo camera system, is presented and is able to achieve zero-shot sim-to-real transfer to real-world robotics manipulation tasks.
- A Computational Framework for Automatic Online Path Generation of Robotic Inspection Tasks via Coverage Planning and Reinforcement Learning57
It is observed that the proposed method could automatically generate the inspection path online for different target objects to meet the coverage requirement, with the presence of pose variation of the target object.
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- Coverage Path Planning using Path Primitive Sampling and Primitive Coverage Graph for Visual Inspection42
This paper proposes a novel planning method to directly sample and plan the inspection path for a camera-equipped UAV to acquire visual and geometric information of the target structures as a video stream setting in complex 3D environment.
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- Model-based view planning for building inspection and surveillance using voxel dilation, Medial Objects, and Random-Key Genetic Algorithm28
A novel two-step computational method for finding near-optimal views to cover the surface of a target set of buildings using voxel dilation, Medial Objects, and Random-Key Genetic Algorithm (RKGA) is presented.
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- Constrained Heterogeneous Vehicle Path Planning for Large-area Coverage21
A novel large-area-coverage planning framework which collectively optimizes the paths for aerial and ground vehicles and shows that the solution time can be reduced to close-to-real-time levels.
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- 6D Pose Estimation with Correlation Fusion18
A novel method to effectively consider the correlation within and across both modalities with attention mechanism to learn discriminative and compact multi-modal features and to explore effective intra- and inter-modality fusion in 6D pose estimation is presented.
- Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios With Multiagent Reinforcement Learning16
The personality modeling network (PeMN) is introduced, which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios and demonstrates that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization than traditional MARL methods.
- Learning Observation-Based Certifiable Safe Policy for Decentralized Multi-Robot Navigation15
A control barrier function (CBF) based optimizer that ensures robot safety with both high probability and flexibility, using only sensor measurement is proposed.
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- A Hyper-Network Based End-to-End Visual Servoing With Arbitrary Desired Poses12
This letter proposes a differentiable architecture for arbitrary pose servoing: a hyper-network based neural controller (HPN-NC), which consists of a hyper net and a low-level controller, where the hyper net learns to generate the parameters of the low- level controller and the controller uses the 2D keypoints error for control.
- Hierarchical Point Cloud Encoding and Decoding With Lightweight Self-Attention Based Model8
SA-CNN is presented, a hierarchical and lightweight self-attention based encoding and decoding architecture for representation learning of point cloud data and is capable of a wide range of applications, namely classification, part segmentation, reconstruction, shape retrieval, and unsupervised classification.
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- Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning7
A novel Deep Reinforcement Learning algorithm is proposed for efficient task generalization and environment adaptation in the robotic task learning problem and is able to efficiently generalize the previously learned task by model fusion to solve the environment adaptation problem.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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