Zobrazeno 1 - 10
of 188
pro vyhledávání: '"Chen, Kejia"'
Graph similarity computation (GSC) aims to quantify the similarity score between two graphs. Although recent GSC methods based on graph neural networks (GNNs) take advantage of intra-graph structures in message passing, few of them fully utilize the
Externí odkaz:
http://arxiv.org/abs/2411.03624
Autor:
Chen, Kejia, Shen, Zheng, Zhang, Yue, Chen, Lingyun, Wu, Fan, Bing, Zhenshan, Haddadin, Sami, Knoll, Alois
Large Language Models (LLMs) have gained popularity in task planning for long-horizon manipulation tasks. To enhance the validity of LLM-generated plans, visual demonstrations and online videos have been widely employed to guide the planning process.
Externí odkaz:
http://arxiv.org/abs/2409.11863
Autor:
Chen, Kejia, Bing, Zhenshan, Wu, Yansong, Wu, Fan, Zhang, Liding, Haddadin, Sami, Knoll, Alois
Controlling the shape of deformable linear objects using robots and constraints provided by environmental fixtures has diverse industrial applications. In order to establish robust contacts with these fixtures, accurate estimation of the contact stat
Externí odkaz:
http://arxiv.org/abs/2401.17154
Autor:
Zhang, Liding, Bing, Zhenshan, Chen, Kejia, Chen, Lingyun, Cai, Kuanqi, Zhang, Yu, Wu, Fan, Krumbholz, Peter, Yuan, Zhilin, Haddadin, Sami, Knoll, Alois
In path planning, anytime almost-surely asymptotically optimal planners dominate the benchmark of sampling-based planners. A notable example is Batch Informed Trees (BIT*), where planners iteratively determine paths to batches of vertices within the
Externí odkaz:
http://arxiv.org/abs/2310.12828
Studying the manipulation of deformable linear objects has significant practical applications in industry, including car manufacturing, textile production, and electronics automation. However, deformable linear object manipulation poses a significant
Externí odkaz:
http://arxiv.org/abs/2307.10153
Condition-based maintenance is becoming increasingly important in hydraulic systems. However, anomaly detection for these systems remains challenging, especially since that anomalous data is scarce and labeling such data is tedious and even dangerous
Externí odkaz:
http://arxiv.org/abs/2306.02709
Autor:
Bing, Zhenshan, Mavrichev, Aleksandr, Shen, Sicong, Yao, Xiangtong, Chen, Kejia, Huang, Kai, Knoll, Alois
Deep reinforcement learning (RL) has been endowed with high expectations in tackling challenging manipulation tasks in an autonomous and self-directed fashion. Despite the significant strides made in the development of reinforcement learning, the pra
Externí odkaz:
http://arxiv.org/abs/2304.09119
Multiplex network embedding is an effective technique to jointly learn the low-dimensional representations of nodes across network layers. However, the number of edges among layers may vary significantly. This data imbalance will lead to performance
Externí odkaz:
http://arxiv.org/abs/2212.14540
Directed graphs model asymmetric relationships between nodes and research on directed graph embedding is of great significance in downstream graph analysis and inference. Learning source and target embedding of nodes separately to preserve edge asymm
Externí odkaz:
http://arxiv.org/abs/2211.02232
Autor:
Yao, Xiangtong, Bing, Zhenshan, Zhuang, Genghang, Chen, Kejia, Zhou, Hongkuan, Huang, Kai, Knoll, Alois
Meta-reinforcement learning (meta-RL) is a promising approach that enables the agent to learn new tasks quickly. However, most meta-RL algorithms show poor generalization in multi-task scenarios due to the insufficient task information provided only
Externí odkaz:
http://arxiv.org/abs/2209.10656