Zobrazeno 1 - 10
of 900
pro vyhledávání: '"Wu Yuwei"'
Autor:
Wu, Yuwei, Tao, Yuezhan, Li, Peihan, Shi, Guangyao, Sukhatmem, Gaurav S., Kumar, Vijay, Zhou, Lifeng
In this paper, we propose a hierarchical Large Language Models (LLMs) in-the-loop optimization framework for real-time multi-robot task allocation and target tracking in an unknown hazardous environment subject to sensing and communication attacks. W
Externí odkaz:
http://arxiv.org/abs/2409.12274
Multi-robot collaboration for target tracking presents significant challenges in hazardous environments, including addressing robot failures, dynamic priority changes, and other unpredictable factors. Moreover, these challenges are increased in adver
Externí odkaz:
http://arxiv.org/abs/2409.11230
Trajectory generation in dynamic environments presents a significant challenge for quadrotors, particularly due to the non-convexity in the spatial-temporal domain. Many existing methods either assume simplified static environments or struggle to pro
Externí odkaz:
http://arxiv.org/abs/2409.10647
Autor:
Li, Pengxiang, Gao, Zhi, Zhang, Bofei, Yuan, Tao, Wu, Yuwei, Harandi, Mehrtash, Jia, Yunde, Zhu, Song-Chun, Li, Qing
Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derive
Externí odkaz:
http://arxiv.org/abs/2407.11522
Stereo matching provides depth estimation from binocular images for downstream applications. These applications mostly take video streams as input and require temporally consistent depth maps. However, existing methods mainly focus on the estimation
Externí odkaz:
http://arxiv.org/abs/2407.11950
We propose an online iterative algorithm to find a suitable convex cover to under-approximate the free space for autonomous navigation to delineate Safe Flight Corridors (SFC). The convex cover consists of a set of polytopes such that the union of th
Externí odkaz:
http://arxiv.org/abs/2406.09631
Autor:
Li, Chuanhao, Li, Zhen, Jing, Chenchen, Liu, Shuo, Shao, Wenqi, Wu, Yuwei, Luo, Ping, Qiao, Yu, Zhang, Kaipeng
Large vision-language models (LVLMs) are ignorant of the up-to-date knowledge, such as LLaVA series, because they cannot be updated frequently due to the large amount of resources required, and therefore fail in many cases. For example, if a LVLM was
Externí odkaz:
http://arxiv.org/abs/2405.14554
Autor:
Bhattacharya, Anish, Rao, Nishanth, Parikh, Dhruv, Kunapuli, Pratik, Wu, Yuwei, Tao, Yuezhan, Matni, Nikolai, Kumar, Vijay
We demonstrate the capabilities of an attention-based end-to-end approach for high-speed vision-based quadrotor obstacle avoidance in dense, cluttered environments, with comparison to various state-of-the-art learning architectures. Quadrotor unmanne
Externí odkaz:
http://arxiv.org/abs/2405.10391
Missing data imputation poses a paramount challenge when dealing with graph data. Prior works typically are based on feature propagation or graph autoencoders to address this issue. However, these methods usually encounter the over-smoothing issue wh
Externí odkaz:
http://arxiv.org/abs/2404.17164
Multi-robot target tracking finds extensive applications in different scenarios, such as environmental surveillance and wildfire management, which require the robustness of the practical deployment of multi-robot systems in uncertain and dangerous en
Externí odkaz:
http://arxiv.org/abs/2404.07880