Zobrazeno 1 - 10
of 427
pro vyhledávání: '"Ding, Mingyu"'
Autor:
Li, Yiheng, Ge, Chongjian, Li, Chenran, Xu, Chenfeng, Tomizuka, Masayoshi, Tang, Chen, Ding, Mingyu, Zhan, Wei
We propose Waymo Open Motion Dataset-Reasoning (WOMD-Reasoning), a language annotation dataset built on WOMD, with a focus on describing and reasoning interactions and intentions in driving scenarios. Previous language datasets primarily captured int
Externí odkaz:
http://arxiv.org/abs/2407.04281
Autor:
Wang, Yixiao, Zhang, Yifei, Huo, Mingxiao, Tian, Ran, Zhang, Xiang, Xie, Yichen, Xu, Chenfeng, Ji, Pengliang, Zhan, Wei, Ding, Mingyu, Tomizuka, Masayoshi
The increasing complexity of tasks in robotics demands efficient strategies for multitask and continual learning. Traditional models typically rely on a universal policy for all tasks, facing challenges such as high computational costs and catastroph
Externí odkaz:
http://arxiv.org/abs/2407.01531
Road surface conditions, especially geometry profiles, enormously affect driving performance of autonomous vehicles. Vision-based online road reconstruction promisingly captures road information in advance. Existing solutions like monocular depth est
Externí odkaz:
http://arxiv.org/abs/2404.06605
Autor:
Peng, Chensheng, Xu, Chenfeng, Wang, Yue, Ding, Mingyu, Yang, Heng, Tomizuka, Masayoshi, Keutzer, Kurt, Pavone, Marco, Zhan, Wei
Monocular SLAM has long grappled with the challenge of accurately modeling 3D geometries. Recent advances in Neural Radiance Fields (NeRF)-based monocular SLAM have shown promise, yet these methods typically focus on novel view synthesis rather than
Externí odkaz:
http://arxiv.org/abs/2403.08125
Motion planners are essential for the safe operation of automated vehicles across various scenarios. However, no motion planning algorithm has achieved perfection in the literature, and improving its performance is often time-consuming and labor-inte
Externí odkaz:
http://arxiv.org/abs/2403.07470
Autor:
Guo, Dingkun, Xiang, Yuqi, Zhao, Shuqi, Zhu, Xinghao, Tomizuka, Masayoshi, Ding, Mingyu, Zhan, Wei
Robotic grasping is a fundamental aspect of robot functionality, defining how robots interact with objects. Despite substantial progress, its generalizability to counter-intuitive or long-tailed scenarios, such as objects with uncommon materials or s
Externí odkaz:
http://arxiv.org/abs/2402.16836
Autor:
Mu, Yao, Chen, Junting, Zhang, Qinglong, Chen, Shoufa, Yu, Qiaojun, Ge, Chongjian, Chen, Runjian, Liang, Zhixuan, Hu, Mengkang, Tao, Chaofan, Sun, Peize, Yu, Haibao, Yang, Chao, Shao, Wenqi, Wang, Wenhai, Dai, Jifeng, Qiao, Yu, Ding, Mingyu, Luo, Ping
Robotic behavior synthesis, the problem of understanding multimodal inputs and generating precise physical control for robots, is an important part of Embodied AI. Despite successes in applying multimodal large language models for high-level understa
Externí odkaz:
http://arxiv.org/abs/2402.16117
Autor:
Chen, Junting, Mu, Yao, Yu, Qiaojun, Wei, Tianming, Wu, Silang, Yuan, Zhecheng, Liang, Zhixuan, Yang, Chao, Zhang, Kaipeng, Shao, Wenqi, Qiao, Yu, Xu, Huazhe, Ding, Mingyu, Luo, Ping
Rapid progress in high-level task planning and code generation for open-world robot manipulation has been witnessed in Embodied AI. However, previous studies put much effort into general common sense reasoning and task planning capabilities of large-
Externí odkaz:
http://arxiv.org/abs/2402.14623
Stereo matching plays a crucial role in 3D perception and scenario understanding. Despite the proliferation of promising methods, addressing texture-less and texture-repetitive conditions remains challenging due to the insufficient availability of ri
Externí odkaz:
http://arxiv.org/abs/2402.08931
Diffusion models have demonstrated strong potential for robotic trajectory planning. However, generating coherent trajectories from high-level instructions remains challenging, especially for long-range composition tasks requiring multiple sequential
Externí odkaz:
http://arxiv.org/abs/2312.11598