Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Cai, Panpan"'
Uncertainties in dynamic road environments pose significant challenges for behavior and trajectory planning in autonomous driving. This paper introduces BoT-Drive, a planning algorithm that addresses uncertainties at both behavior and trajectory leve
Externí odkaz:
http://arxiv.org/abs/2409.18411
Masked point modeling methods have recently achieved great success in self-supervised learning for point cloud data. However, these methods are sensitive to rotations and often exhibit sharp performance drops when encountering rotational variations.
Externí odkaz:
http://arxiv.org/abs/2409.00353
Autor:
Cao, Haiyao, Zhang, Zhen, Cai, Panpan, Liu, Yuhang, Zou, Jinan, Abbasnejad, Ehsan, Huang, Biwei, Gong, Mingming, Hengel, Anton van den, Shi, Javen Qinfeng
One of the significant challenges in reinforcement learning (RL) when dealing with noise is estimating latent states from observations. Causality provides rigorous theoretical support for ensuring that the underlying states can be uniquely recovered
Externí odkaz:
http://arxiv.org/abs/2408.13498
Autor:
Zhang, Ruiqi, Hou, Jing, Walter, Florian, Gu, Shangding, Guan, Jiayi, Röhrbein, Florian, Du, Yali, Cai, Panpan, Chen, Guang, Knoll, Alois
Reinforcement Learning (RL) is a potent tool for sequential decision-making and has achieved performance surpassing human capabilities across many challenging real-world tasks. As the extension of RL in the multi-agent system domain, multi-agent RL (
Externí odkaz:
http://arxiv.org/abs/2408.09675
Robot motion planning has made vast advances over the past decades, but the challenge remains: robot mobile manipulators struggle to plan long-range whole-body motion in common household environments in real time, because of high-dimensional robot co
Externí odkaz:
http://arxiv.org/abs/2405.11317
Trajectory prediction plays a vital role in the performance of autonomous driving systems, and prediction accuracy, such as average displacement error (ADE) or final displacement error (FDE), is widely used as a performance metric. However, a signifi
Externí odkaz:
http://arxiv.org/abs/2306.15136
Identifying internal parameters for planning is crucial to maximizing the performance of a planner. However, automatically tuning internal parameters which are conditioned on the problem instance is especially challenging. A recent line of work focus
Externí odkaz:
http://arxiv.org/abs/2303.06768
Uncertainty on human behaviors poses a significant challenge to autonomous driving in crowded urban environments. The partially observable Markov decision processes (POMDPs) offer a principled framework for planning under uncertainty, often leveragin
Externí odkaz:
http://arxiv.org/abs/2209.11422
Publikováno v:
In Toxicology and Applied Pharmacology May 2024 486
Autor:
Cai, Panpan, Hsu, David
Real-time planning under uncertainty is critical for robots operating in complex dynamic environments. Consider, for example, an autonomous robot vehicle driving in dense, unregulated urban traffic of cars, motorcycles, buses, etc. The robot vehicle
Externí odkaz:
http://arxiv.org/abs/2101.03834