593 citations · 1.8k across the 57 of their papers we have counts for
11 papers · 1 filter
Factor-Aware Mixture-of-Experts with Pretrained Encoder for Combinatorial Generalization
Feihong Zhang, Guojian Zhan, Zeyu He +8
The integration of pretrained encoders with diffusion policies has become a dominant paradigm for visual robotic manipulation. However, it still struggles to generalize across comp…
World Engine: Towards the Era of Post-Training for Autonomous Driving
Tianyu Li, Li Chen, Caojun Wang +16
Autonomous vehicles must operate safely in the real world, where errors can have severe consequences. Although modern end-to-end driving policies excel in routine scenarios, their…
M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking
Zuxing Lu, Ziang Zheng, Yao Lyu +7
Building a general-purpose whole-body controller is essential for enabling diverse motion capabilities in humanoid robots across a wide range of downstream tasks, including locomot…
FUSE: A Framework for Unified State Estimation in Vehicular and Robotic SLAM Systems
Wei Wu, Honglin Chen, Wenhan Cao +7
Tightly coupled SLAM formulations under mixed-rate sensing often bind temporal processing, local geometric association, estimator formulation, and map-update policy into method-spe…
Risk-Aware Vehicle Trajectory Prediction Under Safety-Critical Scenarios
Qingfan Wang, Dongyang Xu, Gaoyuan Kuang +3
Trajectory prediction is significant for intelligent vehicles to achieve high-level autonomous driving, and a lot of relevant research achievements have been made recently. Despite…
Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments
Haicheng Liao, Shangqian Liu, Yongkang Li +5
In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often…
BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving
Haicheng Liao, Zhenning Li, Huanming Shen +5
The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we…
Integrated Drill Boom Hole-Seeking Control via Reinforcement Learning
Haoqi Yan, Haoyuan Xu, Hongbo Gao +3
Intelligent drill boom hole-seeking is a promising technology for enhancing drilling efficiency, mitigating potential safety hazards, and relieving human operators. Most existing i…
LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
Hao Sha, Yao Mu, Yuxuan Jiang +7
Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability. To add…
Fixed-Dimensional and Permutation Invariant State Representation of Autonomous Driving
Jingliang Duan, Dongjie Yu, Shengbo Eben Li +4
In this paper, we propose a new state representation method, called encoding sum and concatenation (ESC), for the state representation of decision-making in autonomous driving. Unl…
Indirect Shared Control of Highly Automated Vehicles for Cooperative Driving between Driver and Automation
Renjie Li, Yanan Li, Shengbo Eben Li +2
It is widely acknowledged that drivers should remain in the control loop of automated vehicles before they completely meet real-world operational conditions. This paper introduces…