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20232026
most citedOptimization Landscape of Policy Gradient Methods for Discrete-time Static Output Feedback

10 citations · 18 across the 25 of their papers we have counts for

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9 papers · 1 filter

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO20241 cited

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…

cs.RO20241 cited

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…