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cs.RO2026

Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories

Xiaomi Robotics Team, Jun Guo, Piaopiao Jin +31

We present Xiaomi-Robotics-1, a foundational vision-language-action (VLA) model capable of (1) following diverse language instructions to perform a wide range of mobile manipulatio…

cs.RO2026

FutureNav: Unified World-Action Modeling for Vision-and-Language Navigation

Lingfeng Zhang, Zeying Gong, Xiaoshuai Hao +7

Vision-and-language navigation (VLN) in continuous environments requires an agent to ground instructions in egocentric observations while maintaining spatial understanding across l…

cs.RO2026

Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning

Ziyang Yao, Haochen Liu, Yuncheng Jiang +10

Autonomous driving requires reasoning about how ego actions shape future world evolution, rather than merely mapping observations to actions. However, most end-to-end methods rely…

cs.RO2026

OneVLA: A Unified Framework for Embodied Tasks

Lingfeng Zhang, Xiaoshuai Hao, Yingbo Tang +10

Navigation and manipulation are fundamental capabilities of embodied intelligence, enabling robots to interpret natural language commands and interact physically with their surroun…

cs.RO2026

SUGAR: A Scalable Human-Video-Driven Generalizable Humanoid Loco-Manipulation Learning Framework

Tianshu Wu, Xiangqi Kong, Yue Chen +5

Building humanoid robots capable of generalizable whole-body loco-manipulation in the real world remains a fundamental challenge. Existing methods either rely on laborious task-spe…

cs.RO2026

Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives

Junli Wang, Zhihua Hua, Xueyi Liu +7

Existing imitation learning methods for end-to-end autonomous driving predominantly learn from successful demonstrations by minimizing geometric deviations from expert trajectories…