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20232026
most citedLightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios

2 citations · 2 across the 9 of their papers we have counts for

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cs.RO2026

HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing

Zhenjie Yang, Xingyu Jiao, Guopeng Zhong +18

Robotic manipulation with dexterous hands is a cornerstone of Embodied AI, yet its progress is stifled by the high cost of collecting embodiment-aware teleoperation data. While abu…

cs.RO2026

OC-VLA++: Monocular Geometry-Guided Cross-View Consistency for Viewpoint-Robust Robotic Manipulation

Tianyi Zhang, Ziyang Gong, Zhenjie Yang +2

We propose OC-VLA++, an extension of OC-VLA for viewpoint generalization under limited camera coverage. While OC-VLA grounds robot actions in the camera coordinate system to align…

cs.RO2026

WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory

Haisheng Su, Zongdai Liu, Xin Jin +13

World Action Models (WAMs) offer a promising paradigm for robotic manipulation by jointly modeling visual state transitions and robot actions. However, existing WAMs are constraine…

cs.RO2026

GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization

Xiaosong Jia, Bowen Yang, Zuhao Ge +17

Vision-Language-Action (VLA) models aim for general robot learning by aligning action as a modality within powerful Vision-Language Models (VLMs). Existing VLAs rely on end-to-end…

cs.RO2026

Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models

Xiaosong Jia, Yuqian Shao, Zhenjie Yang +3

With the rise of vision-language models (VLM), their application for autonomous driving (VLM4AD) has gained significant attention. Meanwhile, in autonomous driving, closed-loop eva…

cs.RO2025

Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)

Zhenjie Yang, Xiaosong Jia, Qifeng Li +3

Reinforcement Learning (RL) can mitigate the causal confusion and distribution shift inherent to imitation learning (IL). However, applying RL to end-to-end autonomous driving (E2E…