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20202026
most citedA Survey on Robotics with Foundation Models: toward Embodied AI

4 citations · 5 across the 11 of their papers we have counts for

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22 papers · 1 filter

cs.RO2026

A Causality-aware Infer-diagnose-refine Framework for Test-time Modality Adaptation in VLA Models

Haoyu Zhang, Yuwei Wu, Jin Chen +6

Vision-language-action (VLA) models predict sequential actions to execute tasks specified by language instructions, conditioned on visual observations and proprioceptive states. Ho…

cs.RO2026

HEX: Humanoid-Aligned Experts for Cross-Embodiment Whole-Body Manipulation

Shuanghao Bai, Meng Li, Xinyuan Lv +14

Humans achieve complex manipulation through coordinated whole-body control, whereas most Vision-Language-Action (VLA) models treat robot body parts largely independently, making hi…

cs.RO2026

RoboGene: Boosting VLA Pre-training via Diversity-Driven Agentic Framework for Real-World Task Generation

Yixue Zhang, Kun Wu, Zhi Gao +12

The pursuit of general-purpose robotic manipulation is hindered by the scarcity of diverse, real-world interaction data. Unlike data collection from web in vision or language, robo…

cs.RO2026

RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation

Xinhua Wang, Kun Wu, Zhen Zhao +10

Enhancing the generalization capability of robotic learning to enable robots to operate effectively in diverse, unseen scenes is a fundamental and challenging problem. Existing app…

cs.RO2025

Real-world Reinforcement Learning from Suboptimal Interventions

Yinuo Zhao, Huiqian Jin, Lechun Jiang +9

Real-world reinforcement learning (RL) offers a promising approach to training precise and dexterous robotic manipulation policies in an online manner, enabling robots to learn fro…

cs.RO2025

RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence

Chengkai Hou, Kun Wu, Jiaming Liu +30

While data-driven imitation learning has revolutionized robotic manipulation, current approaches remain constrained by the scarcity of large-scale, diverse real-world demonstration…