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cs.RO2026

VidAct: Learning Manipulation from In-the-Wild Videos with Object-Centric 3D Awareness

Hang Li, Mingxin Zhang, Zihan Wu +9

Video demonstrations offer a scalable alternative to costly robot data for learning manipulation, yet existing reconstruction-based approaches often rely on constrained camera view…

cs.RO2026

LA4VLA: Learning to Act without Seeing via Language-Action Pretraining

Tao Lin, Yuxin Du, Yiran Mao +13

Vision-Language-Action (VLA) models are commonly pretrained on robot demonstrations by jointly mapping visual observations and language instructions to actions. However, dense visu…

cs.RO2026

AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding

Qize Yu, Jiadi You, Yuran Wang +12

Vision-Language-Action (VLA) models leverage the rich world knowledge of pretrained vision-language models (VLMs) to enable instruction-following robotic manipulation. However, the…

cs.RO2026

SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds

Yunsong Zhou, Hangxu Liu, Xuekun Jiang +12

Robotic manipulation with deformable objects represents a data-intensive regime in embodied learning, where shape, contact, and topology co-evolve in ways that far exceed the varia…

cs.RO2026

UltraDexGrasp: Learning Universal Dexterous Grasping for Bimanual Robots with Synthetic Data

Sizhe Yang, Yiman Xie, Zhixuan Liang +4

Grasping is a fundamental capability for robots to interact with the physical world. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the…

cs.RO2026

RoboInter: A Holistic Intermediate Representation Suite Towards Robotic Manipulation

Hao Li, Ziqin Wang, Zi-han Ding +9

Advances in large vision-language models (VLMs) have stimulated growing interest in vision-language-action (VLA) systems for robot manipulation. However, existing manipulation data…

cs.RO2026

Nimbus: A Unified Embodied Synthetic Data Generation Framework

Zeyu He, Yuchang Zhang, Yuanzhen Zhou +10

Scaling data volume and diversity is critical for generalizing embodied intelligence. While synthetic data generation offers a scalable alternative to expensive physical data acqui…