8 papers · 1 filter
PerturBot: Breaking Shortcut Priors in Vision-Language-Action Models with Perturbative Training
Mingyu Liu, Chonghao Sima, Tianjian Feng +4
A vision--language--action (VLA) policy can complete complex tasks while ignoring the evidence that should determine its actions. An object held near the wrist camera can displace…
Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning
Zishuo Li, Bowen Yang, Changtao Miao +29
Egocentric videos of human manipulation provide scalable supervision for embodied intelligence, yet existing resources rarely combine low-cost continuous capture, manipulation-leve…
CoRE-VLA: Towards Scalable and Robust Vision-Language-Action Modeling via Conditional Routing of Experts
Haozhe Zhang, Sixian Li, Yifei Zhang +5
Vision-language-action (VLA) models have advanced generalist robotic manipulation, yet real-world deployment reveals a fundamental challenge: robots are equipped with diverse and h…
3DGSNav: Enhancing Vision-Language Model Reasoning for Object Navigation via Active 3D Gaussian Splatting
Wancai Zheng, Hao Chen, Xianlong Lu +2
Object navigation is a core capability of embodied intelligence, enabling an agent to locate target objects in unknown environments. Recent advances in vision-language models (VLMs…
StaMo: Unsupervised Learning of Generalizable Robot Motion from Compact State Representation
Mingyu Liu, Jiuhe Shu, Hui Chen +6
A fundamental challenge in embodied intelligence is developing expressive and compact state representations for efficient world modeling and decision making. However, existing meth…
NoTVLA: Semantics-Preserving Robot Adaptation via Narrative Action Interfaces
Zheng Huang, Mingyu Liu, Xiaoyi Lin +9
Vision-Language-Action (VLA) models represent a pivotal advance in embodied intelligence, yet they confront critical barriers to real-world deployment, most notably catastrophic fo…
Reinforced Embodied Planning with Verifiable Reward for Real-World Robotic Manipulation
Zitong Bo, Yue Hu, Jinming Ma +7
Enabling robots to execute long-horizon manipulation tasks from free-form language instructions remains a fundamental challenge in embodied AI. While vision-language models (VLMs)…
ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks
Kaijun Wang, Liqin Lu, Mingyu Liu +7
Language-guided long-horizon mobile manipulation has long been a grand challenge in embodied semantic reasoning, generalizable manipulation, and adaptive locomotion. Three fundamen…