1 citations · 1 across the 18 of their papers we have counts for
19 papers
ETA: A New Agentic Paradigm for Embodied Tasks
Yitong Chen, Zezheng Huai, Sixian Li +7
When will robots have their ChatGPT moment? Such a breakthrough requires a general-purpose robot that can handle unfamiliar tasks in unfamiliar environments, remain controllable ov…
WCM: A World Critic Model for Vision-Language-Action Reinforcement Learning
Senyu Fei, Xiaopeng Yu, Siyin Wang +3
Reinforcement learning (RL) post-training of Vision-Language-Action (VLA) models has shown strong promise for robotic manipulation. Among RL methods, critic-based approaches rely o…
Let It Be Simple: One-Step Action Generation for Vision-Language-Action Models
Yitong Chen, Shiduo Zhang, Jingjing Gong +1
Generating diverse images from sparse text is hard; generating compact actions from rich observations is easier. From the condition-target view, Vision-Language-Action (VLA) thus a…
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…
HiMe: Hierarchical Embodied Memory for Long-Horizon Vision-Language-Action Control
Li Ji, Siyin Wang, Pengfang Qian +5
Current Vision-Language-Action (VLA) models excel at robotic manipulation but often struggle with non-Markovian tasks requiring long-term memory and reasoning due to their reliance…
In-Context World Modeling for Robotic Control
Siyin Wang, Junhao Shi, Senyu Fei +4
Modern Vision-Language-Action (VLA) models often fail to generalize to novel setups, such as altered camera viewpoints or robot morphologies, because they are typically conditioned…