7 papers
DreamAvoid: Critical-Phase Test-Time Dreaming to Avoid Failures in VLA Policies
Xianzhe Fan, Yuxiang Lu, Shenyuan Gao +4
Vision-Language-Action (VLA) models are often brittle in fine-grained manipulation, where minor action errors during the critical phases can rapidly escalate into irrecoverable fai…
ReSim: Reliable World Simulation for Autonomous Driving
Jiazhi Yang, Kashyap Chitta, Shenyuan Gao +7
How can we reliably simulate future driving scenarios under a wide range of ego driving behaviors? Recent driving world models, developed exclusively on real-world driving data com…
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…
Seeing Farther and Smarter: Value-Guided Multi-Path Reflection for VLM Policy Optimization
Yanting Yang, Shenyuan Gao, Qingwen Bu +2
Solving complex, long-horizon robotic manipulation tasks requires a deep understanding of physical interactions, reasoning about their long-term consequences, and precise high-leve…
3D and 4D World Modeling: A Survey
Lingdong Kong, Wesley Yang, Yu Yang +21
World modeling has become a cornerstone in AI research, enabling agents to understand, represent, and predict the dynamic environments they inhabit. While prior work largely emphas…
UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
Qingwen Bu, Yanting Yang, Jisong Cai +5
A generalist robot should perform effectively across various environments. However, most existing approaches heavily rely on scaling action-annotated data to enhance their capabili…