4 citations · 6 across the 10 of their papers we have counts for
12 papers
Neuro-Symbolic Synergy for World Modeling
Hongyu Zhao, Siyu Zhou, Haolin Yang +2
Large language models (LLMs) exhibit strong general-purpose reasoning capabilities, yet they frequently hallucinate when used as world models (WMs), where strict compliance with de…
Visual Spatial Tuning
Rui Yang, Ziyu Zhu, Yanwei Li +9
Capturing spatial relationships from visual inputs is a cornerstone of human-like general intelligence. Several previous studies have tried to enhance the spatial awareness of Visi…
MindJourney: Test-Time Scaling with World Models for Spatial Reasoning
Yuncong Yang, Jiageng Liu, Zheyuan Zhang +5
Spatial reasoning in 3D space is central to human cognition and indispensable for embodied tasks such as navigation and manipulation. However, state-of-the-art vision-language mode…
3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World Model
Hongyan Zhi, Peihao Chen, Siyuan Zhou +4
Manipulation has long been a challenging task for robots, while humans can effortlessly perform complex interactions with objects, such as hanging a cup on the mug rack. A key reas…
MinD: Learning A Dual-System World Model for Real-Time Planning and Implicit Risk Analysis
Xiaowei Chi, Kuangzhi Ge, Jiaming Liu +9
Video Generation Models (VGMs) have become powerful backbones for Vision-Language-Action (VLA) models, leveraging large-scale pretraining for robust dynamics modeling. However, cur…
Learning 3D Persistent Embodied World Models
Siyuan Zhou, Yilun Du, Yuncong Yang +4
The ability to simulate the effects of future actions on the world is a crucial ability of intelligent embodied agents, enabling agents to anticipate the effects of their actions a…
TesserAct: Learning 4D Embodied World Models
Haoyu Zhen, Qiao Sun, Hongxin Zhang +4
This paper presents an effective approach for learning novel 4D embodied world models, which predict the dynamic evolution of 3D scenes over time in response to an embodied agent's…
AdaWorld: Learning Adaptable World Models with Latent Actions
Shenyuan Gao, Siyuan Zhou, Yilun Du +2
World models aim to learn action-controlled future prediction and have proven essential for the development of intelligent agents. However, most existing world models rely heavily…
Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
Shuhao Gu, Jialing Zhang, Siyuan Zhou +23
Recently, Vision-Language Models (VLMs) have achieved remarkable progress in multimodal tasks, and multimodal instruction data serves as the foundation for enhancing VLM capabiliti…
RoboDreamer: Learning Compositional World Models for Robot Imagination
Siyuan Zhou, Yilun Du, Jiaben Chen +3
Text-to-video models have demonstrated substantial potential in robotic decision-making, enabling the imagination of realistic plans of future actions as well as accurate environme…
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning
Kunyang Lin, Yufeng Wang, Peihao Chen +4
Learning optimal behavior policy for each agent in multi-agent systems is an essential yet difficult problem. Despite fruitful progress in multi-agent reinforcement learning, the c…
Adaptive Online Replanning with Diffusion Models
Siyuan Zhou, Yilun Du, Shun Zhang +5
Diffusion models have risen as a promising approach to data-driven planning, and have demonstrated impressive robotic control, reinforcement learning, and video planning performanc…