1 citations · 2 across the 7 of their papers we have counts for
9 papers
GroundingPI: A Grounding Foundation Model towards Physical Intelligence with Visual Primitives
Qize Yu, Lianrui Fan, Boyu Chen +23
Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tiny objects, and it has to be fast enough for closed-loop cont…
WB-WAM: Heterogeneous Body-Hand Pre-training for Humanoid Loco-Manipulation
Chuan Qin, Shaoting Zhu, Siyuan Luo +4
Humanoid loco-manipulation demands coordinated body and hand behavior, while conventional robot pre-training data provide limited coverage of such whole-body motion. We present WB-…
HarnessEval-W: Agentifying the Evaluation of Visual Worlds
Weiliang Chen, Haowen Sun, Jun Gao +40
A benchmark should deliver more than a scalar score: what makes an evaluation trustworthy is the reasoning that justifies the score. This is especially critical for world models, w…
OMG: Omni-Modal Motion Generation for Generalist Humanoid Control
Siqiao Huang, Kun-Ying Lee, Dongming Qiao +5
Humanoid whole-body control has made significant progress in recent years, yet existing approaches remain limited to few-skill policies with heavy reward engineering, or motion tra…
Nano World Models: A Minimalist Implementation of Future Video Prediction
Siqiao Huang, Partha Kaushik, Michael Chen +5
World models have become a central paradigm for learning predictive simulators that support generation, planning, and decision-making. Yet, despite rapid progress in industry-scale…
MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI
Bohan Lyu, Yucheng Yang, Siqiao Huang +25
Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes. As large language models demonstrate advanced capabilities i…
Vid2World: Crafting Video Diffusion Models to Interactive World Models
Siqiao Huang, Jialong Wu, Qixing Zhou +2
World models, which predict future transitions from past observation and action sequences, have shown great promise for improving data efficiency in sequential decision-making. How…
SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors
Bohan Lyu, Siqiao Huang, Zichen Liang
Neural surrogate models are powerful and efficient tools in data mining. Meanwhile, large language models (LLMs) have demonstrated remarkable capabilities in code-related tasks, su…
Trajectory World Models for Heterogeneous Environments
Shaofeng Yin, Jialong Wu, Siqiao Huang +4
Heterogeneity in sensors and actuators across environments poses a significant challenge to building large-scale pre-trained world models on top of this low-dimensional sensor info…