1 citations · 2 across the 10 of their papers we have counts for
10 papers
From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data
Zhiyuan Feng, Qixiu Li, Huizhi Liang +12
Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most existing approaches rely on large…
WorldReasonBench: Human-Aligned Stress Testing of Video Generators as Future World-State Predictors
Keming Wu, Yijing Cui, Wenhan Xue +11
Commercial video generation systems such as Seedance2.0 and Veo3.1 have rapidly improved, strengthening the view that video generators may be evolving into "world simulators." Yet…
HiSpatial: Taming Hierarchical 3D Spatial Understanding in Vision-Language Models
Huizhi Liang, Yichao Shen, Yu Deng +5
Achieving human-like spatial intelligence for vision-language models (VLMs) requires inferring 3D structures from 2D observations, recognizing object properties and relations in 3D…
Does LLM Alignment Really Need Diversity? An Empirical Study of Adapting RLVR Methods for Moral Reasoning
Zhaowei Zhang, Xiaohan Liu, Xuekai Zhu +6
Reinforcement learning with verifiable rewards (RLVR) has achieved remarkable success in logical reasoning tasks, yet whether large language model (LLM) alignment requires fundamen…
HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies
Zhiying Du, Bei Liu, Yaobo Liang +7
Generalist vision--language--action (VLA) policies are typically trained on heterogeneous mixtures of robot demonstrations spanning diverse embodiments, action spaces, and observat…
Discover, Learn, and Reinforce: Scaling Vision-Language-Action Pretraining with Diverse RL-Generated Trajectories
Rushuai Yang, Zhiyuan Feng, Tianxiang Zhang +6
Scaling vision-language-action (VLA) model pre-training requires large volumes of diverse, high-quality manipulation trajectories. Most current data is obtained via human teleopera…
Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos
Qixiu Li, Yu Deng, Yaobo Liang +14
This paper presents a novel approach for pretraining robotic manipulation Vision-Language-Action (VLA) models using a large corpus of unscripted real-life video recordings of human…
Seeing Across Views: Benchmarking Spatial Reasoning of Vision-Language Models in Robotic Scenes
Zhiyuan Feng, Zhaolu Kang, Qijie Wang +16
Vision-language models (VLMs) are essential to Embodied AI, enabling robots to perceive, reason, and act in complex environments. They also serve as the foundation for the recent V…
Beyond Human Demonstrations: Diffusion-Based Reinforcement Learning to Generate Data for VLA Training
Rushuai Yang, Hangxing Wei, Ran Zhang +8
Vision-language-action (VLA) models have shown strong generalization across tasks and embodiments; however, their reliance on large-scale human demonstrations limits their scalabil…
HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
Zhaolu Kang, Junhao Gong, Jiaxu Yan +15
Multimodal Large Language Models (MLLMs) have demonstrated significant potential to advance a broad range of domains. However, current benchmarks for evaluating MLLMs primarily emp…