1 citations · 4 across the 14 of their papers we have counts for
14 papers
IOI: Decoupling Kinematics and Physics for Interactive World Models
Chengyu Bai, Peidong Jia, Tiecheng Guo +11
Developing generalist embodied agents requires interactive environments providing visually realistic feedback and accurate action-conditioned dynamics. Interactive world models add…
MV-WAM: Manifold-Aware World Action Model with Value Augmentation
Jintao Chen, Peidong Jia, Qingpo Wuwu +13
Achieving robust and generalizable manipulation across diverse environments remains a fundamental challenge in embodied robotics. Recent world action models achieve strong in-domai…
SafeDojo: Safe Reinforcement Learning for VLA via Interactive World Model
Kai Tang, Peidong Jia, Zhong Chu +16
Safe control is a prerequisite for real-world embodied intelligence, for which safe reinforcement learning has emerged as a promising paradigm. However, existing safe reinforcement…
VEGA: Visual Encoder Grounding Alignment for Spatially-Aware Vision-Language-Action Models
Hao Wang, Xiaobao Wei, Jingyang He +10
Precise spatial reasoning is fundamental to robotic manipulation, yet the visual backbones of current vision-language-action (VLA) models are predominantly pretrained on 2D image d…
PhysicsMind: Sim and Real Mechanics Benchmarking for Physical Reasoning and Prediction in Foundational VLMs and World Models
Chak-Wing Mak, Guanyu Zhu, Boyi Zhang +16
Modern foundational Multimodal Large Language Models (MLLMs) and video world models have advanced significantly in mathematical, common-sense, and visual reasoning, but their grasp…
Wow, wo, val! A Comprehensive Embodied World Model Evaluation Turing Test
Chun-Kai Fan, Xiaowei Chi, Xiaozhu Ju +18
As world models gain momentum in Embodied AI, an increasing number of works explore using video foundation models as predictive world models for downstream embodied tasks like 3D p…
WoW: Towards a World omniscient World model Through Embodied Interaction
Xiaowei Chi, Peidong Jia, Chun-Kai Fan +33
Humans develop an understanding of intuitive physics through active interaction with the world. This approach is in stark contrast to current video models, such as Sora, which rely…
Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain
Yulin Luo, Chun-Kai Fan, Menghang Dong +19
Building robots that can perceive, reason, and act in dynamic, unstructured environments remains a central challenge. Recent embodied systems often follow a dual-system paradigm, w…
SEEA-R1: Tree-Structured Reinforcement Fine-Tuning for Self-Evolving Embodied Agents
Wanxin Tian, Shijie Zhang, Kevin Zhang +12
Self-evolution, the ability of agents to autonomously improve their reasoning and behavior, is essential for the embodied domain with long-horizon, real-world tasks. Despite curren…
AutoV: Loss-Oriented Ranking for Visual Prompt Retrieval in LVLMs
Yuan Zhang, Chun-Kai Fan, Sicheng Yu +6
Inspired by text prompts in large language models, visual prompts have been explored to enhance the perceptual capabilities of large vision-language models (LVLMs). However, perfor…
EVA: An Embodied World Model for Future Video Anticipation
Xiaowei Chi, Chun-Kai Fan, Hengyuan Zhang +8
Video generation models have made significant progress in simulating future states, showcasing their potential as world simulators in embodied scenarios. However, existing models o…
SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
Yuan Zhang, Chun-Kai Fan, Junpeng Ma +8
In vision-language models (VLMs), visual tokens usually bear a significant amount of computational overhead despite sparsity of information in them when compared to text tokens. To…
SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-Tuning
Gaole Dai, Chun-Kai Fan, Yiming Tang +7
Advances in Parameter-Efficient Fine-Tuning (PEFT) bridged the performance gap with Full Fine-Tuning (FFT) through sophisticated analysis of pre-trained parameter spaces. Starting…
Unveiling the Tapestry of Consistency in Large Vision-Language Models
Yuan Zhang, Fei Xiao, Tao Huang +7
Large vision-language models (LVLMs) have recently achieved rapid progress, exhibiting great perception and reasoning abilities concerning visual information. However, when faced w…