7 citations · 17 across the 27 of their papers we have counts for
34 papers
Vision-Language-Delta-Action: Learning Compact Deltas as Actionable Transition Interfaces for Robotic Manipulation
Lingsen Zhang, Wei Li, Jie He +5
Robotic manipulation is not merely observation-to-action generation, but the realization of task-specified state changes in the physical world. Existing vision-language-action (VLA…
World-Calibrated Proposal-to-Action Flow for Vision-Language-Action Models
Jie He, Wei Li, Junwen Tong +3
Flow-based Vision-Language-Action (VLA) policies generate action chunks by transporting samples from a task-agnostic isotropic Gaussian source. As this source is conditioned on nei…
ATI-VLA: Action-Centric Predictive Vision-Language-Action Models via Actionable Alignment Then Adaptive Injection
Yijie Zhu, Rui Shao, Jie He +8
Predictive Vision-Language-Action (VLA) models aim to improve robotic manipulation via future observation or world dynamics forecasting. However, existing approaches often fail to…
Inline Memory Meets Reusable Skills: Memory-centric Framework for Vision-Language-Action Model
Zaijing Li, Rui Shao, Bing Hu +3
Vision-Language-Action (VLA) models have shown strong promise for general-purpose robotic manipulation, yet adapting them to new tasks and domains remains inefficient: existing met…
In-Context Learning for Robots: Methods and Applications
Haojian Huang, Zexi Li, Junhao Guo +36
General-purpose robots must infer what a new task requires and translate that understanding into appropriate physical action. In-context learning (ICL) for robots supports this pro…
UniMPA: A Unified Memory-Prediction-Action Model via Action-Grounded Transition Modeling
Wei Li, Rui Shao, Jie He +3
Recent advances in Vision-Language-Action (VLA) models have improved robotic manipulation, yet observation-to-action learning remains limited by a fundamental transition realizabil…
Learning to Use Imagination: Progress-Conditioned Future Utilization for World Action Models
Yijie Zhu, Zitong Yu, Wei Li +4
World Action Models (WAMs) extend Vision-Language-Action (VLA) models by incorporating future visual dynamics into action generation. However, existing WAMs often utilize imagined…
LookAgain: Closed-Loop GUI Grounding with Visually Grounded Reflection
Renshan Zhang, Haoyang Meng, Yixiao He +3
Recent graphical user interface (GUI) grounders have significantly advanced single-shot accuracy on standard benchmarks, yet their performance degrades sharply on small targets, de…
From Abstraction to Instantiation: Learning Behavioral Representation for Vision-Language-Action Model
Bing Hu, Zaijing Li, Rui Shao +4
Vision-Language-Action (VLA) models often suffer from performance degradation under distribution shifts, as they struggle to learn generalized behavior representations across varyi…
ConsisVLA-4D: Advancing Spatiotemporal Consistency in Efficient 3D-Perception and 4D-Reasoning for Robotic Manipulation
Wei Li, Jizhihui Liu, Li Yixing +3
Current Vision-Language-Action (VLA) models primarily focus on mapping 2D observations to actions, but exhibit notable limitations in spatiotemporal perception and reasoning: 1) sp…
HATS: Hardness-Aware Trajectory Synthesis for GUI Agents
Rui Shao, Ruize Gao, Bin Xie +5
Graphical user interface (GUI) agents powered by large vision-language models (VLMs) have shown remarkable potential in automating digital tasks, highlighting the need for high-qua…
Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation
Zaijing Li, Bing Hu, Rui Shao +5
Hierarchical Vision-Language-Action (VLA) models have rapidly become a dominant paradigm for robotic manipulation. It typically comprising a Vision-Language backbone for perception…
PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records
Yibo Lyu, Gongwei Chen, Rui Shao +2
While GUI agents have shown strong performance under explicit and completion instructions, real-world deployment requires aligning with users' more complex implicit intents. In thi…
SemanticVLA: Semantic-Aligned Sparsification and Enhancement for Efficient Robotic Manipulation
Wei Li, Renshan Zhang, Rui Shao +4
Vision-Language-Action (VLA) models have advanced in robotic manipulation, yet practical deployment remains hindered by two key limitations: 1) perceptual redundancy, where irrelev…
CogVLA: Cognition-Aligned Vision-Language-Action Model via Instruction-Driven Routing & Sparsification
Wei Li, Renshan Zhang, Rui Shao +2
Recent Vision-Language-Action (VLA) models built on pre-trained Vision-Language Models (VLMs) require extensive post-training, resulting in high computational overhead that limits…
Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
Rui Shao, Wei Li, Lingsen Zhang +4
Robotic manipulation, a key frontier in robotics and embodied AI, requires precise motor control and multimodal understanding, yet traditional rule-based methods fail to scale or g…
UniEmo: Unifying Emotional Understanding and Generation with Learnable Expert Queries
Yijie Zhu, Lingsen Zhang, Zitong Yu +3
Emotional understanding and generation are often treated as separate tasks, yet they are inherently complementary and can mutually enhance each other. In this paper, we propose the…
PUMA: Layer-Pruned Language Model for Efficient Unified Multimodal Retrieval with Modality-Adaptive Learning
Yibo Lyu, Rui Shao, Gongwei Chen +3
As multimedia content expands, the demand for unified multimodal retrieval (UMR) in real-world applications increases. Recent work leverages multimodal large language models (MLLMs…
Mirage-1: Augmenting and Updating GUI Agent with Hierarchical Multimodal Skills
Yuquan Xie, Zaijing Li, Rui Shao +5
Recent efforts to leverage the Multi-modal Large Language Model (MLLM) as GUI agents have yielded promising outcomes. However, these agents still struggle with long-horizon tasks i…
Optimus-3: Dual-Router Aligned Mixture-of-Experts Agent with Dual-Granularity Reasoning-Aware Policy Optimization
Zaijing Li, Yuquan Xie, Rui Shao +5
Developing generalist agents capable of solving open-ended tasks in visually rich, dynamic environments remains a core pursuit of embodied AI. While Minecraft has emerged as a comp…
STAR: Learning Diverse Robot Skill Abstractions through Rotation-Augmented Vector Quantization
Hao Li, Qi Lv, Rui Shao +4
Transforming complex actions into discrete skill abstractions has demonstrated strong potential for robotic manipulation. Existing approaches mainly leverage latent variable models…
GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent
Bin Xie, Rui Shao, Gongwei Chen +5
GUI automation faces critical challenges in dynamic environments. MLLMs suffer from two key issues: misinterpreting UI components and outdated knowledge. Traditional fine-tuning me…
DiVE: Efficient Multi-View Driving Scenes Generation Based on Video Diffusion Transformer
Junpeng Jiang, Gangyi Hong, Miao Zhang +4
Collecting multi-view driving scenario videos to enhance the performance of 3D visual perception tasks presents significant challenges and incurs substantial costs, making generati…
Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation
Qi Lv, Hao Li, Xiang Deng +6
Despite the significant success of imitation learning in robotic manipulation, its application to bimanual tasks remains highly challenging. Existing approaches mainly learn a poli…