17 citations · 36 across the 39 of their papers we have counts for
44 papers
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…
NavHarness: Adaptive Goals for Agentic Vision-Language Navigation
Haoxiang Shi, Zaijing Li, Muhe Ding +3
Vision-Language Navigation (VLN) requires embodied agents to generate actions based on instructions and observations. General-purpose multimodal agents offer a promising basis for…
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…
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…
ViMax: Agentic Video Generation
Lingxuan Huang, Sizhe He, Hengji Zhou +3
Long-form video generation requires systematic narrative planning and visual consistency that current short-clip methods cannot provide. Existing methods generate isolated sequence…
Technical Report of RoboSpatial Challenge at CVPR 2026: Selective Reasoning Activation and Reference-Frame Disambiguation for Embodied Spatial Reasoning
Yuxiang Xie, Qi Lv, Jianming Xing +4
Vision-language models achieve strong general perception but often struggle with the spatial reasoning required for embodied tasks. We present RoboSpatialBrain, our submission to t…
Efficient Visual Pointing for Embodied AI:Agent-Driven Data Synthesis, Cross-Block Attention, and Iterative Correction
Zijian Hong, Qi Lv, Yuxiang Xie +4
Visual pointing maps a language instruction to pixel co ordinates, a core skill for embodied AI. We describe our PointArena 2026 solution, which achieves 77.2% overall accuracy and…
Learning New Tasks via Reusable Skills: Skill-Compositional Experts for Embodied Continual Learning
Shuaike Zhang, Shaokun Wang, Haoyu Tang +2
Embodied Continual Learning (ECL) aims to enable robots to continually acquire new manipulation tasks while retaining previously learned behaviors under closed-loop control. In ECL…
Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action
Cheng Zhang, Qing Cai, Xingzheng Wu +6
Foundation models have demonstrated impressive performance in enhancing healthcare efficiency across a wide range of medical applications. Nevertheless, their limited ability to pe…
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…
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…
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…
DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation
Haoxiang Shi, Xiang Deng, Zaijing Li +3
Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural language instructions through free-form 3D spaces. Existing VLN-CE approaches typic…
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…
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…
Beyond Quantity: Distribution-Aware Labeling for Visual Grounding
Yichi Zhang, Gongwei Chen, Jun Zhu +2
Visual grounding requires large and diverse region-text pairs. However, manual annotation is costly and fixed vocabularies restrict scalability and generalization. Existing pseudo-…
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…