Vision-Language-Action Models

Also known as VLA

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Latest papers 1,107

221 papers in the last four weeks, up 110% on the four weeks before. 1.5% of all new papers.

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Splat2Act: From Captured Scenes to Personalized VLA Manipulation
Oct 9, 2026Xiaomeng Ye, Krishna Jaganathan, Gyubeum Lim +3Sim-to-Real Transfer Vision-Language-Action Models
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA. · Waymo LLC, Mountain View, CA, USA. · Toyota Research Institute, Cambridge, MA, USA.
Noise Space Learning for VLA Adaptation
Oct 9, 2026Junjie Lu, Chuheng Zhang, Yucen Wang +4Vision-Language-Action Models VLMs for Robotics
University of Technology Sydney · Microsoft Research · Nanjing University +1
Dual-Stream Memory for Vision-Language-Action Policies
Oct 9, 2026Yang Wang, Ping Liu, Ruiyu Wang +3Memory-Augmented VLMs Visuomotor Policy Learning
Magic-Lab Team, Magiclab Robotics Inc. · University of California, Los Angeles, USA
Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models
Oct 9, 2026Min Lin, Bokai Zhou, Jianqi Lin +8Vision-Language-Action Models Robot Imitation Learning
Sun Yat-sen University · Yinwang Intelligent Technology Co., Ltd.
Forward Dynamics Models
Oct 9, 2026Zipei Ma, Junzhe Jiang, Jiahui Zhang +6Robot Dynamics Learning Vision-Language-Action Models
School of Data Science, Fudan University · Shanghai Innovation Institute · Imperial College London +1
Evo-World: A Lightweight Vision-Language-Action Model with Future Motion Prediction via Optical-Flow Supervision
Oct 9, 2026Zile Zhong, Yuxin Du, Tao Lin +4Vision-Language-Action Models Efficient VLA Models
School of AI, Shanghai Jiao Tong University · Zhejiang University · University of Electronic Science and Technology of China +1
OGPO: Offline Goal-conditioned Policy Optimization for Recoverable Vision-Language-Action Models
Oct 9, 2026Xule Gao, Xi Wang, Zehua Zang +3Vision-Language-Action Models Robotic Manipulation
National Key Laboratory of Space Integrated Information System · Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences +1
Evidence-Guided Counterfactual Learning for Long-Tailed Robotic Policies
Oct 9, 2026Weiwei Chen, Chenghao Xu, Meiling Yu +2Robot Policy Learning Robot Learning
Hohai University
Spa-Bench: A Real-Robot Benchmark for Spatial Grounding in Vision-Language-Action Models
Oct 8, 2026Justin Tien-Smith, Matthew Pidden, Rolandos Alexandros PotamiasVision-Language Grounding Vision-Language-Action Models
Imperial College London
VersaCamVLA: Camera-Configurable VLA Policies for Robotic Manipulation
Oct 8, 2026Boyao Han, Chen Shi, Jingjing Qian +2Vision-Language-Action Models Robotic Manipulation
The Chinese University of Hong Kong, Shenzhen · Shenzhen Loop Area Institute · Harbin Institute of Technology, Shenzhen
PLaW-VLA: Predictive Latent World Modeling for Vision-Language-Action Policies
Oct 8, 2026Yu Liu, Hetian Guo, Tianlv Huang +11Vision-Language-Action Models Latent World Models
Jilin University · Astribot · Harbin Institute of Technology, Shenzhen +2
ContourVLA: A Closed-Loop Perception-Action Contour Policy for Generalized Referring Expression Segmentation
Oct 8, 2026Ruicheng Zhang, Kaiwen Shen, Jiaqi Hou +6Referring Expression Segmentation Referring Image Segmentation
Sun Yat-sen University · Tsinghua University · The Chinese University of Hong Kong, Shenzhen +1
Recompose and Refine Latent Reasoning Flows for Vision-Language-Action Models
Oct 8, 2026Hongyu Shi, Sen Zhao, Zuyu Zhang +5Vision-Language-Action Models VLMs for Robotics
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China · Academy of Advanced Interdisciplinary Studies, Chongqing University of Posts and Telecommunications, Chongqing, China · School of Artificial Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China +2
REACT: Rolling Denoising and Dual Decoupling for Reactive Robot Control with VLA Models
Oct 8, 2026Houlong Xiong, Zhenqi Qiu, Zechen Wang +8Robotic Control Vision-Language-Action Models
Shanghai Jiao Tong University · PrimeBot · ShanghaiTech University +2
Tell Robot What Not to Do: A Negation Understanding Perspective
Oct 8, 2026Fazeng Li, Gan Sun, Hao Cheng +2Vision-Language-Action Models Reasoning for VLA Models
South China University of Technology · Harbin Institute of Technology
PathTime-VLA: Path-Time Decoupling for Factorized Post-Training of Vision-Language-Action Policies
Oct 8, 2026Qing Huang, Yifei Yang, Ziqing Zou +5Robot Policy Learning Vision-Language-Action Models
Zhejiang University
WARP-VLA: Wrist-Camera Adaptation for View-Robust Policy Execution in Vision-Language-Action Models
Oct 8, 2026Junmyeong Lee, Dongmin Shin, Min-Gyu Park +3Robustness of VLA Models Vision-Language-Action Models
Yonsei University. · Korea Electronics Technology Institute(KETI).
Rewiring Semantics, Dynamics, and Control: A Simple yet Effective Action-Centric Tri-Stream Transformer
Oct 8, 2026Shuang Luo, Yilun Kong, Yunpeng Qing +5Vision-Language-Action Models Language-Conditioned Robot Manipulation
Nanyang Technological University · ACE Robotics
SimVLA: Zero-Shot Sim-to-Real VLA Learning for Mobile Manipulation
Oct 8, 2026Kyoungin Baik, Youngwoon LeeSim-to-Real Transfer Vision-Language-Action Models
Yonsei University · Seoul National University
When Listening Becomes Easier: Scrubbing Visual Cues for Shortcut-Free VLAs
Oct 7, 2026Jasper Gerigk, Kenzo Aspuru-Takata, Chin-Hsuan Wu +3Vision-Language-Action Models Robustness of VLA Models
University of Toronto, Toronto, ON M5S 3H5, Canada · Vector Institute, Toronto, ON M5G 0C6, Canada
Do Vision-Language-Action Models Understand Instructions? A Mechanistic Interpretability Study on Language Grounding
Oct 7, 2026Theodor Wulff, Angelo CangelosiVision-Language-Action Models Robustness of VLA Models
Department of Computer Science The University of Manchester Manchester, United Kingdom
Juno: Taming Predictive Latents for Vision-Language-Action Models
Oct 7, 2026Yuchen Zhu, Chenyi Xu, Yulin Zhang +2Vision-Language-Action Models World Models for Robotics
University of Science and Technology of China · Eastern Institute of Technology, Ningbo · Hangzhou Dianzi University +1
YUBI-STAG: Contact and Semantic-Rich Alignment for VLAs via Automated Video-Language Grounding
Oct 7, 2026Masatoshi Tateno, Takehiko Ohkawa, Yueh-Hua Wu +4Vision-Language Grounding VLMs for Robotics
AI Robot Association (AIRoA) · The University of Tokyo · Institute of Science Tokyo
SpikingVLA: Asynchronous Spiking Vision-Language-Action Models
Oct 7, 2026Jingya Wang, Dehao Zhang, Shuai Wang +3Efficient VLA Model Inference Spiking Neural Networks
University of Electronic Science and Technology of China · Shenzhen Loop Area Institute · The Chinese University of Hong Kong (Shenzhen)
Sparse Feature Policy Unlearning Mitigates State Hallucination in Vision-Language-Action Models
Oct 7, 2026Jiho Lee, Jeongeun Park, Heayoun Choi +2Vision-Language-Action Models Robotic Manipulation
School of Computer Science Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea · School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, 30308, USA · NAVER AI Lab, Seongnam, 13561, Republic of Korea
TempoBridge: Language-Guided Tempo Control for Vision-Language-Action Policies
Oct 7, 2026Yeonseo Lee, Hyosup Shin, Guebin Hwang +1Language-Conditioned Robot Control Vision-Language-Action Models
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
DIVA: Dual-Space Intent-Aware Visual Attenuation for Vision-Language-Action Policies
Oct 6, 2026Kaixi Feng, Guoheng Sun, Ziyao Wang +3Vision-Language-Action Models Robustness of VLA Models
University of Maryland, College Park
PAIR: Bridging Perception and Action in Vision-Language-Action Models
Oct 6, 2026Kaixi Feng, Guoheng Sun, Ang liVision-Language-Action Models Robot Learning
University of Maryland, College Park
CARE: Certifying Acceleration for Vision-Language-Action Inference
Oct 6, 2026Rui Liu, Tong Zheng, Jindong Gu +1Efficient VLA Model Inference Vision-Language-Action Models
University of Maryland, College Park · University of Oxford · Google
WareFly-VLA: A Vision-Language-Action Framework for UAV Navigation and Human Tracking in Smart Warehouses
Oct 6, 2026Thinh D. Le, Son T. Nguyen, Duong Q. Nguyen +3Aerial Robotics UAV Navigation
Center for AI Research, VinUniversity, Ho Chi Minh City, Vietnam · College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam · VinRobotics, Hanoi, Vietnam

Oct 9, 2026J/K move · Enter open · S save

Splat2Act: From Captured Scenes to Personalized VLA Manipulation

Xiaomeng Ye, Krishna Jaganathan, Gyubeum Lim, Gunjan Chhablani+2

Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA. · Waymo LLC, Mountain View, CA, USA. · Toyota Research Institute, Cambridge, MA, USA.

Sim-to-Real Transfer Vision-Language-Action Models Robot Imitation Learning

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A user who brings a generalist manipulation policy into their home or office wants it to pick up their objects from their shelves, counters, and tables. Yet even a strong generalist like

π_{0.5}

achieves 0% pickup success in our captured deployment scenes without adaptation, and collecting real demonstrations in every user's environment is prohibitively expensive. We present Splat2Act, an automated real-to-sim-to-real pipeline that personalizes a pretrained vision-language-action (VLA) policy to a specific scene and object set using only a handheld phone scan and images of the user's objects. Unlike prior real-to-sim work that reconstructs individual tabletops, Splat2Act operates on building-scale indoor environments: it builds a photorealistic digital twin with 3D Gaussian Splatting and collision geometry, automatically discovers tabletop and non-tabletop receptacles, constructs feasible pickup tasks, and generates successful demonstrations with a privileged simulation oracle, with only scene and object capture required from a human. Applied to 101 scanned campus environments with a median navigable area of 104.87 m

^2

and 7 receptacles, the pipeline yields 852 receptacles and over 68,000 pickup demonstrations. We fine-tune

π_{0.5}

in two stages: Stage 1 adapts to hundreds of public object assets, drawn either from the deployment scene alone or from all 101 scenes; Stage 2 personalizes to the user's own objects on the deployment scene's receptacles. Our strongest personalized policy reaches 62.5% aggregate pickup success in the target scene. Deployed on a real Stretch 3, the same policy achieves 63.9% success on a 12-task subset, closely tracking its 66.7% simulated success on those tasks. These results suggest that automated real-to-sim-to-real training can personalize VLA manipulation policies using only simulation-oracle data.