VLMs for Robotics

VLM: Vision-Language Model

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92 papers in the last four weeks, up 608% on the four weeks before. 0.9% of all new papers.

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RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data
May 13, 2026Harold Haodong Chen, Sirui Chen, Yingjie Xu +2Robot Skill Learning Synthetic Data Generation for Robotics
1The Hong Kong University of Science and Technology (Guangzhou) · 2The Hong Kong University of Science and Technology
Revealing Interpretable Failure Modes of VLMs
May 12, 2026Isha Chaudhary, Vedaant V Jain, Kavya Sachdeva +2VLM Robustness Vision-Language Models
UIUC · Kumo AI · IIT Delhi
Reinforcing VLAs in Task-Agnostic World Models
May 12, 2026Yucen Wang, Rui Yu, Fengming Zhang +5World Model Learning World Models
Nanjing University · Interns at Microsoft · University of Illinois at Urbana-Champaign +4
See What Matters: Differentiable Grid Sample Pruning for Generalizable Vision-Language-Action Model
May 12, 2026Yixu Feng, Zinan Zhao, Yanxiang Ma +4Vision-Language-Action Models VLMs for Robotics
The University of Sydney · City University of Hong Kong · StellarEdge Robotics
Overcoming Dynamics-Blindness: Training-Free Pace-and-Path Correction for VLA Models
May 12, 2026Yanyan Zhang, Chaoda Song, Vikash Singh +6Robotic Control Vision-Language-Action Models
Case Western Reserve University · The Hong Kong Polytechnic University · Tsinghua University +1
SEVO: Semantic-Enhanced Virtual Observation for Robust VLA Manipulation via Active Illumination and Data-Centric Collection
May 11, 2026Tianchonghui Fang, Yuan Zhuang, Fei MiaoRobotic Data Collection Robotic Manipulation
School of Computing, University of Connecticut, Storrs, CT 06268, USA
ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models
May 11, 2026Zuojin Tang, Haoyun Liu, Xinyuan Chang +11Latent Action Learning Vision-Language-Action Models
Zhejiang University · Amap, Alibaba Group · Nanjing University +5
VISOR: A Vision-Language Model-based Test Oracle for Testing Robots
May 11, 2026Prasun Saurabh, Pablo Valle, Aitor Arrieta +2Uncertainty Estimation for VLA Models Automated Software Testing
Simula Research Laboratory and Oslo Metropolitan University · Mondragon University · National Institute of Informatics
LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models
May 11, 2026Boyang Shen, Kaixiang Yang, Hao Wang +4Efficient VLM Inference Efficient VLA Model Inference
1Huazhong University of Science and Technology · 2Wuhan United Imaging Surgical Co.,Ltd. (UIS)
Drift is a Sampling Error: SNR-Aware Power Distributions for Long-Horizon Robotic Planning
May 10, 2026Kewei Chen, Yayu Long, Mingsheng ShangRobotic Control Inference-Time Scaling
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences · Chongqing School, University of Chinese Academy of Sciences
ProcVLM: Learning Procedure-Grounded Progress Rewards for Robotic Manipulation
May 9, 2026Youhe Feng, Hansen Shi, Haoyang Li +7Long-Horizon Robotic Manipulation Multimodal Reward Modeling
School of Information, Renmin University of China · Work done during the internship at Zhipu AI. · Zhipu AI +1
One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy
May 8, 2026Zuojin Tang, Shengchao Yuan, Xiaoxin Bai +4Action-Conditioned World Models Efficient VLA Models
Zhejiang University · Central South University · Harbin Institute of Technology +2
ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations
May 8, 2026Yuhao Zhou, Yunpeng Zhu, Yang Zhou +7Vision-Language Models Efficient VLA Models
Sichuan University · Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education · Zhejiang University +2
Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation
May 8, 2026Yanzhe Chen, Kevin Yuchen Ma, Qi Lv +4Robot Policy Adaptation VLM Adaptation
National University of Singapore · Institude for Information Research, A*STAR · Harbin Institute of Technology (Shenzhen).
VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts
May 7, 2026Yuhua Jiang, Junjie Lu, Xinyao Qin +4Fine-Tuning Vision-Language-Action Models
Microsoft Research Asia · Tsinghua University
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
May 7, 2026Senthil Palanisamy, Abhishek Anand, Satpal Singh Rathore +3Egocentric Vision VLMs for Robotics
FPV Labs
PRISM: Perception Reasoning Interleaved for Sequential Decision Making
May 6, 2026Mohamed Salim Aissi, Clemence Grislain, Clement Romac +4Active Perception VLM Reasoning
Sorbonne Universite, CNRS, ISIR, F-75005 Paris, France · Hugging Face · Inria (Flowers), University of Bordeaux, France +2
From Pixels to Tokens: A Systematic Study of Latent Action Supervision for Vision-Language-Action Models
May 6, 2026Yihan Lin, Haoyang Li, Yang Li +4Latent Action Learning Vision-Language-Action Models
School of Information, Renmin University of China, Beijing, China · Key Laboratory of Data Engineering and Knowledge Engineering, Beijing, China · Engineering Research Center of Database and Business Intelligence, Beijing, China
StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning
May 5, 2026Xiaowen Sun, Matthias Kerzel, Mengdi Li +3Physical Reasoning VLM Adaptation
Department of Informatics, University of Hamburg, Germany · King Abdullah University of Science and Technology, Saudi Arabia
Task-Aware Scanning Parameter Configuration for Robotic Inspection Using Vision Language Embeddings and Hyperdimensional Computing
May 5, 2026Zhiling Chen, David Gorsich, Matthew P. Castanier +3Robotic Inspection Robotics
aSchool of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, Connecticut, USA · bUS Army DEVCOM Ground Vehicle Systems Center, Warren, Michigan, USA
Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference
May 4, 2026Yudong Liu, Yuan Li, Zijia Tang +12Efficient VLM Inference Efficient VLA Model Inference
1Duke University · 2Qualcomm AI Research · University of Florida
CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation
May 4, 2026Berk Çiçek, Mert K. Er, Ozgur S. OguzLanguage Model-Based Control Large Language Model-Based Robot Planning
LiRA Lab, Department of Computer Engineering, Bilkent University, Ankara, Türkiye
A Semantic Autonomy Framework for VLM-Integrated Indoor Mobile Robots: Hybrid Deterministic Reasoning and Cross-Robot Adaptive Memory
May 4, 2026Bogdan Felician Abaza, Andrei-Alexandru Staicu, Cristian Vasile DoicinMobile Robotics Vision-Language Navigation
Faculty of Industrial Engineering and Robotics (FIIR), National University of Science and Technology POLITEHNICA Bucharest
DexSim2Real: Foundation Model-Guided Sim-to-Real Transfer for Generalizable Dexterous Manipulation
May 3, 2026Zijian Zeng, Fei Ding, Huiming Yang +2Domain Randomization Dexterous Manipulation
Tsinghua University · Alibaba Group · Bengbu University +1
Research on Vision-Language Question Answering Models for Industrial Robots
May 2, 2026Ping Li, Bartlomiej BrzozkaVisual Question Answering Robotics
Shandong Management University, Jinan, Shandong Province, 250357, China · Maria Curie-Sklodowska University, Faculty of Mathematics, Physics and Computer Science, 20-031 Lublin, Poland
ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue
May 2, 2026Daoxuan Zhang, Ping Chen, Jianyi Zhou +1Aerial Robotics Robotics Simulation
Harbin Institute of Technology, Shenzhen
VLA-ATTC: Adaptive Test-Time Compute for VLA Models with Relative Action Critic Model
May 2, 2026Wenhao Li, Xiu Su, Yichao Cao +5Vision-Language-Action Models Test-Time Adaptation of VLA Models
University of Sydney · Central South University · University of Science and Technology of China +2
Sentinel-VLA: A Metacognitive VLA Model with Active Status Monitoring for Dynamic Reasoning and Error Recovery
May 2, 2026Wenhao Li, Xiu Su, Dan Niu +6Continual Learning for LLMs Robotics
University of Sydney · Central South University · Southeast University +2
Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
May 1, 2026Yi Wang, Xinchen Li, Pengwei Xie +13Robot Policy Learning Robotic Manipulation
Shanghai Innovation Institute. · AGIBOT Finch. · Columbia University.
Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance
Apr 29, 2026Lingfeng Zhang, Xiaoshuai Hao, Xizhou Bu +11Robot Navigation Safe Robot Navigation
Tsinghua University · Pengcheng Laboratory · Xiaomi EV +5

May 13, 2026, cs.ROJ/K move · Enter open · S save

RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data

Harold Haodong Chen, Sirui Chen, Yingjie Xu, Wenhang Ge+1

1The Hong Kong University of Science and Technology (Guangzhou) · 2The Hong Kong University of Science and Technology

Robot Skill Learning Synthetic Data Generation for Robotics Robotic Manipulation Data-Efficient Robot Learning VLMs for Robotics Robot Learning

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The scalability of robotic manipulation is fundamentally bottlenecked by the scarcity of task-aligned physical interaction data. While vision-language models (VLMs) and video generation models (VGMs) hold promise for autonomous data synthesis, they suffer from semantic-spatial misalignment and physical hallucinations, respectively. To bridge this gap, we introduce RoboEvolve, a novel framework that couples a VLM planner and a VGM simulator into a mutually reinforcing co-evolutionary loop. Operating purely on unlabeled seed images, RoboEvolve leverages a cognitive-inspired dual-phase mechanism: (i) daytime exploration fosters physically grounded behavioral discovery through a semantic-controlled multi-granular reward, and (ii) nighttime consolidation mines "near-miss" failures to stabilize policy optimization. Guided by an autonomous progressive curriculum, the system naturally scales from simple atomic actions to complex tasks. Extensive experiments demonstrate that RoboEvolve (I) achieves superior effectiveness, elevating base planners by 30 absolute points and amplifying simulator success by 48% on average; (II) exhibits extreme data efficiency, surpassing fully supervised baselines with merely 500 unlabeled seeds--a 50x reduction; and (III) demonstrates robust continual learning without catastrophic forgetting.

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