Dataset
First 50 rows of train
| idstring | sourcesList | titlestring | abstractstring | authorsList | categoriesList | fields_of_studyList | published_datetimestamp[s] | urlstring | pdf_urlstring | arxiv_idstring | doistring | citation_countint64 | influential_citation_countint64 | has_codebool | code_urlstring | venuestring | quality_scorefloat64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9b0211793f7847c1be0116f6d1f0569997a89b8d45a635725a19e92e1560da88 | ["arxiv"] | Co-VLA: Coordination-Aware Structured Action Modeling for Dual-Arm Vision-Language-Action Systems | Vision-language-action (VLA) models show strong capabilities in single and dual-arm robotic manipulation. Prior works show coordinated bimanual behaviors can emerge from end-to-end learning, leveraging large vision-language backbones with continuous action prediction. However, as… | ["Yandong Wang","Jiaqian Yu","Xiongfeng Peng","Lu Xu","Yamin Mao","Weiming Li","Jaewook Yoo","Dongwook Lee","Daehyun Ji","Mingbo Zhao","Chao Zhang"] | ["cs.RO"] | [] | 2026-06-18T00:00:00 | https://arxiv.org/abs/2606.20285 | https://arxiv.org/pdf/2606.20285v1 | 2606.20285 | 0 | 0 | false | 0.35 | |||
| 9253fde7e4edc7a2b683dfbcfc800919c1d651eb23b83974acffadc626ece46b | ["arxiv"] |
| Training generalist Vision-Language-Action(VLA) models typically requires massive, diverse robotic datasets with high-fidelity action annotations. While egocentric human manipulation videos are abundant and capture significant environmental diversity, the absence of action labels… |
| ["Runze Xu","Yiluo Zhang","Jian Wang","Yu Wang","Jincheng Yu"] |
| ["cs.CV","cs.RO"] |
| [] |
| 2026-06-17T00:00:00 |
| https://arxiv.org/abs/2606.18955 |
| https://arxiv.org/pdf/2606.18955v1 |
| 2606.18955 |
| 0 |
| 0 |
| false |
| 0.35 |
| a335227c9cd309517192e66ef2288c9ff663effe1d86093b551e70ee83d3ef01 | ["arxiv"] | Does VLA Even Know the Basics? Measuring Commonsense and World Knowledge Retention in Vision-Language-Action Models | Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet it is unclear how much commonsense and factual knowledge they retain after adaptation. Failures on knowledge-sensitive tasks are ambiguous, conflating… | ["Nikita Kachaev","Andrey Moskalenko","Matvey Skripkin","Nikita Kurlaev","Daria Pugacheva","Albina Burlova","Mikhail Kolosov","Denis Shepelev","Andrey Kuznetsov","Elena Tutubalina","Aleksandr I. Panov","Alexey K. Kovalev","Vlad Shakhuro"] | ["cs.LG","cs.RO"] | [] | 2026-06-17T00:00:00 | https://arxiv.org/abs/2606.19297 | https://arxiv.org/pdf/2606.19297v1 | 2606.19297 | 0 | 0 | false | 0.35 |
| 87afd51bb8b2fc27e463036a9a9ca2b183ebc2f852b2382ad4a50f0b57df5694 | ["arxiv"] | MuseVLA: An Adaptive Multimodal Sensing Vision-Language-Action Model for Robotic Manipulation | Humans naturally leverage diverse sensing modalities to interact with the physical world, while most Vision-Language-Action (VLA) models for robotics rely solely on RGB observations. This limits their ability to perceive physical properties that are difficult or impossible to inf… | ["Xingyuming Liu","Ruichun Ma","Heyu Guo","Qixiu Li","Qingwen Yang","Lin Luo","Shiqi Jiang","Chenren Xu","Jiaolong Yang","Baining Guo"] | ["cs.RO","cs.CV"] | [] | 2026-06-16T00:00:00 | https://arxiv.org/abs/2606.17598 | https://arxiv.org/pdf/2606.17598v1 | 2606.17598 | 0 | 0 | false | 0.35 |
| a4c037e5bfb503b16303e6bb60b590745f433b1affe1e1a723c853833971044c | ["arxiv"] | Guava: An Effective and Universal Harness for Embodied Manipulation | Language models trained on large-scale vision-language data have demonstrated strong potential for embodied agents. Harnessing models through embodied tools use offers a promising alternative to end-to-end vision-language-action systems by combining high-level reasoning with exte… | ["Haowen Liu","Xirui Li","Shaoxiong Yao","Peng Shi","Tianyi Zhou","Jia-Bin Huang","Furong Huang","Jiayuan Mao"] | ["cs.RO","cs.AI"] | [] | 2026-06-16T00:00:00 | https://arxiv.org/abs/2606.18363 | https://arxiv.org/pdf/2606.18363v1 | 2606.18363 | 0 | 0 | true | 0.65 |
| e147ebabbd70351d7a179b8040599998357efdf6bf41e1e3396ee074f791b5f7 | ["arxiv","semantic_scholar"] | Geometric Action Model for Robot Policy Learning | Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world. Recent vision-language-action models (VLAs) and video world-action models (WAMs) inherit strong semantic or temporal priors fro… | ["Jisang Han","Seonghu Jeon","Jaewoo Jung","René Zurbrügg","Honggyu An","Tifanny Portela","Marco Hutter","Marc Pollefeys","Seungryong Kim","Sunghwan Hong"] | ["cs.RO","cs.CV","cs.LG"] | ["Computer Science"] | 2026-06-15T00:00:00 | https://arxiv.org/abs/2606.17046 | https://arxiv.org/pdf/2606.17046v1 | 2606.17046 | 0 | 0 | false | 0.35 |
| 91080ba195d50b134f70b8b2f00b4d20ea97e2947976815fab51eea0d9dbd84b | ["arxiv","semantic_scholar"] | RT-VLA: Real-Time Vision-Language-Action Models via Knowledge Distillation | Vision-Language-Action (VLA) models have shown strong potential for end-to-end autonomous driving by jointly modeling visual perception, language reasoning, explainability and action prediction. However, their large vision-language backbones and reasoning modules introduce substa… | ["Xiangyu Huang","Zhenlin Hua","Han Zhou","Shounak Sural","Ragunathan Rajkumar"] | ["cs.CV","cs.LG","cs.RO"] | ["Computer Science"] | 2026-06-12T00:00:00 | https://arxiv.org/abs/2606.14010 | https://arxiv.org/pdf/2606.14010v1 | 2606.14010 | 0 | 0 | false | 0.35 |
| c93a0674864d1f65dc990ff625e7fd27b78af95049fd1d30e50f7c8e34972bb3 | ["arxiv","semantic_scholar"] | ReactVLA: Fast and Lightweight Reactive Robot Manipulation via Improved Mean Flow Action Generation | Diffusion-based Vision-Language-Action (VLA) policies have demonstrated strong capability in modeling expressive and multimodal action distributions. However, their reliance on iterative sampling introduces substantial inference latency, which limits their applicability to reacti… | ["Yanzhao Guo","Wenkai Chen","Jianwei Zhang"] | ["cs.RO"] | ["Computer Science"] | 2026-06-12T00:00:00 | https://arxiv.org/abs/2606.14255 | https://arxiv.org/pdf/2606.14255v1 | 2606.14255 | 0 | 0 | false | 0.35 |
| 205689892b222a4b54f7e326d9626c33a79f64f8b4e6f9d376cf1e682c84189f | ["arxiv","semantic_scholar"] | Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack | In this report, we present Hy-Embodied-0.5-VLA, abbreviated as HyVLA-0.5, an end-to-end system that spans the full robot learning stack: data collection, model design, continued pre-training and supervised fine-tuning, RL post-training, and real-world deployment. Each component s… | ["He Zhang","Lingzhu Xiang","Haitao Lin","Zeyu Huang","Minghui Wang","Dingyan Zhong","Yubo Dong","Yihao Wu","Yongming Rao","Dongsheng Zhang","Wanjia He","Ling Chen","Kai Huang","Jiahao Chen","Sichang Su","Xumin Yu","Ziyi Wang","Chengwei Zhu","Xiao Teng","Yuchun Guo","Yufeng Zhang… | ["cs.RO","cs.AI"] | ["Computer Science"] | 2026-06-12T00:00:00 | https://arxiv.org/abs/2606.14409 | https://arxiv.org/pdf/2606.14409v1 | 2606.14409 | 0 | 0 | false | 0.35 |
| d0d748be3f47d17890a9c89c6705e282aa423daba7b2743abd3953962cbf67d6 | ["arxiv","semantic_scholar"] | Trajectory-Level Redirection Attacks on Vision-Language-Action Models | Vision-language-action (VLA) policies bring natural language into closed-loop robot control, enabling robots to execute manipulation tasks directly from text instructions. The same interface gives text a recurring role in control because the prompt is reused at every replanning s… | ["Gokul Puthumanaillam","Vardhan Dongre","Pranay Thangeda","Hooshang Nayyeri","Dilek Hakkani-Tür","Melkior Ornik"] | ["cs.RO","cs.CV","eess.SY"] | ["Computer Science","Engineering"] | 2026-06-11T00:00:00 | https://arxiv.org/abs/2606.12978 | https://arxiv.org/pdf/2606.12978v2 | 2606.12978 | 0 | 0 | false | 0.35 |
| c30e020e1499e219b927724834211a907b7d381e70159497aeea378091915ead | ["arxiv","semantic_scholar"] | DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model | Vision-language-action (VLA) models inherit a shared synchronous clock from vision-language pretraining, processing every input at one rate. This is misaligned with physical interaction, where a high-frequency modality changes at hundreds of hertz, vision evolves more slowly, and… | ["Pankhuri Vanjani","Zhuoyue Li","Jakub Suliga","Moritz Reuss","Gianluca Geraci","Xinkai Jiang","Rudolf Lioutikov"] | ["cs.RO","cs.CV","cs.LG"] | ["Computer Science"] | 2026-06-10T00:00:00 | https://arxiv.org/abs/2606.12105 | https://arxiv.org/pdf/2606.12105v1 | 2606.12105 | 0 | 0 | false | 0.35 |
| ee9676bd0b18f4979c4fcb29e913fae2afadcba67b2a1f3021c9b06009c7fd73 | ["arxiv","semantic_scholar"] | Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering | Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically similar instructions can induce drastically different behaviors, while some capabilities may not be … | ["Hyun Joe Jeong","Gokul Swamy","Andrea Bajcsy"] | ["cs.RO","cs.LG"] | ["Computer Science"] | 2026-06-10T00:00:00 | https://arxiv.org/abs/2606.12299 | https://arxiv.org/pdf/2606.12299v1 | 2606.12299 | 0 | 0 | false | 0.35 |
| fc5b0aeb0bab4866980b7ef7d175ce22eba98ec99ccca08409bae5a6e5f47b20 | ["arxiv","semantic_scholar"] | VLGA: Vision-Language-Geometry-Action Models for Autonomous Driving | Vision-language-action (VLA) models can describe scenes and reason about them in language, yet still struggle to ground their actions in the dense 3D world around them. Existing approaches either inject features from a frozen 3D foundation model without an objective that ensures … | ["Jin Yao","Dhruva Dixith Kurra","Tom Lampo","Zezhou Cheng","Danhua Guo","Burhan Yaman"] | ["cs.CV","cs.RO"] | ["Computer Science"] | 2026-06-10T00:00:00 | https://arxiv.org/abs/2606.12396 | https://arxiv.org/pdf/2606.12396v1 | 2606.12396 | 0 | 0 | false | 0.35 |
| e5e39c84479f03f677ad27953277fd09f035dde57e1d4936503d52510b0f7527 | ["arxiv","semantic_scholar"] | Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models | We introduce Embodied-R1.5, a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities, spanning embodied cognition, task planning, correction, and pointing, within a single architecture toward general physical intelligence. Leveraging… | ["Yifu Yuan","Yaoting Huang","Xianze Yao","Yutong Li","Shuoheng Zhang","Linqi Han","Pengyi Li","Jiangeng Sun","Wenting Jia","Zhao Zhang","Yuhao Liu","Ruihao Liao","Yucheng Hu","Qiyu Wu","Yuxiao Li","Zibin Dong","Fei Ni","Yan Zheng","Shuyang Gu","Yi Ma","Hongyao Tang","Han Hu","Ji… | ["cs.RO","cs.AI","cs.LG"] | ["Computer Science"] | 2026-06-09T00:00:00 | https://arxiv.org/abs/2606.11324 | https://arxiv.org/pdf/2606.11324v1 | 2606.11324 | 0 | 0 | true | 0.65 |
| 87f5039f49ef4f409d6fcd74db1354e6564e0e2613ad5a574cadea62b5b3b765 | ["arxiv","semantic_scholar"] | GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation | Vision-Language-Action (VLA) models achieve strong benchmark performance but still struggle in real-world deployment with unseen objects, background shifts, and different robot embodiments. We argue that this stems from the lack of a unified geometry-aware manipulation representa… | ["Yuan Zhang","Shiqi Zhang","Yedong Shen","Shuai Dong","Jiajun Deng","Xin Zhang","Yuxuan Gao","Jiajia Wu","Xin Nie","Zhiyuan Cheng","Jianmin Ji","Yanyong Zhang","Xingyi Zhang","Jia Pan"] | ["cs.RO","cs.AI"] | ["Computer Science"] | 2026-06-07T00:00:00 | https://arxiv.org/abs/2606.08530 | https://arxiv.org/pdf/2606.08530v2 | 2606.08530 | 0 | 0 | true | https://github.com/babynabeauty/GEAR-VLA | 0.65 |
| 9b4b20743a0886b67a61e97631eedb26002ff94bb638c37d26c4c840c57c22af | ["arxiv","semantic_scholar"] | X-Tokenizer: A Multimodal Action Tokenizer for Vision-Language-Action Pretraining | Modern Vision-Language-Action (VLA) models must bridge pretrained vision-language reasoning and precise continuous robot control. Existing action tokenizers discretize actions primarily for reconstruction, producing codes that preserve motion geometry but provide only weak semant… | ["Xirui Kang","Yanpei Shi","Lucy Liang","Roy Gan","Dongxiu Liu","Pushi Zhang","Danpeng Chen","Xiaoyi Qin","Yinan Zheng","Jinliang Zheng","Hao Wang","Xianyuan Zhan","Hang Su"] | ["cs.CV","cs.AI","cs.LG","cs.RO"] | ["Computer Science"] | 2026-06-07T00:00:00 | https://arxiv.org/abs/2606.14752 | https://arxiv.org/pdf/2606.14752v1 | 2606.14752 | 0 | 0 | false | 0.35 |
| 73c07504295b6cf7b888ee01a07c9723a06125569c53fb28665a911aa657b9f2 | ["arxiv","semantic_scholar"] | TBD-VLA: Temporal Block Diffusion Vision Language Action Model | Discrete Vision-Language-Action (VLA) models typically formulate action generation as next-token prediction over discretized action spaces, conditioning each token autoregressively on prior context. While effective, this paradigm incurs high inference latency and largely ignores … | ["Sung-Wook Lee","Xuhui Kang","Yen-Ling Kuo"] | ["cs.CV","cs.RO"] | ["Computer Science"] | 2026-06-05T00:00:00 | https://arxiv.org/abs/2606.07895 | https://arxiv.org/pdf/2606.07895v1 | 2606.07895 | 0 | 0 | false | 0.35 |
| 82d6a33d12d000ee788515d0234a0ac3cdc7d3d6a2b565b997c511e28199cab6 | ["arxiv","semantic_scholar"] | A Conversational Framework for Human-Robot Collaborative Manipulation with Distributed Generative AI models | This paper presents a distributed conversational framework for human-robot collaborative manipulation that integrates local language and vision-language models (VLMs) with a Robot Operating System 2 (ROS 2)-based execution stack. Language understanding, visual grounding, orchestr… | ["Arash Ghasemzadeh Kakroudi","Roel Pieters"] | ["cs.RO"] | ["Computer Science"] | 2026-06-04T00:00:00 | https://arxiv.org/abs/2606.06061 | https://arxiv.org/pdf/2606.06061v1 | 2606.06061 | 0 | 0 | true | https://github.com/cogrob-tuni/franka-llm | 0.65 |
| 2b5fe57e79aab8c58f7c140c696cc99f96f19faad29ee5f9723957b8baf29587 | ["arxiv","semantic_scholar"] | AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding | Vision-Language-Action (VLA) models leverage the rich world knowledge of pretrained vision-language models (VLMs) to enable instruction-following robotic manipulation. However, the structural mismatch between VLM semantic spaces and embodied control policies often hinders the lea… | ["Qize Yu","Jiadi You","Yuran Wang","Jiaqi Liang","Bowen Ping","Yang Tian","Yue Chen","Minghong Cai","Zeying Gong","Ruihai Wu","Yinchuan Li","Junwei Liang","Yingcong Chen"] | ["cs.RO","cs.CV","cs.MM"] | ["Computer Science"] | 2026-06-04T00:00:00 | https://arxiv.org/abs/2606.06155 | https://arxiv.org/pdf/2606.06155v1 | 2606.06155 | 0 | 0 | true | https://github.com/Skywalker-yqz/AffordanceVLA | 0.65 |
| c81add21776a811ecd1298b988bebaa6f3155bceb60476735cd3eb214c302df9 | ["arxiv","semantic_scholar"] | Robots Need More than VLA and World Models | Generalist robot intelligence is often framed as a policy-scaling problem: collect more robot demonstrations, train larger Vision-Language-Action (VLA) models, and expect broader generalisation. In this position paper, we argue that this framing is incomplete. The central bottlen… | ["Elis Karcini","Faisal Mehrban","Quang Nguyen","Mac Schwager","Arash Ajoudani","Cesar Cadena","Jan Peters","Marco Hutter","Haitham Bou-Ammar"] | ["cs.RO"] | ["Computer Science"] | 2026-06-04T00:00:00 | https://arxiv.org/abs/2606.06556 | https://arxiv.org/pdf/2606.06556v1 | 2606.06556 | 0 | 0 | false | 0.35 |
| 9db21eefed1740a2d8f152611f878d964ff0b44a67af34ab082798829f8213b9 | ["arxiv","semantic_scholar"] | Revisiting Embodied Chain-of-Thought for Generalizable Robot Manipulation | Embodied chain-of-thought (CoT) aims to bridge linguistic reasoning and robotic control, but its effective form and integration strategy remain underexplored. In this paper, we revisit embodied CoT for vision-language-action (VLA) models at large scale. We construct the largest e… | ["Nan Sun","Yuan Zhang","Yongkun Yang","Wentao Zhao","Peiyan Li","Jun Guo","Wenxuan Song","Pengxiang Ding","Runze Suo","Yifei Su","Xin Xiao","Xinghang Li","Huaping Liu"] | ["cs.RO"] | ["Computer Science"] | 2026-06-02T00:00:00 | https://arxiv.org/abs/2606.03784 | https://arxiv.org/pdf/2606.03784v2 | 2606.03784 | 0 | 0 | false | 0.35 |
| 7b89468a59e418bf0a13143bf5fdd186e9962e97c0d1b6f5cc08bca6f351fe65 | ["arxiv","semantic_scholar"] | TTT-VLA: Test-Time Latent Prompt Optimization for Vision-Language-Action Models | Vision-Language-Action (VLA) models trained on large-scale data have made remarkable progress, but they remain vulnerable to distribution shifts at deployment time. Recent VLA models suggest that prompts can serve as an efficient interface for steering policy behavior, but existi… | ["Wenbo Zhang","Jianxiong Li","Shuai Yang","Sijin Chen","Jiajun Liu","Lingqiao Liu","Xiao Ma"] | ["cs.RO"] | ["Computer Science"] | 2026-06-02T00:00:00 | https://arxiv.org/abs/2606.03127 | https://arxiv.org/pdf/2606.03127v1 | 2606.03127 | 0 | 0 | false | 0.35 |
| 2ceeb14f3948ba857a6596ddd0e5ea3d407415c64657ab6dc02af9e3eaf3fa4d | ["arxiv","semantic_scholar"] | RoboSemanticBench: Diagnosing Semantic Grounding in Action Prediction for VLA Models | Vision-language-action (VLA) models are built on the premise that semantic understanding from pretrained language or vision-language backbones should guide robot action prediction. Yet robot fine-tuning is optimized as imitation over task-specific action distributions, and many e… | ["Bin Yu","Yao Zhang","Haishan Liu","Shijie Lian","Yuliang Wei","Xiaopeng Lin","Zhaolong Shen","Changti Wu","Ruina Hu","Bailing Wang","Cong Huang","Kai Chen"] | ["cs.RO"] | ["Computer Science"] | 2026-06-01T00:00:00 | https://arxiv.org/abs/2606.02277 | https://arxiv.org/pdf/2606.02277v1 | 2606.02277 | 0 | 0 | true | https://github.com/ZGC-EmbodyAI/RoboSemanticBench | 0.65 |
| 96d793e5187d9ec779bbb4873681246cfdb01ff37f1d7158d39fb671b31c1238 | ["arxiv","semantic_scholar"] | LEGS: Fine-Tuning Teleop-Free VLAs for Humanoid Loco-manipulation in an Embodied Gaussian Splatting World | Training vision-language-action (VLA) policies for humanoid loco-manipulation is constrained by the high cost and complexity of collecting human teleoperation demonstrations. VLA policies fine-tuned in simulators have, until now, failed to transfer effectively in humanoid loco-ma… | ["Hojune Kim","Timothy Chen","Jiankai Sun","Lars W. Osterberg","Qianzhong Chen","Ke Wang","Mac Schwager"] | ["cs.RO"] | ["Computer Science"] | 2026-05-31T00:00:00 | https://arxiv.org/abs/2606.01458 | https://arxiv.org/pdf/2606.01458v1 | 2606.01458 | 0 | 0 | false | 0.35 |
| c5e48f0186be7ae4985dd72c2f91269431f0d975f14bc227a67560d782295b80 | ["arxiv","semantic_scholar"] | PaCo-VLA: Passivity-Shielded Compliance Prior for Contact-Rich Vision-Language-Action Manipulation | Contact-rich manipulation demands both high-level semantic reasoning and the safe regulation of high-frequency contact dynamics. While Vision-Language-Action (VLA) models provide unprecedented semantic generalization, their low-rate outputs lack the reliability required for direc… | ["Haofan Cao","Zhaoyang Li","Zhichao You","Liang Guo","Tianrui Li"] | ["cs.RO","cs.AI","eess.SY"] | ["Computer Science","Engineering"] | 2026-05-30T00:00:00 | https://arxiv.org/abs/2606.00515 | https://arxiv.org/pdf/2606.00515v1 | 2606.00515 | 0 | 0 | false | 0.35 |
| 486d0b3b6a3c65eaa5328195588a9df0e1556dc09ca91314506da97fd994e2d4 | ["arxiv","semantic_scholar"] | HARP-VLA: Human-Robot Aligned Representation Learning for Vision-Language-Action Model | Learning generalizable vision-language-action (VLA) models from large-scale human videos is promising but challenging due to cross-embodiment discrepancies in both visual observations and executable actions. While latent action models reduce the action execution gap by learning a… | ["Xiang Zhu","Puzhen Yuan","Yichen Liu","Jianyu Chen"] | ["cs.RO"] | ["Computer Science"] | 2026-05-29T00:00:00 | https://arxiv.org/abs/2605.31234 | https://arxiv.org/pdf/2605.31234v1 | 2605.31234 | 0 | 0 | false | 0.35 |
| fe89f523ea0e1d7fe3860cdfcd14c144d91c8ff665615fffbf34022a8174a60f | ["arxiv","semantic_scholar"] | Per-Group Error, Not Total MSE: Fine-Tuning Vision-Language-Action Models for 11-DoF Mobile Manipulation | Fine-tuning Vision-Language-Action (VLA) models for mobile manipulators with heterogeneous joint spaces can produce a counterintuitive result: the checkpoint with the lowest aggregate MSE is not the one that performs best on the real robot. We argue this is a predictable conseque… | ["Pau Montagut Bofi","Mario García Blasco","Tessa Pulli","Markus Vincze"] | ["cs.RO","cs.LG"] | ["Computer Science"] | 2026-05-29T00:00:00 | https://arxiv.org/abs/2606.00253 | https://arxiv.org/pdf/2606.00253v1 | 2606.00253 | 0 | 0 | true | https://github.com/paumontagut/per-group-mse-vla | 0.65 |
| f6ca4fb169ba2995398eb38700d3920f8757e45ad527ebc4c14b6984b871db36 | ["arxiv","semantic_scholar"] | DeMaVLA: A Vision-Language-Action Foundation Model for Generalizable Deformable Manipulation | Real-world household robots require Vision-Language-Action (VLA) foundation models that can acquire reusable manipulation skills across diverse objects, task conditions, and household environments. Deformable-object folding is a representative challenge, requiring robots to handl… | ["Taiyi Su","Jian Zhu","Tianjian Wang","Youzhang He","Zitai Huang","Jianjun Zhang","Chong Ma","Hanyang Wang","Tianjiao Zhang","Munan Yin","Weihao Ding","Yi Xu"] | ["cs.RO","cs.AI"] | ["Computer Science"] | 2026-05-29T00:00:00 | https://arxiv.org/abs/2605.31286 | https://arxiv.org/pdf/2605.31286v2 | 2605.31286 | 0 | 0 | false | 0.35 |
| a4c48fb3afc5ce9cb78c4bc1a59abf28d2692fe6ef7dd48ee30d9b4b3789cd95 | ["arxiv","semantic_scholar"] | Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments | Embodied intelligence is often studied through specialized models for individual tasks such as manipulation or navigation, resulting in fragmented capabilities and limited generalization across tasks, environments, and robot embodiments. In this work, we study whether heterogeneo… | ["Qiuyue Wang","Mingsheng Li","Jian Guan","Jinhui Ye","Sicheng Xie","Yitao Liu","Junhao Chen","Zhixuan Liang","Jie Zhang","Xintong Hu","Xuhong Huang","Pei Lin","Junyang Lin","Dayiheng Liu","Shuai Bai","Jingren Zhou","Jiazhao Zhang","Haoqi Yuan","Gengze Zhou","Hang Yin","Ye Wang",… | ["cs.RO","cs.AI","cs.CL"] | ["Computer Science"] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.30280 | https://arxiv.org/pdf/2605.30280v2 | 2605.30280 | 1 | 0 | false | 0.35 |
| 9593b1813e277c0083450c076611266261cd73a131c6cac93dba9fcd464b827f | ["arxiv","semantic_scholar"] | VLA-Pro: Cross-Task Procedural Memory Transfer for Vision-Language-Action Models | Vision-Language-Action~(VLA) models have shown strong potential for general-purpose robotic manipulation, yet they still struggle to generalize to unseen tasks that necessitate transferring relevant experience across objects, scenes, and action patterns. This paper proposes VLA-P… | ["Shengyu Si","Yuanzhuo Lu","Ruimeng Yang","Ziyi Ye","Zuxuan Wu","Yu-Gang Jiang"] | ["cs.RO","cs.AI","cs.CV"] | ["Computer Science"] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.29562 | https://arxiv.org/pdf/2605.29562v1 | 2605.29562 | 0 | 0 | false | 0.35 |
| 8a94229e9b0660fade5b8b7e0d24b8f1d08f07e32d604ad5c73c82c0f52e0cfb | ["arxiv","semantic_scholar"] | Gaze2Act: Gaze-Conditioned Vision-Language-Action Policies for Interactive Robot Manipulation | Vision-Language-Action (VLA) models have recently shown strong potential for robot learning by following language instructions. However, in practice, language alone is often insufficient to precisely convey human intent. It is difficult to describe which exact object to interact … | ["Kuangji Zuo","Gen Li","Bofan Lyu","Yanshuo Lu","Boyu Ma","Shijia Han","Xinyu Zhou","Xichen Yuan","Chuhao Zhou","Jiaqi Bai","Geng Li","Jianfei Yang"] | ["cs.RO"] | ["Computer Science"] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.30282 | https://arxiv.org/pdf/2605.30282v1 | 2605.30282 | 1 | 0 | false | 0.35 |
| fa3ef28391997d34346d7668332626443ddb8b3daae3668295160fa9627d3c56 | ["arxiv","semantic_scholar"] | ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation | Vision-Language-Action (VLA) models have shown promise for robotic manipulation, yet most existing policies operate reactively by directly regressing actions from current observations, without explicitly modeling future dynamics. This limits their ability to generalize under out-… | ["Zeyuan He","Bowen Yang","Zhirui Fang","Keru Zhou","Lei Jiang","Jingjing Qian","Fan Mo","Junchi Yan","Philip Torr","Xiu Li","Li Jiang","Jialin Yu"] | ["cs.RO"] | ["Computer Science"] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.30484 | https://arxiv.org/pdf/2605.30484v1 | 2605.30484 | 0 | 0 | false | 0.35 |
| db171f45f8879bc7ad7d16293470f7500d13b2dce5612dfe594c5ac4477420fc | ["arxiv","semantic_scholar"] | VisualThink-VLA: Visual Intermediate Reasoning for Effective and Low-Latency Vision-Language-Action Policies | Recent work has begun to equip vision-language-action (VLA) policies with explicit intermediate reasoning. In embodied control, however, textual chain-of-thought is a poor fit: irrelevant or weakly textual information can interfere with action prediction, while autoregressive tex… | ["Mingjian Gao","Wenqiao Zhang","Yuqian Yuan","Yang Dai","Binhe Yu","Zheqi Lv","Haoyu Zheng","Jiaqi Zhu","Zhiqi Ge","Zixuan Wan","Siliang Tang","Yueting Zhuang"] | ["cs.CV","cs.AI"] | ["Computer Science"] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.30011 | https://arxiv.org/pdf/2605.30011v1 | 2605.30011 | 0 | 0 | false | 0.35 |
| 2f9f26bdb26f7378a942a2b48b0a2ed5a7e14cce42bc44f387edb914dc67eaf8 | ["arxiv","semantic_scholar"] | Mitigating State Aliasing in Vision-Language-Action Models via Inverse Dynamics Learning | Vision-Language-Action (VLA) models have emerged as a promising framework that unifies perception, reasoning, and control for robot manipulation by adapting pretrained vision-language models (VLMs) to action prediction. However, VLM-derived representations are often insensitive t… | ["Kyujin Lee","Injae Kim","Jihwan Park","Yejun Ju","Minseok Joo","Hyunwoo J. Kim"] | ["cs.CV"] | ["Computer Science"] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.29577 | https://arxiv.org/pdf/2605.29577v1 | 2605.29577 | 0 | 0 | false | 0.35 |
| 71c4219a71d02ccc15b0d6f49bc02fd9e33e794f0fd4c406baa66b30a48103a7 | ["arxiv","semantic_scholar"] | VLA-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing | Understanding how Vision-Language-Action (VLA) models transform multimodal knowledge into embodied control remains an open challenge. We present VLA-Trace, a progressive diagnostic framework that analyzes VLA models through a unified evidence chain from representation dynamics to… | ["Haoyuan Shi","Xiancong Ren","Yingji Zhang","Qinfan Zhang","Jiayu Hu","Haozhe Shan","Han Dong","Jinpeng Lu","Yinda Chen","Yi Zhang","Yong Dai","Xiaozhu Ju"] | ["cs.AI"] | ["Computer Science"] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.30117 | https://arxiv.org/pdf/2605.30117v1 | 2605.30117 | 0 | 0 | false | 0.35 |
| bceda92364871573270f0ea1c5bc8e15e7cf0c841cefbcaf260d41500f4f1fd9 | ["arxiv","semantic_scholar"] | ElegantVLA: Learning When to Think for Efficient Vision-Language-Action Models | Vision-Language-Action (VLA) models are a powerful paradigm for generalist robotic control. However, their high computational cost and limited control frequency hinder real-time robotic manipulation, especially when large vision-language backbones and iterative action heads run a… | ["Ye Li","Huanan Liu","Kangye Ji","Yuan Meng","Jiajun Fan","Yuansong Wang","Shiyu Qin","Chenglei Wu","Shu-Tao Xia","Zhi Wang"] | ["cs.RO"] | ["Computer Science"] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.29438 | https://arxiv.org/pdf/2605.29438v1 | 2605.29438 | 0 | 0 | false | 0.35 |
| 8b8de60c6a175f5d341af98920d6937dc9f00b42159e517e8cabbc18d75b227a | ["arxiv","semantic_scholar"] | Mag-VLA: Vision-Language-Action Model for Bimanual Magnetically Actuated Microrobot Manipulation | Magnetically actuated microrobots have been used as wireless, non-contact manipulation tools at microscales, making them promising for minimally invasive applications. However, their control remains challenging due to indirect actuation, limited sensing, and nonlinear magnetic in… | ["Yongchen Wang","Kangyi Lu","Lan Wei","Dandan Zhang"] | ["cs.RO"] | ["Computer Science"] | 2026-05-27T00:00:00 | https://arxiv.org/abs/2605.28486 | https://arxiv.org/pdf/2605.28486v1 | 2605.28486 | 0 | 0 | false | 0.35 |
| 5456b00c533d88e12455d6af84df3a718f46c76bbab8716e5092a0301afc3340 | ["arxiv","semantic_scholar"] | VLA-Hijack: A Transferable Patch Attack against Vision-Language-Action Models via Visual Proprioception Hijacking | While Vision-Language-Action (VLA) models have emerged as powerful generalist policies, their severe vulnerability to adversarial patches significantly hinders their deployment in safety-critical domains. Moreover, existing patch attacks primarily focus on white-box settings, hea… | ["Jiyuan Fu","Kaixun Jiang","Jingkai Jia","Zhaoyu Chen","Xueyao Chen","Lingyi Hong","Shuyong Gao","Chenzhi Tan","Dingkang Yang","Wenqiang Zhang"] | ["cs.CV"] | ["Computer Science"] | 2026-05-27T00:00:00 | https://arxiv.org/abs/2605.28083 | https://arxiv.org/pdf/2605.28083v1 | 2605.28083 | 0 | 0 | false | 0.35 |
| 806ccc583b4e3789c3d85f488c6fa0f4bd48894ee192f57f54e3320b59a9e03f | ["arxiv","semantic_scholar"] | How VLAs Fail Differently: Black-Box Action Monitoring Reveals Architecture-Specific Failure Signatures | We discover that VLA architectures fail in fundamentally different, predictable ways at the motor-command level. Running VQ-BeT, Diffusion Policy, and ACT on identical evaluation protocols (n=450 episodes across PushT and ALOHA 14-DOF bimanual manipulation), we find: (1) directio… | ["Krishnam Gupta"] | ["cs.RO","cs.LG"] | ["Computer Science"] | 2026-05-27T00:00:00 | https://arxiv.org/abs/2605.28726 | https://arxiv.org/pdf/2605.28726v1 | 2605.28726 | 0 | 0 | true | https://github.com/krishnam94/vla-edge | 0.65 |
| 092f2ecdec63ef7258b94753a3f342acac1534e6d137bcfdcc6a3ab106765c77 | ["arxiv","semantic_scholar"] | Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation | Embodied navigation requires an agent to map language and visual observations to a stream of spatial actions that drive a real robot through environments it has never seen. The dominant approach has been to scale vision-language-action (VLA) foundation models on ever-larger colle… | ["Hongyu Ding","Sizhuo Zhang","Ziming Xu","Jinwen Guo","Hongxiu Liu","Xingzhi Cheng","Zixuan Chen","Haifei Qi","Duo Wang","Hao Xu","Jieqi Shi","Yifan Zhang","Jing Huo","Jian Cheng","Yang Gao","Jiebo Luo"] | ["cs.RO","cs.CV"] | ["Computer Science"] | 2026-05-26T00:00:00 | https://arxiv.org/abs/2605.27582 | https://arxiv.org/pdf/2605.27582v1 | 2605.27582 | 0 | 0 | false | 0.35 |
| d948758f35a0785200fc81cb40fcccde63e14744b5f1a0d3e03f5b5db1218d84 | ["arxiv","semantic_scholar"] | X-DiffVLA: X-Embodied Diffusion Action Heads for Vision-Language-Action Models | Learning universal policies from cross-embodied data remains a fundamental challenge in robotics. Although Vision-Language-Action (VLA) models are pre-trained on large and diverse datasets, they typically rely on embodiment-specific fine-tuning to achieve strong performance in do… | ["Boyu Li","Chaoyi Xu","Haoqi Yuan","Xinrun Xu","Börje F. Karlsson","Dongbin Zhao","Haoran Li","Zongqing Lu"] | ["cs.RO"] | ["Computer Science"] | 2026-05-24T00:00:00 | https://arxiv.org/abs/2605.25044 | https://arxiv.org/pdf/2605.25044v1 | 2605.25044 | 0 | 0 | false | 0.35 |
| b91e08e894b753654272101d0aed2e4d48ae0e690d88f4bbce1198c998328d74 | ["arxiv","semantic_scholar"] | QuoVLA: Quotient Space for Vision-Language-Action Models | Vision-Language-Action (VLA) models commonly adapt pretrained Vision-Language Models (VLMs) to robot control by mapping visual observations and language instructions to continuous actions. Existing approaches typically take an action-insufficiency view, assuming that pretrained V… | ["Xuan Wang","Yinan Wu","Haoran Duan","Jungong Han"] | ["cs.CV"] | ["Computer Science"] | 2026-05-24T00:00:00 | https://arxiv.org/abs/2605.24890 | https://arxiv.org/pdf/2605.24890v2 | 2605.24890 | 0 | 0 | false | 0.35 |
| 8a64d364a1280ed84c87045259318a9230e4e37611e323b2b6e321e508b0fcbf | ["arxiv","semantic_scholar"] | Agentic-VLA: Efficient Online Adaptation for Vision-Language-Action Models | Vision-Language-Action (VLA) models have emerged as a promising paradigm for robotic manipulation by leveraging pre-trained vision-language representations. However, current VLA training methods suffer from two critical limitations: poor generalization to novel environments and l… | ["Ruofan Jin","Zaixi Zhang"] | ["cs.RO","cs.AI","cs.LG"] | ["Computer Science"] | 2026-05-21T00:00:00 | https://arxiv.org/abs/2605.22896 | https://arxiv.org/pdf/2605.22896v1 | 2605.22896 | 0 | 0 | false | 0.35 |
| f6bc786d5ed536f155d476ff00689e11a1fb8d7249ea6af3c5015fd7c9e6758a | ["arxiv","semantic_scholar"] | Seeing without Looking: Do Vision-Language Benchmarks Really Test Vision? | Benchmark accuracy is often implicitly assumed to reflect grounded visual understanding in vision-language models (VLMs), yet it remains unclear to what extent such scores truly reflect reliance on visual evidence. Motivated by a surprising observation that removing a substantial… | ["Zixuan Lan","Luzhe Sun","Matthew R. Walter","Jiawei Zhou"] | ["cs.CV","cs.AI","cs.CL"] | ["Computer Science"] | 2026-05-21T00:00:00 | https://arxiv.org/abs/2605.22903 | https://arxiv.org/pdf/2605.22903v1 | 2605.22903 | 0 | 0 | true | 0.65 |
| d606f602cbcb190f769a86be0247eada68d7c6e592b54e29b12acaae800fd0be | ["arxiv","semantic_scholar"] | Pre-VLA: Preemptive Runtime Verification for Reliable Vision-Language-Action and World-Model Rollouts | While large vision-language-action (VLA) models and generative world models (WM) have advanced long-horizon embodied intelligence, their practical deployment remains challenged by uncertainty in learning-based action generation. Low-quality actions may cause physical failures dur… | ["Zhen Sun","Yongjian Guo","Haoran Sun","Luqiao Wang","Wei Lu","Jiachi Ji","Shengzhe Ji","Junwu Xiong","Zhijun Meng"] | ["cs.CV","cs.AI","cs.RO"] | ["Computer Science"] | 2026-05-21T00:00:00 | https://arxiv.org/abs/2605.22446 | https://arxiv.org/pdf/2605.22446v1 | 2605.22446 | 1 | 1 | false | 0.35 |
| ede3ec974ee3cfc540381ae0df4cc20dda5f5744c302ecc0487e7f651fc24f71 | ["arxiv","semantic_scholar"] | CrossVLA: Cross-Paradigm Post-Training and Inference Optimization for Vision-Language-Action Models | Vision-Language-Action (VLA) models have rapidly converged on a small set of architectural patterns: discrete-token autoregression (e.g. OpenVLA) and continuous-action flow-matching (e.g. pi-0.5). Yet preference alignment via Direct Preference Optimisation (DPO) -- the de-facto p… | ["Zhi Liu"] | ["cs.CV","cs.AI"] | ["Computer Science"] | 2026-05-21T00:00:00 | https://arxiv.org/abs/2605.21854 | https://arxiv.org/pdf/2605.21854v2 | 2605.21854 | 0 | 0 | true | https://github.com/lz-googlefycy/vla-lab | 0.65 |
| 793c983075bd4157ae5391b2925e8a4e9fc1840e3f5c26000c43a9da7b7fcc16 | ["arxiv","semantic_scholar"] | GesVLA: Gesture-Aware Vision-Language-Action Model Embedded Representations | Vision-Language-Action (VLA) models have shown strong potential for general-purpose robot manipulation by unifying perception and action. However, existing VLA systems primarily rely on textual instructions and struggle to resolve spatial ambiguity in complex scenes with multiple… | ["Wenxuan Guo","Ziyuan Li","Meng Zhang","Yichen Liu","Yimeng Dong","Chuxi Xu","Yunfei Wei","Ze Chen","Erjin Zhou","Jianjiang Feng"] | ["cs.RO","cs.CV"] | ["Computer Science"] | 2026-05-21T00:00:00 | https://arxiv.org/abs/2605.22812 | https://arxiv.org/pdf/2605.22812v1 | 2605.22812 | 0 | 0 | false | 0.35 |
| 77b40ba03760cc656bbe2365527439f1e87fb215cd8ef0fae0ebb9efd54f0633 | ["arxiv","semantic_scholar"] | Humanoid Whole-Body Manipulation via Active Spatial Brain and Generalizable Action Cerebellum | In this paper, we explore spatial-aware humanoid whole-body manipulation task. Compared with tabletop settings, this task poses two key challenges: 1) Spatial understanding is challenging in complex 3D environments with diverse spatial relations. 2) Action generation is difficult… | ["Zhizhao Liang","Yi-Lin Wei","Xuhang Chen","Mu Lin","Yi-Xiang He","Zhexi Luo","Jun-Hui Liu","Kun-Yu Lin","Wei-Shi Zheng"] | ["cs.RO"] | ["Computer Science"] | 2026-05-20T00:00:00 | https://arxiv.org/abs/2605.21133 | https://arxiv.org/pdf/2605.21133v1 | 2605.21133 | 1 | 0 | false | 0.35 |
| aa16cce7a42acd32e4e98e3b74d34b90ad8837463246f4b331add87025a65713 | ["arxiv","semantic_scholar"] | PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction | Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation by leveraging large pretrained vision-language backbones. However, most existing VLAs rely primarily on 2D visual representations, which limit their ability to reason about fi… | ["Shizhe Chen","Paul Pacaud","Cordelia Schmid"] | ["cs.RO","cs.CV"] | ["Computer Science"] | 2026-05-20T00:00:00 | https://arxiv.org/abs/2605.21414 | https://arxiv.org/pdf/2605.21414v1 | 2605.21414 | 0 | 0 | false | 0.35 |
| 4b42c56a3a146a6f203e8e5d4b299a5df5326b5dc78136333fa1111bb30abc1f | ["arxiv","semantic_scholar"] | VLA-REPLICA: A Low-Cost, Reproducible Benchmark for Real-World Evaluation of Vision-Language-Action Models | Vision-Language-Action (VLA) models have shown strong promise for general-purpose robotic manipulation, but their real-world evaluation remains limited by a lack of accessible, reproducible, and consistent benchmarks. Simulation benchmarks fail to capture real-world complexity, w… | ["Alex S. Huang","Jiahui Zhang","Shiqing Tang","Yu Xiang"] | ["cs.RO"] | ["Computer Science"] | 2026-05-20T00:00:00 | https://arxiv.org/abs/2605.20774 | https://arxiv.org/pdf/2605.20774v1 | 2605.20774 | 0 | 0 | false | 0.35 |
