Datasets:

fineset-io
/

vision-language-action-papers

id string	sources list	title string	abstract string	authors list	categories list	fields_of_study list	published_date timestamp[s]	url string	pdf_url string	arxiv_id string	doi string	citation_count int64	influential_citation_count int64	has_code bool	code_url string	venue string	quality_score float64
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 bimanual tasks become tightly coupled a...	[ "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	null	0	0	false	null	null	0.35
9253fde7e4edc7a2b683dfbcfc800919c1d651eb23b83974acffadc626ece46b	[ "arxiv" ]	Motion-Focused Latent Action Enables Cross-Embodiment VLA Training from Human EgoVideos	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 makes them difficult to use in conventi...	[ "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	null	0	0	false	null	null	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 missing knowledge with poor generalizat...	[ "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	null	0	0	false	null	null	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 infer from RGB cameras, such as temperature...	[ "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	null	0	0	false	null	null	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 external modules for perception, planning, a...	[ "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	null	0	0	true	null	null	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 from large-scale foundation models, but the...	[ "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	null	0	0	false	null	null	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 substantial inference latency and thereby prev...	[ "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	null	0	0	false	null	null	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 reactive closed-loop robot manipulation. To ad...	[ "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	null	0	0	false	null	null	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 serves a distinct role in this stack.	[ "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", "Yuchu...	[ "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	null	0	0	false	null	null	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 step, and each prompt-conditioned action ...	[ "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	null	0	0	false	null	null	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 language stays constant across an episo...	[ "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	null	0	0	false	null	null	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 elicitable through prompting alone. As a...	[ "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	null	0	0	false	null	null	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 the policy uses them, or constrain geome...	[ "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	null	0	0	false	null	null	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 three automated data construction pipel...	[ "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", "Hon...	[ "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	null	0	0	true	null	null	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 representation, leaving existing VLAs vulnerable t...	[ "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	null	0	0	true	https://github.com/babynabeauty/GEAR-VLA	null	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 semantic supervision to the backbone. We there...	[ "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	null	0	0	false	null	null	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 the temporal structure inherent in actio...	[ "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	null	0	0	false	null	null	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, orchestration, and motion execution run as separ...	[ "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	null	0	0	true	https://github.com/cogrob-tuni/franka-llm	null	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 learning of precise perception--action mapp...	[ "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	null	0	0	true	https://github.com/Skywalker-yqz/AffordanceVLA	null	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 bottleneck is not only policy learning, but the...	[ "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	null	0	0	false	null	null	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 embodied CoT corpus to date, comprising 9...	[ "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	null	0	0	false	null	null	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 existing prompt-based steering typically relie...	[ "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	null	0	0	false	null	null	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 evaluations can be solved through visual ...	[ "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	null	0	0	true	https://github.com/ZGC-EmbodyAI/RoboSemanticBench	null	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-manipulation tasks. We present LEGS (Loco-...	[ "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	null	0	0	false	null	null	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 direct plant authority in force-sensitive tas...	[ "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	null	0	0	false	null	null	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 action abstractions, they still rely on v...	[ "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	null	0	0	false	null	null	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 consequence of collapsing heterogeneous joint gr...	[ "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	null	0	0	true	https://github.com/paumontagut/per-group-mse-vla	null	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 handle clothing items from random initial sta...	[ "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	null	0	0	false	null	null	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 heterogeneous embodied decision-making problems can...	[ "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", "...	[ "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	null	1	0	false	null	null	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-Pro, a plug-and-play framework designed t...	[ "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	null	0	0	false	null	null	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 with among similar candidates, where to ...	[ "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	null	1	0	false	null	null	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-of-distribution perturbations. To addres...	[ "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	null	0	0	false	null	null	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 text decoding adds too much latency for rea...	[ "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	null	0	0	false	null	null	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 to subtle visual distinctions required fo...	[ "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	null	0	0	false	null	null	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 causal control attribution and behavior...	[ "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	null	0	0	false	null	null	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 at every control step. Existing VLA accel...	[ "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	null	0	0	false	null	null	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 interactions. In this work, we propose Mag...	[ "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	null	0	0	false	null	null	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, heavily overfitting to the specific action ...	[ "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	null	0	0	false	null	null	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) direction reversal rate is a universal failure p...	[ "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	null	0	0	true	https://github.com/krishnam94/vla-edge	null	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 collections of robot trajectories. This paper...	[ "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	null	0	0	false	null	null	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 downstream tasks. This requirement severel...	[ "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	null	0	0	false	null	null	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 VLM latents either lack directly usable a...	[ "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	null	0	0	false	null	null	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 low training efficiency requiring extensi...	[ "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	null	0	0	false	null	null	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 fraction of image tokens only degrades ...	[ "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	null	0	0	true	null	null	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 during execution or lead to misleading worl...	[ "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	null	1	1	false	null	null	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 post-training step in language models -- ...	[ "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	null	0	0	true	https://github.com/lz-googlefycy/vla-lab	null	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 similar objects. To address this limita...	[ "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	null	0	0	false	null	null	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 to generalize, as limited and costly re...	[ "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	null	1	0	false	null	null	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 fine-grained geometry and spatial groundin...	[ "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	null	0	0	false	null	null	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, while existing real-world benchmarks ofte...	[ "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	null	0	0	false	null	null	0.35
2d9ae35b0561209cbb693daaff9e23b48160e3d2adf8b2dc4d05cc9cb6bf4984	[ "arxiv", "semantic_scholar" ]	GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation	Video world models can generate realistic futures from a single instruction, but they often fail to track the same physical points consistently across time. As a result, the generated videos appear plausible, yet lack the physical grounding required for reliable action execution, such as robot manipulation. We present ...	[ "Kaichen Zhou", "Yuzhen Chen", "Fangneng Zhan", "Hang Hua", "Grace Chen", "Xinhai Chang", "Ao Qu", "Yilun Du", "Zhuang Liu", "Paul Pu Liang", "Mengyu Wang" ]	[ "cs.CV", "cs.RO" ]	[ "Computer Science" ]	2026-05-20T00:00:00	https://arxiv.org/abs/2605.22882	https://arxiv.org/pdf/2605.22882v3	2605.22882	null	1	0	false	null	null	0.35
50e9d4fd55ad7d1b0ab3f36d0022ce68e8ccc4348f953f1ad6aa0f72ec9fd825	[ "arxiv", "semantic_scholar" ]	PAPO-VLA: Planning-Aware Policy Optimization for Vision-Language-Action Models	Vision-Language-Action (VLA) models show promising ability in language-guided robotic tasks. However, making VLA policies reliable remains challenging, because a manipulation task is completed through closed-loop interaction, where each action affects subsequent execution. To analyze this problem, we revisit VLA policy...	[ "Peizheng Guo", "Jingyao Wang", "Changwen Zheng", "Wenwen Qiang" ]	[ "cs.RO" ]	[ "Computer Science" ]	2026-05-19T00:00:00	https://arxiv.org/abs/2605.19580	https://arxiv.org/pdf/2605.19580v1	2605.19580	null	0	0	false	null	null	0.35
0783c396dd367b00db25eccdc84cdaa345e6da2fb7a26a0c1cc532de5344f32d	[ "arxiv", "semantic_scholar" ]	From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data	Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most existing approaches rely on large collections of robot demonstrations, which are costly to obtain and tightly coupled to specific embodiments. Human videos, by contrast, are...	[ "Zhiyuan Feng", "Qixiu Li", "Huizhi Liang", "Rushuai Yang", "Yichao Shen", "Zhiying Du", "Zhaowei Zhang", "Yu Deng", "Li Zhao", "Hao Zhao", "Zongqing Lu", "Oier Mees", "Marc Pollefeys", "Jiaolong Yang", "Baining Guo" ]	[ "cs.RO", "cs.AI", "cs.CV" ]	[ "Computer Science" ]	2026-05-18T00:00:00	https://arxiv.org/abs/2606.00054	https://arxiv.org/pdf/2606.00054v1	2606.00054	null	3	0	true	https://github.com/AaronFengZY/HumanCentricToVLA-Survey	null	0.65
c1e938b7d75610377a119557346bca8071f9cd63fde0b5b8964ca30d8985fa9c	[ "arxiv", "semantic_scholar" ]	ManiSoft: Towards Vision-Language Manipulation for Soft Continuum Robotics	Most existing vision-language manipulation research targets rigid robotic arms, whose fixed morphology limits adaptability in cluttered or confined spaces. Soft robotic arms offer an appealing alternative due to their deformability, but confront challenges such as unreliable proprioception and distributed low-level act...	[ "Ziyu Wei", "Luting Wang", "Chen Gao", "Li Wen", "Si Liu" ]	[ "cs.RO", "cs.AI", "cs.CV" ]	[ "Computer Science" ]	2026-05-18T00:00:00	https://arxiv.org/abs/2605.18617	https://arxiv.org/pdf/2605.18617v1	2605.18617	null	0	0	false	null	null	0.35
ea5316ebf9cb565dcfa6b20164de9c0ca8ea1b0c4cbdd3c4230c3f4f4b48ff02	[ "arxiv", "semantic_scholar" ]	Health-Conditioned Vision-Language-Action Models for Malfunction-Aware Robot Control	Research on Vision Language Action (VLA) models has been increasing rapidly in recent years. Although some of them focus on detecting, preventing, and recovering from task failures, they usually don't deal with adapting to robot's physical failures. In real-life scenarios, most robots face physical degradations in vari...	[ "Hüseyin Arslan", "Özgür Erkent" ]	[ "cs.RO" ]	[ "Computer Science" ]	2026-05-15T00:00:00	https://arxiv.org/abs/2605.16056	https://arxiv.org/pdf/2605.16056v1	2605.16056	null	0	0	true	https://github.com/h-arslan/health-aware-vla	null	0.65
62d9c395984938e1ea3c909ea83f41b9759fc23c6516faf47229d834947c92cc	[ "arxiv", "semantic_scholar" ]	Guide, Think, Act: Interactive Embodied Reasoning in Vision-Language-Action Models	In this paper, we propose GTA-VLA(Guide, Think, Act), an interactive Vision-Language-Action (VLA) framework that enables spatially steerable embodied reasoning by allowing users to guide robot policies with explicit visual cues. Existing VLA models learn a direct "Sense-to-Act" mapping from multimodal observations to r...	[ "Yiran Ling", "Qing Lian", "Jinghang Li", "Qing Jiang", "Tianming Zhang", "Xiaoke Jiang", "Chuanxiu Liu", "Jie Liu", "Lei Zhang" ]	[ "cs.RO", "cs.CV" ]	[ "Computer Science" ]	2026-05-13T00:00:00	https://arxiv.org/abs/2605.13632	https://arxiv.org/pdf/2605.13632v1	2605.13632	null	0	0	false	null	null	0.35
0c899c31679d8c5bb0416e8a3ebde6dec16d62c88d7b61165f307d904561351d	[ "arxiv", "semantic_scholar" ]	RotVLA: Rotational Latent Action for Vision-Language-Action Model	Latent Action Models (LAMs) have emerged as an effective paradigm for handling heterogeneous datasets during Vision-Language-Action (VLA) model pretraining, offering a unified action space across embodiments. However, existing LAMs often rely on discrete quantization encode and decode pipelines, which can lead to trivi...	[ "Qiwei Li", "Xicheng Gong", "Xinghang Li", "Peiyan Li", "Quanyun Zhou", "Hangjun Ye", "Jiahuan Zhou", "Yadong Mu" ]	[ "cs.RO", "cs.CV" ]	[ "Computer Science" ]	2026-05-13T00:00:00	https://arxiv.org/abs/2605.13403	https://arxiv.org/pdf/2605.13403v1	2605.13403	null	1	0	false	null	null	0.35
fb012bf0475736a892231647a873b87a90e7f7798121e10a34fff93e93ba58d2	[ "arxiv", "semantic_scholar" ]	Towards Long-horizon Embodied Agents with Tool-Aligned Vision-Language-Action Models	Vision-language-action (VLA) models are effective robot action executors, but they remain limited on long-horizon tasks due to the dual burden of extended closed-loop planning and diverse physical operations. We therefore propose VLAs-as-Tools, a strategy that distributes this burden across a high-level vision language...	[ "Zixing Lei", "Changxing Liu", "Yichen Xiong", "Minhao Xiong", "Yuanzhuo Ding", "Zhipeng Zhang", "Weixin Li", "Siheng Chen" ]	[ "cs.RO", "cs.AI", "cs.CV" ]	[ "Computer Science" ]	2026-05-13T00:00:00	https://arxiv.org/abs/2605.13119	https://arxiv.org/pdf/2605.13119v1	2605.13119	null	1	0	false	null	null	0.35
10962b730ca9340dca6b11621e3f56247b964748b15e08c74365d4c174718f10	[ "arxiv", "semantic_scholar" ]	World Action Models: The Next Frontier in Embodied AI	Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning, yet they learn reactive observation-to-action mappings without explicitly modeling how the physical world evolves under intervention. A growing body of work addresses this limitation by integrating world model...	[ "Siyin Wang", "Junhao Shi", "Zhaoyang Fu", "Xinzhe He", "Feihong Liu", "Chenchen Yang", "Yikang Zhou", "Zhaoye Fei", "Jingjing Gong", "Jinlan Fu", "Mike Zheng Shou", "Xuanjing Huang", "Xipeng Qiu", "Yu-Gang Jiang" ]	[ "cs.RO", "cs.CL", "cs.CV" ]	[ "Computer Science" ]	2026-05-12T00:00:00	https://arxiv.org/abs/2605.12090	https://arxiv.org/pdf/2605.12090v1	2605.12090	null	11	0	false	null	null	0.35
6b387163d4288a1c0d9ff101a4ddee9b81f0eb152658577fa7e040e94417f2c2	[ "arxiv", "semantic_scholar" ]	ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models	Vision-language-action (VLA) models remain constrained by the scarcity of action-labeled robot data, whereas action-free videos provide abundant evidence of how the physical world changes. Latent action models offer a promising way to extract such priors from videos, but reconstruction-trained latent codes are not nece...	[ "Zuojin Tang", "Haoyun Liu", "Xinyuan Chang", "Changjie Wu", "Dongjie Huo", "Yandan Yang", "Bin Liu", "Zhejia Cai", "Feng Xiong", "Mu Xu", "jiachen Luo", "De Ma", "Zhiheng Ma", "Gang Pan" ]	[ "cs.RO", "cs.AI", "cs.CV" ]	[ "Computer Science" ]	2026-05-11T00:00:00	https://arxiv.org/abs/2605.10819	https://arxiv.org/pdf/2605.10819v2	2605.10819	null	0	0	false	null	null	0.35
fcb443900ca67aa56e8a42ea7c878c085a09b8c2f4b5289e4a3aadd65aab16ec	[ "arxiv", "semantic_scholar" ]	BioProVLA-Agent: An Affordable, Protocol-Driven, Vision-Enhanced VLA-Enabled Embodied Multi-Agent System with Closed-Loop-Capable Reasoning for Biological Laboratory Manipulation	Biological laboratory automation can reduce repetitive manual work and improve reproducibility, but reliable embodied execution in wet-lab environments remains challenging. Protocols are often unstructured, labware is frequently transparent or reflective, and multi-step procedures require state-aware execution beyond o...	[ "Zhaohui Du", "Zhe Wang", "Hongmei Fei", "Xiwen Cao", "Ting Xiao", "Qi Wang", "Huanbo Jin", "Jiaming Gu", "Quan Lu", "Zhe Liu" ]	[ "cs.RO", "cs.AI" ]	[ "Computer Science" ]	2026-05-08T00:00:00	https://arxiv.org/abs/2605.07306	https://arxiv.org/pdf/2605.07306v1	2605.07306	null	1	0	false	null	null	0.35
d9992e518aa6d04bfe5de2de1f7257e94dfb1882c3d26030477ab92e6ed3814f	[ "arxiv", "semantic_scholar" ]	AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models	Vision-Language-Action (VLA) models have significantly advanced the capabilities of robotic agents in executing diverse tasks; however, they still face challenges in contact-rich manipulation scenarios that require precise physical interactions. To address this limitation, recent studies have attempted to incorporate t...	[ "Xiaoqi Li", "Muhe Cai", "Jiadong Xu", "Juan Zhu", "Hongwei Fan", "Yan Shen", "Guangrui Ren", "Hao Dong" ]	[ "cs.RO" ]	[ "Computer Science" ]	2026-05-08T00:00:00	https://arxiv.org/abs/2605.07308	https://arxiv.org/pdf/2605.07308v2	2605.07308	null	1	0	false	null	null	0.35
9c8d0d22761160ac99f162bce52ca8a7e2bd9f597a7f5e1e0dc6005a8af5708b	[ "arxiv", "semantic_scholar" ]	ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations	Vision-Language-Action (VLA) models hold great promise for general-purpose robotic intelligence, yet scaling up such models is severely bottlenecked by the high cost of acquiring annotated training data. Fortunately, vision-equipped robots deployed across various domains already produce abundant vision-action pairs tha...	[ "Yuhao Zhou", "Yunpeng Zhu", "Yang Zhou", "Jindi Lyu", "Jian Lan", "Zhangyuan Wang", "Dan Si", "Thomas Seidl", "Qing Ye", "Jiancheng Lyu" ]	[ "cs.CV", "cs.AI" ]	[ "Computer Science" ]	2026-05-08T00:00:00	https://arxiv.org/abs/2605.07474	https://arxiv.org/pdf/2605.07474v1	2605.07474	null	0	0	false	null	null	0.35
a0d2d4449d8b53cc92d2180f04f443293e59ab1c1eb700c54c30f4e8bb1a74b3	[ "arxiv", "semantic_scholar" ]	Anticipation-VLA: Solving Long-Horizon Embodied Tasks via Anticipation-based Subgoal Generation	Vision-Language-Action (VLA) models have emerged as a powerful paradigm for embodied intelligence, enabling robots to perform tasks based on natural language instructions and current visual input. However, existing VLA models struggle with long-horizon tasks due to compounding errors. Prior methods decompose tasks into...	[ "Zhilong Zhang", "Wenyu Luo", "Haonan Wang", "Yifei Sheng", "Yidi Wang", "Hanyuan Guo", "Haoxiang Ren", "Xinghao Du", "Yuhan Che", "Tongtong Cao", "Lei Yuan", "Yang Yu" ]	[ "cs.RO", "cs.LG" ]	[ "Computer Science" ]	2026-05-03T00:00:00	https://arxiv.org/abs/2605.01772	https://arxiv.org/pdf/2605.01772v1	2605.01772	null	0	0	false	null	null	0.35
0c0e79b9429f7b448fda5070671df039f191559a0571accfb53dfd195a216971	[ "arxiv", "semantic_scholar" ]	VLA-ATTC: Adaptive Test-Time Compute for VLA Models with Relative Action Critic Model	Vision-Language-Action (VLA) models have demonstrated remarkable capabilities and generalization in embodied manipulation. However, their decision-making relies on a fast, instinctive process that lacks deliberation. This strategy often leads to suboptimal or catastrophic actions when facing complex or ambiguous scenar...	[ "Wenhao Li", "Xiu Su", "Yichao Cao", "Hongyan Xu", "Xiaobo Xia", "Shan You", "Yi Chen", "Chang Xu" ]	[ "cs.RO" ]	[ "Computer Science" ]	2026-05-02T00:00:00	https://arxiv.org/abs/2605.01194	https://arxiv.org/pdf/2605.01194v2	2605.01194	null	5	0	true	null	null	0.65
27f1850f9b72f1250deffe9cbeb9938759ee98106a9073e8018614b02b043348	[ "arxiv", "semantic_scholar" ]	Embodied Interpretability: Linking Causal Understanding to Generalization in Vision-Language-Action Models	Vision-Language-Action (VLA) policies often fail under distribution shift, suggesting that decisions may depend on spurious visual correlations rather than task-relevant causes. We formulate visual-action attribution as an interventional estimation problem. Accordingly, we introduce the Interventional Significance Scor...	[ "Hanxin Zhang", "Mingshuo Xu", "Abdulqader Dhafer", "Shigang Yue", "Hongbiao Dong", "Zhou Daniel Hao" ]	[ "cs.RO" ]	[ "Computer Science" ]	2026-05-01T00:00:00	https://arxiv.org/abs/2605.00321	https://arxiv.org/pdf/2605.00321v2	2605.00321	null	0	0	false	null	null	0.35
994f061aff714f149a438d918c2ba1afae955ce5a546b7ce5e614dc449f4da48	[ "arxiv", "semantic_scholar" ]	CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies	Flow-based vision-language-action (VLA) policies offer strong expressivity for action generation, but suffer from a fundamental inefficiency: multi-step inference is required to recover action structure from uninformative Gaussian noise, leading to a poor efficiency-quality trade-off under real-time constraints. We add...	[ "Fan Du", "Feng Yan", "Jianxiong Wu", "Xinrun Xu", "Weiye Zhang", "Weinong Wang", "Yu Guo", "Bin Qian", "Zhihai He", "Fei Wang", "Heng Yang" ]	[ "cs.CV", "cs.AI" ]	[ "Computer Science" ]	2026-04-27T00:00:00	https://arxiv.org/abs/2604.24622	https://arxiv.org/pdf/2604.24622v2	2604.24622	10.48550/arXiv.2604.24622	0	0	true	https://github.com/EmbodiedAI-RoboTron/CF-VLA	arXiv.org	0.85
c21cf57ab244aba2fc1c6612e9a8cf2b3fe494be9252da2cbad816d1e9ca3d63	[ "arxiv", "semantic_scholar" ]	$M^2$-VLA: Boosting Vision-Language Models for Generalizable Manipulation via Layer Mixture and Meta-Skills	Current Vision-Language-Action (VLA) models predominantly rely on end-to-end fine-tuning. While effective, this paradigm compromises the inherent generalization capabilities of Vision-Language Models (VLMs) and incurs catastrophic forgetting. To address these limitations, we propose $M^2$-VLA, which demonstrates that a...	[ "Siyao Xiao", "Yuhong Zhang", "Zhifang Liu", "Zihan Gao", "Jingye Zhang", "Sinwai Choo", "Dake Zhong", "Mengzhe Wang", "Xiao Lin", "Xianfeng Zhou", "Jia Jia", "Haoqian Wang" ]	[ "cs.RO" ]	[ "Computer Science" ]	2026-04-27T00:00:00	https://arxiv.org/abs/2604.24182	https://arxiv.org/pdf/2604.24182v1	2604.24182	10.48550/arXiv.2604.24182	0	0	false	null	arXiv.org	0.55
b383d9c99272510184d1904fa9b7818c24e18caee707e9728d809ea60b1dca08	[ "arxiv", "semantic_scholar" ]	PokeVLA: Empowering Pocket-Sized Vision-Language-Action Model with Comprehensive World Knowledge Guidance	Recent advances in Vision-Language-Action (VLA) models have opened new avenues for robot manipulation, yet existing methods exhibit limited efficiency and a lack of high-level knowledge and spatial awareness. To address these challenges, we propose PokeVLA, a lightweight yet powerful foundation model for embodied manip...	[ "Yupeng Zheng", "Xiang Li", "Songen Gu", "Yuhang Zheng", "Shuai Tian", "Weize Li", "Linbo Wang", "Senyu Fei", "Pengfei Li", "Yinfeng Gao", "Zebin Xing", "Yilun Chen", "Qichao Zhang", "Haoran Li", "Wenchao Ding" ]	[ "cs.RO" ]	[ "Computer Science" ]	2026-04-22T00:00:00	https://arxiv.org/abs/2604.20834	https://arxiv.org/pdf/2604.20834v2	2604.20834	10.48550/arXiv.2604.20834	2	0	true	null	arXiv.org	0.85
215ffdd3ee6e5046328eb37ba351a4b21ae397ef0289458fe6476a20c688808c	[ "arxiv", "semantic_scholar" ]	EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training	Vision-Language-Action Models (VLAs) inherit their visual and linguistic capabilities from Vision-Language Models (VLMs), yet most VLAs are built from off-the-shelf VLMs that are not adapted to the embodied domain, limiting their downstream performance. In this work, we propose EmbodiedMidtrain to bridge the gap betwee...	[ "Yiyang Du", "Zhanqiu Guo", "Xin Ye", "Liu Ren", "Chenyan Xiong" ]	[ "cs.CV", "cs.AI", "cs.CL" ]	[ "Computer Science" ]	2026-04-21T00:00:00	https://arxiv.org/abs/2604.20012	https://arxiv.org/pdf/2604.20012v1	2604.20012	10.48550/arXiv.2604.20012	3	0	false	null	arXiv.org	0.55
10c0368353d9dc881afeb631431043782370ec79f095119bcafd3b6eb95d33bd	[ "arxiv", "semantic_scholar" ]	VLA Foundry: A Unified Framework for Training Vision-Language-Action Models	We present VLA Foundry, an open-source framework that unifies LLM, VLM, and VLA training in a single codebase. Most open-source VLA efforts specialize on the action training stage, often stitching together incompatible pretraining pipelines. VLA Foundry instead provides a shared training stack with end-to-end control, ...	[ "Jean Mercat", "Sedrick Keh", "Kushal Arora", "Isabella Huang", "Paarth Shah", "Haruki Nishimura", "Shun Iwase", "Katherine Liu" ]	[ "cs.RO", "cs.AI", "cs.CV", "cs.LG", "cs.SE" ]	[ "Computer Science" ]	2026-04-21T00:00:00	https://arxiv.org/abs/2604.19728	https://arxiv.org/pdf/2604.19728v1	2604.19728	10.48550/arXiv.2604.19728	0	0	true	https://github.com/TRI-ML/vla_foundry	arXiv.org	0.85
702d1274058ca2c1ec82526ffd033b8fc4f2e3b69e8c757b565ceaaec8549cba	[ "arxiv", "semantic_scholar" ]	ST-$π$: Structured SpatioTemporal VLA for Robotic Manipulation	Vision-language-action (VLA) models have achieved great success on general robotic tasks, but still face challenges in fine-grained spatiotemporal manipulation. Typically, existing methods mainly embed spatiotemporal knowledge into visual and action representations, and directly perform a cross-modal mapping for step-l...	[ "Chuanhao Ma", "Hanyu Zhou", "Shihan Peng", "Yan Li", "Tao Gu", "Luxin Yan" ]	[ "cs.RO", "cs.CV" ]	[ "Computer Science" ]	2026-04-20T00:00:00	https://arxiv.org/abs/2604.17880	https://arxiv.org/pdf/2604.17880v1	2604.17880	10.48550/arXiv.2604.17880	1	0	true	https://github.com/chuanhaoma/ST-pi	arXiv.org	0.85
df1bd04bb3c8c5b872fc3ca181686ac0cbe033ad216fffa90d562b15bf0c26f1	[ "arxiv", "semantic_scholar" ]	Unmasking the Illusion of Embodied Reasoning in Vision-Language-Action Models	Recent Vision-Language-Action (VLA) models report impressive success rates on standard robotic benchmarks, fueling optimism about general-purpose physical intelligence. However, recent evidence suggests a systematic misalignment between standard benchmark success and true embodied reasoning, raising the question of whe...	[ "Haiweng Xu", "Sipeng Zheng", "Hao Luo", "Wanpeng Zhang", "Ziheng Xi", "Zongqing Lu" ]	[ "cs.RO" ]	[ "Computer Science" ]	2026-04-20T00:00:00	https://arxiv.org/abs/2604.18000	https://arxiv.org/pdf/2604.18000v1	2604.18000	10.48550/arXiv.2604.18000	0	0	false	null	arXiv.org	0.55
d79d8331a119dd6705a9e9e7cfcee34afa34edde63e7ea54ec1595c6afef8054	[ "arxiv", "semantic_scholar" ]	ReconVLA: An Uncertainty-Guided and Failure-Aware Vision-Language-Action Framework for Robotic Control	Vision-language-action (VLA) models have emerged as generalist robotic controllers capable of mapping visual observations and natural language instructions to continuous action sequences. However, VLAs provide no calibrated measure of confidence in their action predictions, thus limiting their reliability in real-world...	[ "Lingling Chen", "Zongyao Lyu", "William J. Beksi" ]	[ "cs.RO", "cs.AI" ]	[ "Computer Science" ]	2026-04-17T00:00:00	https://arxiv.org/abs/2604.16677	https://arxiv.org/pdf/2604.16677v1	2604.16677	10.48550/arXiv.2604.16677	2	0	false	null	arXiv.org	0.5466
cab90f5d83e0618fd49047f785afe7e66dc1653047b07ebb7e13ff0d0092ae90	[ "arxiv", "semantic_scholar" ]	Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents	Reinforcement learning (RL) enables high-frequency, closed-loop control for robotic manipulation, but scaling to long-horizon tasks with sparse or imperfect rewards remains difficult due to inefficient exploration and poor credit assignment. Vision-Language-Action (VLA) models leverage large-scale multimodal pretrainin...	[ "Angelo Moroncelli", "Roberto Zanetti", "Marco Maccarini", "Loris Roveda" ]	[ "cs.LG", "cs.AI", "cs.RO" ]	[ "Computer Science" ]	2026-04-15T00:00:00	https://arxiv.org/abs/2604.13733	https://arxiv.org/pdf/2604.13733v2	2604.13733	null	0	0	false	null	null	0.3464
3cb21aaa519fff7e5fe4458b9e030cf0cdef066b90c1c1ee0a11133dc3884826	[ "arxiv", "semantic_scholar" ]	Robotic Manipulation is Vision-to-Geometry Mapping ($f(v) \rightarrow G$): Vision-Geometry Backbones over Language and Video Models	At its core, robotic manipulation is a problem of vision-to-geometry mapping ($f(v) \rightarrow G$). Physical actions are fundamentally defined by geometric properties like 3D positions and spatial relationships. Consequently, we argue that the foundation for generalizable robotic control should be a vision-geometry ba...	[ "Zijian Song", "Qichang Li", "Jiawei Zhou", "Zhenlong Yuan", "Tianshui Chen", "Liang Lin", "Guangrun Wang" ]	[ "cs.RO" ]	[ "Computer Science" ]	2026-04-14T00:00:00	https://arxiv.org/abs/2604.12908	https://arxiv.org/pdf/2604.12908v1	2604.12908	10.48550/arXiv.2604.12908	3	1	false	null	arXiv.org	0.5431
3c67fe1199e5723b208e25bc46f80cd78e2d34e754286dd47aab64adbd670bdd	[ "arxiv", "semantic_scholar" ]	STRONG-VLA: Decoupled Robustness Learning for Vision-Language-Action Models under Multimodal Perturbations	Despite their strong performance in embodied tasks, recent Vision-Language-Action (VLA) models remain highly fragile under multimodal perturbations, where visual corruption and linguistic noise jointly induce distribution shifts that degrade task-level execution. Existing robustness approaches typically rely on joint t...	[ "Yuhan Xie", "Yuping Yan", "Yunqi Zhao", "Handing Wang", "Yaochu Jin" ]	[ "cs.RO" ]	[ "Computer Science" ]	2026-04-11T00:00:00	https://arxiv.org/abs/2604.10055	https://arxiv.org/pdf/2604.10055v2	2604.10055	10.48550/arXiv.2604.10055	0	0	false	null	arXiv.org	0.5397
561b58a02fddc0bfb0b5cb7cb2d74ceea54f04d742656826c38765cb514cd8ef	[ "arxiv", "semantic_scholar" ]	ProGAL-VLA: Grounded Alignment through Prospective Reasoning in Vision-Language-Action Models	Vision language action (VLA) models enable generalist robotic agents but often exhibit language ignorance, relying on visual shortcuts and remaining insensitive to instruction changes. We present Prospective Grounding and Alignment VLA (ProGAL-VLA), which constructs a 3D entity-centric graph (GSM), uses a slow planner ...	[ "Nastaran Darabi", "Amit Ranjan Trivedi" ]	[ "cs.RO", "cs.CL", "cs.CV" ]	[ "Computer Science" ]	2026-04-10T00:00:00	https://arxiv.org/abs/2604.09824	https://arxiv.org/pdf/2604.09824v1	2604.09824	10.48550/arXiv.2604.09824	0	0	false	null	arXiv.org	0.5385
c3a582b4738c554242b41e7e776bef036b49e3dbb5d90da8dbec1c2db59d34fb	[ "arxiv", "semantic_scholar" ]	HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents	We introduce HY-Embodied-0.5, a family of foundation models specifically designed for real-world embodied agents. To bridge the gap between general Vision-Language Models (VLMs) and the demands of embodied agents, our models are developed to enhance the core capabilities required by embodied intelligence: spatial and t...	[ "Tencent Robotics X", "HY Vision Team", " :", "Xumin Yu", "Zuyan Liu", "Ziyi Wang", "He Zhang", "Yongming Rao", "Fangfu Liu", "Yani Zhang", "Ruowen Zhao", "Oran Wang", "Yves Liang", "Haitao Lin", "Minghui Wang", "Yubo Dong", "Kevin Cheng", "Bolin Ni", "Rui Huang", "Han Hu", "...	[ "cs.CV" ]	[ "Computer Science" ]	2026-04-08T00:00:00	https://arxiv.org/abs/2604.07430	https://arxiv.org/pdf/2604.07430v1	2604.07430	10.48550/arXiv.2604.07430	4	0	true	https://github.com/Tencent-Hunyuan/HY-Embodied	arXiv.org	0.8287
2130dcfef388dcc94173b9c5333244960d22f868b765a5cee5e897d618640146	[ "arxiv", "semantic_scholar" ]	Grounding Hierarchical Vision-Language-Action Models Through Explicit Language-Action Alignment	Achieving robot transparency is a critical step toward effective human-robot collaboration. To be transparent, a robot's natural language communication must be consistent with its actions and explicitly grounded in the task and environment. Existing hierarchical Vision-Language-Action (VLA) models can generate language...	[ "Theodor Wulff", "Federico Tavella", "Rahul Singh Maharjan", "Manith Adikari", "Angelo Cangelosi" ]	[ "cs.RO" ]	[ "Computer Science" ]	2026-04-07T00:00:00	https://arxiv.org/abs/2604.05614	https://arxiv.org/pdf/2604.05614v1	2604.05614	10.48550/arXiv.2604.05614	0	0	false	null	arXiv.org	0.5351
322eecfb62a673dd90b120e2ae23f79d297878a99b31f36a1b7859dd0be44405	[ "arxiv", "semantic_scholar" ]	E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes	Robotic Vision-Language-Action (VLA) models generalize well for open-ended manipulation, but their perception is fragile under sensing-stage degradations such as extreme low light, motion blur, and black clipping. We present E-VLA, an event-augmented VLA framework that improves manipulation robustness when conventional...	[ "Jiajun Zhai", "Hao Shi", "Shangwei Guo", "Kailun Yang", "Kaiwei Wang" ]	[ "cs.CV", "cs.MM", "cs.RO", "eess.IV" ]	[ "Computer Science", "Engineering" ]	2026-04-06T00:00:00	https://arxiv.org/abs/2604.04834	https://arxiv.org/pdf/2604.04834v1	2604.04834	10.48550/arXiv.2604.04834	0	0	true	https://github.com/JJayzee/E-VLA	arXiv.org	0.8252
8894f53bda319754afe6291e8c06986ae7ae000a1c8e377d84f7e98048a18c02	[ "arxiv", "semantic_scholar" ]	StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing	Building generalist embodied agents requires integrating perception, language understanding, and action, which are core capabilities addressed by Vision-Language-Action (VLA) approaches based on multimodal foundation models, including recent advances in vision-language models and world models. Despite rapid progress, V...	[ "StarVLA Community" ]	[ "cs.RO", "cs.AI", "cs.CV" ]	[ "Computer Science" ]	2026-04-06T00:00:00	https://arxiv.org/abs/2604.05014	https://arxiv.org/pdf/2604.05014v1	2604.05014	10.48550/arXiv.2604.05014	45	8	true	https://github.com/starVLA/starVLA	arXiv.org	0.8252
d75b788c3f174068ff6cf706da065e91fd800db79162ebdbc31125ecec4df2a7	[ "arxiv", "semantic_scholar" ]	VLA-Forget: Vision-Language-Action Unlearning for Embodied Foundation Models	Vision-language-action (VLA) models are emerging as embodied foundation models for robotic manipulation, but their deployment introduces a new unlearning challenge: removing unsafe, spurious, or privacy-sensitive behaviors without degrading perception, language grounding, and action control. In OpenVLA-style policies, ...	[ "Ravi Ranjan", "Agoritsa Polyzou" ]	[ "cs.CV", "cs.AI" ]	[ "Computer Science" ]	2026-04-05T00:00:00	https://arxiv.org/abs/2604.03956	https://arxiv.org/pdf/2604.03956v2	2604.03956	10.48550/arXiv.2604.03956	2	0	false	null	arXiv.org	0.5328
c5403c56b97119733a08cbba7797915809f62d3d2dfd4083035c48130c5bdd8e	[ "arxiv", "semantic_scholar" ]	UAV-Track VLA: Embodied Aerial Tracking via Vision-Language-Action Models	Embodied visual tracking is crucial for Unmanned Aerial Vehicles (UAVs) executing complex real-world tasks. In dynamic urban scenarios with complex semantic requirements, Vision-Language-Action (VLA) models show great promise due to their cross-modal fusion and continuous action generation capabilities. To benchmark mu...	[ "Qiyao Zhang", "Shuhua Zheng", "Jianli Sun", "Chengxiang Li", "Xianke Wu", "Zihan Song", "Zhiyong Cui", "Yisheng Lv", "Yonglin Tian" ]	[ "cs.CV", "cs.RO" ]	[ "Computer Science" ]	2026-04-02T00:00:00	https://arxiv.org/abs/2604.02241	https://arxiv.org/pdf/2604.02241v2	2604.02241	10.48550/arXiv.2604.02241	1	0	true	https://github.com/Hub-Tian/UAV-Track_VLA	arXiv.org	0.8181
e3dc348233645dd759da536b888f3eab84852425b68f5955f1dc98629cab8e53	[ "arxiv", "semantic_scholar" ]	Hierarchical Pre-Training of Vision Encoders with Large Language Models	The field of computer vision has experienced significant advancements through scalable vision encoders and multimodal pre-training frameworks. However, existing approaches often treat vision encoders and large language models (LLMs) as independent modules, limiting the integration of hierarchical visual features. In th...	[ "Eugene Lee", "Ting-Yu Chang", "Jui-Huang Tsai", "Jiajie Diao", "Chen-Yi Lee" ]	[ "cs.CV", "cs.AI", "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2026-03-31T00:00:00	https://arxiv.org/abs/2604.00086	https://arxiv.org/pdf/2604.00086v1	2604.00086	10.48550/arXiv.2604.00086	0	0	false	null	arXiv.org	0.5271
11c90e53e070f681c906ed89a95e3f87c06c1f0d3bfd73ea28b7cd85f657c9b4	[ "arxiv", "semantic_scholar" ]	ProgressVLA: Progress-Guided Diffusion Policy for Vision-Language Robotic Manipulation	Most existing vision-language-action (VLA) models for robotic manipulation lack progress awareness, typically relying on hand-crafted heuristics for task termination. This limitation is particularly severe in long-horizon tasks involving cascaded sub-goals. In this work, we investigate the estimation and integration of...	[ "Hongyu Yan", "Qiwei Li", "Jiaolong Yang", "Yadong Mu" ]	[ "cs.RO", "cs.AI" ]	[ "Computer Science" ]	2026-03-29T00:00:00	https://arxiv.org/abs/2603.27670	https://arxiv.org/pdf/2603.27670v1	2603.27670	10.48550/arXiv.2603.27670	3	0	false	null	arXiv.org	0.5248
cbcecb279746ede8a1c21c60ad9afc7f169705e143d2f3525961f1c63b6b90e3	[ "arxiv", "semantic_scholar" ]	Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses	Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open-world, safety-critical environments. As these systems gain autonomy and enter domains such as transportation, healthcare, and industrial or assistive robotics, ensuring their safet...	[ "Xiao Li", "Xiang Zheng", "Yifeng Gao", "Xinyu Xia", "Yixu Wang", "Xin Wang", "Ye Sun", "Yunhan Zhao", "Ming Wen", "Jiayu Li", "Zixing Chen", "Xun Gong", "Yi Liu", "Yige Li", "Yutao Wu", "Cong Wang", "Jun Sun", "Yixin Cao", "Zhineng Chen", "Jingjing Chen", "Tao Gui", "Qi Zh...	[ "cs.CR", "cs.AI", "cs.CV", "cs.RO" ]	[ "Computer Science" ]	2026-03-28T00:00:00	https://arxiv.org/abs/2605.02900	https://arxiv.org/pdf/2605.02900v2	2605.02900	null	0	0	false	null	null	0.3332

End of preview. Expand in Data Studio

Vision-Language-Action (VLA) & Robot Learning Papers — FineSet

A research-paper dataset on Vision-Language-Action (VLA) & Robot Learning Papers, assembled, deduplicated, and quality-scored by FineSet from arXiv and Semantic Scholar.

📸 This is a dated snapshot — generated 2026-06-19. It is not auto-updated. Research on Vision-Language-Action (VLA) & Robot Learning Papers moves fast — new papers land on arXiv every week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. ↓

Why this dataset

Quality-scored: quality_score float (0–1), blends citations with recency + code/venue signals — filter out the noise
Papers with code: 96 flagged via has_code — find reproducible work fast
Deduplicated: arXiv + Semantic Scholar cross-referenced, duplicate records merged
Clean JSONL: 484 records, one per line, normalized fields — no encoding garbage

Dataset details

Records: 484
Date range: 2021–2026
Snapshot date: 2026-06-19 (frozen — see note above)
Sources: arXiv, Semantic Scholar (cross-referenced, duplicates merged)
arXiv categories: cs.RO, cs.LG, cs.CV
Quality scoring: citations + recency + code/venue blend, 0–1 (p50=0.401, p90=0.65)
Format: JSONL, one record per line

Fields

Field	Type	Description
id	string	Deterministic SHA256 record id
sources	list	Which sources contributed (`arxiv`, `semantic_scholar`)
title	string	Paper title
abstract	string	Full abstract
authors	list	Author names
categories	list	arXiv category codes
fields_of_study	list	Semantic Scholar field tags
published_date	string	ISO 8601 date
url	string	arXiv abstract URL
pdf_url	string\|null	Open-access PDF if available
arxiv_id	string\|null	arXiv identifier
doi	string\|null	DOI if available
citation_count	int	Citation count (Semantic Scholar)
influential_citation_count	int	Influential citations (Semantic Scholar)
has_code	bool	Code repo detected in the arXiv comment
code_url	string\|null	GitHub URL if detected
venue	string\|null	Publication venue
quality_score	float	0–1, blended (citations + recency + code/venue)

Quality score methodology

quality_score = max(impact, freshness), clamped to [0, 1], where:

impact = max( log10(citations+1)/4 , log10(influential_citations+1)/2 ) — realized impact (0.5 at 100 citations, ~0.75 at 1,000, 1.0 at 10,000+).
freshness = recency × (0.35 + 0.30·has_code + 0.20·has_venue) — a baseline for recent papers (so a strong paper published this week isn't scored 0 just for lacking citations), where recency is 1.0 for papers ≤60 days old and decays linearly to 0 by ~18 months.

Old highly-cited papers score on impact; brand-new papers score on freshness; old uncited papers score ~0. Useful for filtering training data by quality, not just age.

👉 Want this on YOUR topic, updated daily?

This snapshot is frozen at 2026-06-19. The live FineSet pipeline keeps a dataset like this refreshed every day on whatever topic you describe — new papers in, dedup and quality scoring automatic, export as JSONL/Parquet or push straight to the Hub.

Tell me the topic you'd want and I'll run the pipeline on it — open a discussion on this dataset, it's free and it's how I decide what to build next.

→ fineset.io — describe what you want to train on, get a dataset. Early-access waitlist open (referral skip available).

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