网站onetab
chrome://extensions/?id=chphlpgkkbolifaimnlloiipkdnihall | 扩展程序 - OneTab
https://www.google.com/search?q=onetab+%E5%A4%87%E4%BB%BD&oq=onetab+%E5%A4%87%E4%BB%BD&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIGCAEQRRg90gEIMzA2N2owajeoAgCwAgA&sourceid=chrome&ie=UTF-8&source=chrome.ob | onetab 备份 - Google Search
https://hjfy.top/arxiv/2602.20200 | Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation
https://chatgpt.com/c/6a887956-1da0-83ec-9c8d-5cb518c7888f | 检索对象中心VLA工作
https://liauto-dsr.github.io/AVA-VLA-Page/ | AVA-VLA | CVPR 2026 Project Page
https://hjfy.top/arxiv/2506.16211 | ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models
https://hjfy.top/arxiv/2602.01067 | A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation
https://hjfy.top/arxiv/2602.23408 | Demystifying Action Space Design for Robotic Manipulation Policies
https://openreview.net/forum?id=UC0fdiMJdU | Unified Noise Steering for Efficient Human-Guided VLA Adaptation | OpenReview
https://arxiv.org/pdf/2606.02735 | See Less, Specify More: Visual Evidence Budgets for Generalizable VLAs
https://hjfy.top/arxiv/2602.17659 | When Vision Overrides Language: Evaluating and Mitigating Counterfactual Failures in VLAs
https://chatgpt.com/c/6a8d4aa3-c3d4-83ec-8198-84668e0f9488 | 修改双动作专家
https://github.com/microsoft/simmim | microsoft/SimMIM: This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".
https://hjfy.top/arxiv/2603.25399 | LaMP: Learning Vision-Language-Action Policies with 3D Scene Flow as Latent Motion Prior
https://arxiv.org/pdf/2605.30484 | ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation
https://openreview.net/forum?id=vPKy3qQG3W | 从人手到机械臂：通过轨迹对齐实现操作技能迁移 | OpenReview
https://arxiv.org/pdf/2602.20200 | Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation
https://hjfy.top/arxiv/2410.24221 | EgoMimic: Scaling Imitation Learning via Egocentric Video
https://co-training-lbm.github.io/files/TRI-LBM-co-training.pdf | co-training-lbm.github.io/files/TRI-LBM-co-training.pdf
https://www.google.com/sorry/index?continue=https://scholar.google.com/scholar%3Foi%3Dbibs%26hl%3Dzh-CN%26cites%3D8314152825717464710&hl=zh-CN&q=EgRDnzTnGLia_NQGIiwl2O-Sd7nDXOu6J8gHemxvjeg3MUt8bXdDUYj360biLJxbVzNUgw_ROSFYHTICclJaAUM | https://scholar.google.com/scholar?oi=bibs&hl=zh-CN&cites=8314152825717464710
https://arxiv.org/pdf/2603.19199 | FASTER: Rethinking Real-Time Flow VLAs
https://arxiv.org/pdf/2604.05656 | SnapFlow: One-Step Action Generation for Flow-Matching VLAs via Progressive Self-Distillation
https://chatgpt.com/c/6a64cfb2-06f8-83ec-8695-91f35c14218e | VLA模型与异构数据
https://www.google.com.hk/search?q=wallx+train_fsdp.py&sca_esv=8b4e0f10aed68b61&sxsrf=APpeQnvyxLB_qWJAPTtHJS61E1QYgMTaFg%3A1782586121428&ei=CRtAaunUGfqiseMPw4aasAY&biw=2213&bih=999&ved=0ahUKEwip_bHkiqiVAxV6UWwGHUODBmYQ4dUDCBI&uact=5&oq=wallx+train_fsdp.py&gs_lp=Egxnd3Mtd2l6LXNlcnAiE3dhbGx4IHRyYWluX2ZzZHAucHlIwwxQgwpYuAtwAXgAkAEAmAGKAaABigGqAQMwLjG4AQPIAQD4AQL4AQGYAgCgAgCYAwCIBgGSBwCgBySyBwC4BwDCBwDIBwCACAE&sclient=gws-wiz-serp | wallx train_fsdp.py - Google Search
https://github.com/samsja/muon_fsdp_2/blob/main/train_fsdp.py | muon_fsdp_2/train_fsdp.py at main · samsja/muon_fsdp_2
https://github.com/X-Square-Robot/wall-x | X-Square-Robot/wall-x：基于具身基础模型的通用机器人构建
https://x2robot.com/en/research | X Square Official Site X Square Robot WALL-A Large Operating Model Robot
https://arxiv.org/abs/2606.20781 | [2606.20781] World Action Models: A Survey
https://arxiv.org/pdf/2606.27374 | World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays
https://gemini.google.com/app/0a2844f9eed48835 | FlashAttention 编译错误解决 - Google Gemini
https://lilianweng.github.io/posts/2026-06-24-scaling-laws/ | 仔细审视尺度定律 | Lil'Log
https://github.com/karpathy/autoresearch | karpathy/autoresearch: AI agents running research on single-GPU nanochat training automatically
https://hjfy.top/arxiv/2602.23408 | Demystifying Action Space Design for Robotic Manipulation Policies
https://chatgpt.com/g/g-p-6a9b96ad20448191ace72c3f06a1e21f/c/6a996e69-8b5c-83ec-aec2-66220ca61df6 | action expert - 跨形态协同训练深化
https://hjfy.top/arxiv/2607.19745 | EgoRecovery: Acquiring Failure Recovery Ability Through Human Recovery Demonstration
https://arxiv.org/pdf/2607.28698 | arxiv.org/pdf/2607.28698
https://arxiv.org/pdf/2608.15748 | Making two action heads agree: coordination mechanisms and a runtime collapse certificate for flow-matching policies
https://hjfy.top/arxiv/2608.15748 | Making two action heads agree: coordination mechanisms and a runtime collapse certificate for flow-matching policies
https://mm-flow.github.io/?utm_source=chatgpt.com | Flow Matching for Multimodal Distributions
https://hjfy.top | 幻觉翻译 - arXiv 及文档翻译
https://hjfy.top/arxiv/2512.23703 | Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation
https://arxiv.org/pdf/2604.23073 | RL Token: Bootstrapping Online RL with Vision-Language-Action Models
https://arxiv.org/pdf/2605.27970 | Geometry of Human Perceptual Domains Emerges Transiently in LLM Representations
https://hjfy.top/arxiv/2606.10025 | GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
https://arxiv.org/pdf/2605.04970 | Skill Neologisms: Towards Skill-based Continual Learning
https://docs.google.com/document/d/1b3P0aOPkgSBUuQF76xwyK_Cgg5tszxswOt3m01HvgfE/edit?tab=t.0 | VLA 跨实施形态动作表征与时空谱域解耦流匹配的完美融合架构 - Google 文档
https://blog.jxmo.io/p/zen-and-the-art-of-machine-learning | Zen and the Art of Machine Learning Research
https://tml.stanford.edu/cloak/ | Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA
https://www.google.com/search?q=Cloak%3A+Zero-Shot+Cross-Embodiment+Manipulation+by+Masking+the+End-Effector+from+the+VLA&sourceid=chrome&ie=UTF-8 | Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA - Google Search
https://openaccess.thecvf.com/content/CVPR2026W/GigaBrainChallenge/papers/Ge_CrossZero_Zero-Shot_of_Unseen_Embodiment_Manipulation_via_Object-Centric_Hierarchical_Diffusion_CVPRW_2026_paper.pdf | CrossZero: Zero-Shot of Unseen Embodiment Manipulation via Object-Centric Hierarchical Diffusion Models
https://hjfy.top/arxiv/2606.22836 | Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA
https://arxiv.org/pdf/2606.11087 | Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning
https://gemini.google.com/app/cc8b43e10e0762d2 | 深度学习中的级联模型结构 - Google Gemini
https://www.google.com/search?q=factr2&oq=factr2&gs_lcrp=EgZjaHJvbWUyBggAEEUYOdIBCDE1ODFqMGo3qAIIsAIB&sourceid=chrome&ie=UTF-8 | factr2 - Google Search
https://arxiv.org/pdf/2603.16666 | Fast-WAM: Do World Action Models Need Test-time Future Imagination?
https://www.nvidia.com/en-us/glossary/world-action-model/ | What Is a World Action Model (WAM)? | NVIDIA Glossary
https://arxiv.org/pdf/2602.15922#page=15 | World Action Models are Zero-shot Policies
https://gemini.google.com/app | Google Gemini
https://x.com/vintcessun/status/2071482045189197853 | X 上的 恒星：“1个反直觉发现：现有FlowDPS/FLOWER等流逆求解器居然在“矫正速度场”上走偏路。理论证明，贝叶斯条件化应重新加权源分布而非修改轨迹——零阶、高斯、近端近似都只是不同偏差。为什么重要？未被量化的近似使这类求解器的可靠性成谜。论文怎么解：从后验传输严格推导偏差上界，提出仅靠源重新加权+原始速度” / X
https://paperswithcode.co/paper/2606.18394 | JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting | Papers with Code
https://paperswithcode.co/paper/2606.28128 | PhysisForcing: Physics Reinforced World Simulator for Robotic Manipulation | Papers with Code
https://gemini.google.com/app/fb9bc57451d35114 | PointACT 使用 Concerto 模型 - Google Gemini
https://openreview.net/forum?id=rEhVHla9zp | BadVLA：基于目标解耦优化的视觉-语言-动作模型后门攻击 | OpenReview
https://arxiv.org/pdf/2605.31559 | Functional Attention: From Pairwise Affinities to Functional Correspondences
https://arxiv.org/pdf/2407.00299 | 2407.00299
https://arxiv.org/pdf/2001.06782 | 2001.06782
https://gemini.google.com/app/9b2c8d210500464b | StarVLA 实现 $\pi_0$ / $\pi_{0.5}$ 特性 - Google Gemini
https://arxiv.org/abs/2606.20246 | [2606.20246] Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
https://chatgpt.com | ChatGPT
https://arxiv.org/abs/2605.27284 | [2605.27284] FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action Policies
https://arxiv.org/abs/2001.07685 | [2001.07685] FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/google-research/fixmatch | google-research/fixmatch: A simple method to perform semi-supervised learning with limited data.
https://arxiv.org/pdf/2001.07685 | 2001.07685
https://www.google.com/search?q=MimicDreamer&sourceid=chrome&ie=UTF-8 | www.google.com
https://openreview.net/profile?id=~Haoyun_Li1 | Haoyun Li | OpenReview
https://openreview.net/forum?id=xCAum9gOkj | MimicDreamer: Aligning Human and Robot Demonstrations for Scalable VLA Training | OpenReview
https://hjfy.top | 幻觉翻译 - arXiv 及文档翻译
https://hjfy.top/arxiv/2509.22199 | MimicDreamer: Aligning Human and Robot Demonstrations for Scalable VLA Training
https://www.google.com/search?q=mimicDreamer&sca_esv=bcc915fd4b92abab&sxsrf=ANbL-n6sL8UDgxCX3slD-7R7ej5MFYKXpA%3A1781017021476&ei=vSkoavvaHKLl4-EPi_PkmQU&biw=2277&bih=1083&ved=0ahUKEwi7rZ23tfqUAxWi8jgGHYs5OVMQ4dUDCBA&uact=5&oq=mimicDreamer&gs_lp=Egxnd3Mtd2l6LXNlcnAiDG1pbWljRHJlYW1lcjIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzINEAAYgAQYigUYQxiwAzINEAAYgAQYigUYQxiwAzINEAAYgAQYigUYQxiwAzINEAAYgAQYigUYQxiwAzIOEAAY5AIY1gQYsAPYAQEyDhAAGOQCGNYEGLAD2AEBMg4QABjkAhjWBBiwA9gBATIXEC4Y3AYYuAYY2gYY2AIYyAMYsAPYAQEyFxAuGNgCGLgGGNoGGNwGGMgDGLAD2AEBMhcQLhjYAhi4BhjaBhjcBhjIAxiwA9gBATIXEC4Y2AIYuAYY2gYY3AYYyAMYsAPYAQFIwyFQ1SBY1SBwBHgBkAEAmAEAoAEAqgEAuAEDyAEA-AEBmAIEoAIMmAMA4gMFEgExIECIBgGQBhO6BgYIARABGAmSBwE0oAcAsgcAuAcAwgcFMC4zLjHIBwqACAE&sclient=gws-wiz-serp | www.google.com
https://fugumt.com/fugumt/paper_check/2509.22199v2_enmode | Related papers: MimicDreamer: Aligning Human and Robot Demonstrations for Scalable VLA Training
https://openreview.net/forum/de11ceea59ac7d006e767ff330f8ab6d12bea124.pdf | Forum | OpenReview
https://hjfy.top/arxiv/2605.27284 | FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action Policies
https://chatgpt.com/c/6a26ef9f-6d30-8321-8f9b-3dc1777c282c?mweb_fallback=1 | chatgpt.com
https://drive.google.com/drive/folders/1yZq0GV9Rd7hjS9FEuyy3PJKpGnWZlolU | analysis - Google 云端硬盘
https://research.nvidia.com/labs/eai/blogs/scaling-video-training-with-parallelism/ | Scaling Video Training with Parallelism
https://arxiv.org/abs/2506.01583 | [2506.01583] FreqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens
https://hjfy.top/arxiv/2506.08822 | FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency
https://gemini.google.com/app/e4005e94aea56792 | 频域流匹配在机器人控制中的应用 - Google Gemini
https://openreview.net/pdf?id=NKNryrCGYn | pdf
https://hjfy.top/arxiv/2605.01581v4 | Hyper-DP3: Frequency-Aware Right-Sizing of 3D Diffusion Policies for Visuomotor Control
https://gemini.google.com/app/13e3efc924182d68 | 机器人强化学习新视角 - Google Gemini
https://gemini.google.com/app/34b1dbcac9daff52 | Google Gemini
https://www.google.com/search?q=reqPolicy%3A+Frequency+Autoregressive+Visuomotor+Policy+with+Continuous+Tokens&sourceid=chrome&ie=UTF-8 | reqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens - Google Search
https://openreview.net/forum?id=eKFrXWb0sT¬eId=QfzK1HG574 | Frequency-Enhanced Data Augmentation for Vision-and-Language Navigation | OpenReview
https://arxiv.org/pdf/2512.01031 | VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference
https://hjfy.top/arxiv/2604.13001 | XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios
https://github.com/X-Square-Robot/XRZero-G0 | GitHub - X-Square-Robot/XRZero-G0: XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios · GitHub
https://huggingface.co/datasets/x-square-robot/XRZero-G0-3K/tree/main | x-square-robot/XRZero-G0-3K at main
https://www.google.com/search?q=self+forcing%E8%A7%A3%E8%AF%BB&sca_esv=4a455b0f3ad2390b&sxsrf=ANbL-n6FdBzvgtcVSVhFvbEXQWH3B9x1Gg%3A1781070268157&ei=vPkoaoGVCZya4-EPw67GiQM&biw=2277&bih=1083&ved=0ahUKEwiB45zl-_uUAxUczTgGHUOXMTEQ4dUDCBA&uact=5&oq=self+forcing%E8%A7%A3%E8%AF%BB&gs_lp=Egxnd3Mtd2l6LXNlcnAiEnNlbGYgZm9yY2luZ-ino-ivuzIFEAAYgAQyBRAAGO8FMgUQABjvBTIFEAAY7wVInw9Q-A1Y-A1wAXgBkAEAmAGFAaABhQGqAQMwLjG4AQPIAQD4AQGYAgKgAosBwgIKEAAYRxjWBBiwA8ICDRAAGIAEGIoFGEMYsAPCAhcQLhjcBhi4BhjaBhjYAhjIAxiwA9gBAZgDAIgGAZAGFLoGBggBEAEYGZIHAzEuMaAHxgGyBwMwLjG4B4cBwgcFMC4xLjHIBwSACAE&sclient=gws-wiz-serp | self forcing解读 - Google Search
https://github.com/thu-ml/Causal-Forcing | GitHub - thu-ml/Causal-Forcing：[ICML 2026] “Causal Forcing：用于高质量实时交互式视频生成的自回归扩散蒸馏”和 Causal Forcing++ 的官方代码库 · GitHub
https://hjfy.top/arxiv/1806.07366 | Neural Ordinary Differential Equations
https://arxiv.org/html/2606.08242v1 | Light-WAM: Efficient World Action Models with State-Fusion Action Decoding
https://arxiv.org/abs/2605.27919 | [2605.27919] Frequency-Guided Action Diffusion via Sub-Frequency Manifold Traversal
https://www.reddit.com/r/Scholar/comments/1cczwfk/article_learning_spatiotemporal_and_motion/ | [Article] Learning SpatioTemporal and Motion Features in a Unified 2D Network for Action Recognition - M. Wang, J. Xing, J. Su, J. Chen and Y. Liu, : r/Scholar
https://blog.jxmo.io/p/zen-and-the-art-of-machine-learning | Zen and the Art of Machine Learning Research
https://gemini.google.com/app/68a20df833eb0776 | WAM4D 融合 PointACT 策略 - Google Gemini
https://x.com/_akhaliq/status/2076727488168841609 | X 上的 AK：“Video Generation Models are General-Purpose Vision Learners https://t.co/9IPa1VH8Jj” / X
https://hjfy.top/arxiv/2607.06655 | Pelican-VLA 0.5: Attending Before Acting Benefits Generalization
https://x.com/HuggingPapers/status/2072842236761346092 | X 上的 DailyPapers：“DART: one-shot VLA adaptation under environmental shifts Seoul National University researchers show that weight space arithmetic can isolate domain shifts from task knowledge, letting you adapt a robot policy to new cameras or embodiments with a single demonstration. https://t.co/oxsgrUH2eO” / X
https://arxiv.org/pdf/2607.02501 | Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
https://arxiv.org/pdf/2607.02503 | VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation
https://twkang43.github.io/projects/dart/ | Domain Arithmetic: One-Shot VLA Adaptation under Environmental Shifts
https://gemini.google.com/app/cbb5f44ee7f26223 | 频域信号的特点与优势 - Google Gemini
https://x.com/home | (21) 主页 / X
https://arxiv.org/abs/2607.01686 | [2607.01686] WARP: Weight-Space Analysis for Recovering Training Data Portfolios
https://hjfy.top | 幻觉翻译 - arXiv 及文档翻译
https://hjfy.top/arxiv/2607.02515 | 幻觉翻译 - arXiv 及文档翻译
https://chatgpt.com | ChatGPT
https://www.doubao.com/chat/38433990578089986 | nas数据传输优化 - 豆包
https://gemini.google.com/app/d79b073fe0880213 | HumanEgo 技术细节梳理 - Google Gemini
http://10.15.88.87:8090 | All Systems / Beszel
https://hjfy.top/arxiv/2606.12604 | EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations
https://arxiv.org/html/2606.28133v1 | Translation as a Bridging Action: Transferring Manipulation Skills from Humans to Robots
https://gemini.google.com/app/56833b0e2dc8199b | Pip Install Error: No Space Left - Google Gemini
https://hjfy.top/arxiv/2602.16710 | EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data
https://github.com/starVLA/starVLA/blob/starVLA_dev/docs/faq.md | starVLA/docs/faq.md at starVLA_dev · starVLA/starVLA
https://www.cnblogs.com/duzetao/p/19730124 | h2r - 张达嘛 - 博客园
https://www.google.com/search?q=codex+%E5%AF%B9%E8%AF%9D+%E5%8E%86%E5%8F%B2&sca_esv=55ef6e74c89a4cce&sxsrf=APpeQnudPPrC0LjULbl9JtmNa8LZI7WZxg%3A1783333645243&ei=DYNLari8Dpqb4-EPxLiiqAY&biw=2181&bih=957&oq=codex%E6%9F%A5%E7%9C%8B%E6%89%80%E6%9C%89%E7%9A%84%E5%AF%B9%E8%AF%9D&gs_lp=Egxnd3Mtd2l6LXNlcnAiGmNvZGV45p-l55yL5omA5pyJ55qE5a-56K-dKgIIAjIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwA0jYFVAAWABwAXgBkAEAmAEAoAEAqgEAuAEByAEAmAIBoAICmAMAiAYBkAYGkgcBMaAHALIHALgHAMIHAzAuMcgHAYAIAQ&sclient=gws-wiz-serp | codex 对话 历史 - Google Search
https://crs.ruinique.com/login | Login - Ruinique API
https://yesailab.feishu.cn/wiki/VIBHw14nSiRNoHkP63Zc0llYnYd | 服务器空间清理指南 - Feishu Docs
https://github.com/xpeng-robotics/UniT | About eef action format! · Issue #3 · xpeng-robotics/UniT
https://www.cnblogs.com/duzetao/p/19131705 | ubuntu daili - 张达嘛 - 博客园
https://gemini.google.com/app/fb9bc57451d35114 | PointACT 使用 Concerto 模型 - Google Gemini
https://docs.google.com/document/d/1h_MAFYm1XehpDxTKWbjzKAIChsPVxJ_Q0m5vPMsjoz8/edit?tab=t.0 | Spatio-Temporal Spectral-Decoupled Flow Matching for VLA (V2 Detail) - Google 文档
https://docs.google.com/document/d/1yCVddjSDm7W49Siu5iT_K5VGLN3OnJWEOVG9XUoMADk/edit?tab=t.0 | Spatio-Temporal Spectral-Decoupled Flow Matching for VLA (V3 Full Mathematical Derivation) - Google 文档
https://docs.google.com/document/d/1X4X6RHqxPEcaRFM3Ak2lCtOC359502-CLZokJk5ig5A/edit?tab=t.0 | 你帮我把我们聊的整合成一个文档 - Google 文档
https://github.com/starVLA/starVLA | GitHub - starVLA/starVLA: StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing · GitHub
https://linux.do/login | LINUX DO - 新的理想型社区
https://claude.ai/upgrade/max | Upgrade - Claude
https://www.google.com/search?q=VLA+backdoor&oq=VLA+backdoor&gs_lcrp=EgZjaHJvbWUyBggAEEUYOdIBCDIyNzNqMGo3qAIIsAIB&sourceid=chrome&ie=UTF-8 | VLA backdoor - Google Search
https://arxiv.org/html/2601.04266v2 | State Backdoor: Towards Stealthy Real-world Poisoning Attack on Vision-Language-Action Model in State Space
https://arxiv.org/pdf/2505.16640 | BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization
https://badvla-project.github.io | BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization
https://openreview.net/forum?id=rEhVHla9zp | BadVLA：基于目标解耦优化的视觉-语言-动作模型后门攻击 | OpenReview
https://gemini.google.com/app/10965e4ef8b4bc21 | AI 模型后门攻击详解 - Google Gemini
https://github.com/TX-Leo/HumanEgo/issues/6 | Question about Humanego paper · Issue #6 · TX-Leo/HumanEgo
https://www.doubao.com/chat | 豆包 - 字节跳动旗下 AI 智能助手
https://gemini.google.com/app/d79b073fe0880213 | HumanEgo 技术细节梳理 - Google Gemini
https://hjfy.top | 幻觉翻译 - arXiv 及文档翻译
https://cshizhe.github.io/projects/pointact.html | PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction
https://cshizhe.github.io | Shizhe Chen
https://github.com/cshizhe | cshizhe (Shizhe Chen)
https://www.google.com/search?q=PointACT%3A+Vision-Language-Action+Models+with+Multi-Scale+Point-Action+Interaction&sourceid=chrome&ie=UTF-8 | PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction - Google Search
https://roboticsconference.org/program/papers/73/ | PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction · Robotics: Science and Systems
https://github.com/zetaodu/Awesome-Robotics-Manipulation | zetaodu/Awesome-Robotics-Manipulation: A comprehensive list of papers about Robot Manipulation, including papers, codes, and related websites.
https://mail.shanghaitech.edu.cn/coremail/XT/index.jsp?sid=CAbFYnjjLbCyRrhxAqkpodSEhOiknTBs#mail.compose|%7B%22id%22%3A%221782320227657%22%7D | ShanghaiTech Mails
https://www.cnblogs.com/duzetao/p/19730124 | h2r - 张达嘛 - 博客园
https://huggingface.co/xpeng-robotics/DIAL_checkpoints/tree/main | xpeng-robotics/DIAL_checkpoints at main
https://crs.ruinique.com/login | Login - Ruinique API
https://www.cnblogs.com/clnchanpin/p/19203767 | 论文笔记：π0.5 (PI 0.5)KI改进版 - 详解 - clnchanpin - 博客园
https://gemini.google.com/app/e9f763efc37ba1a0 | 梯度截断在 Embodied AI 中的应用 - Google Gemini
https://arxiv.org/pdf/2605.31559 | Functional Attention: From Pairwise Affinities to Functional Correspondences
https://arxiv.org/pdf/2407.00299 | 2407.00299
https://hjfy.top/arxiv/2407.00299 | Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition
https://gemini.google.com/app/a6e06c64b3de7d67 | GR00T N1.5 模型深度解析 - Google Gemini
https://chatgpt.com/c/6a3cd418-1b70-83ec-abf2-fdec66c41599 | OpenPI pi05 梯度截断解释
https://arxiv.org/pdf/2001.06782 | 2001.06782
https://api.ikuncode.cc/console/token | IKunCode
https://hjfy.top/arxiv/2001.06782 | Gradient Surgery for Multi-Task Learning
https://hjfy.top/arxiv/2605.21414 | PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction
https://gemini.google.com/app/9b2c8d210500464b | StarVLA 实现 $\pi_0$ / $\pi_{0.5}$ 特性 - Google Gemini
https://www.google.com/search?q=%E4%BA%BA%E6%89%8B%E8%85%95%E9%83%A8%E7%9B%B8%E6%9C%BA&sca_esv=eeb84f3a1330d64b&sxsrf=APpeQns7KHzRYZ2zAWQMckezOdq7YQ6hQQ:1782389336354&ei=WBo9as6CFfKQnesP8IzasAc&start=10&sa=N&sstk=AU9db-ChL6rXKEyH4I1dXGCJDoln_VsuJF-ZEZYlbOTK2910boiFCUGS1hsIQAWsQ1MiPkvlk9S7Od-KNfIL76RUQyJmEEmr3Menhw&ved=2ahUKEwjOjvrZraKVAxVySGcHHXCGFnYQ8NMDegQIIxAW&biw=2213&bih=999&dpr=1 | 人手腕部相机 - Google Search
https://x.com/home | (21) 主页 / X
https://github.com/zetaodu/chinese-buy-us-stock-guide | zetaodu/chinese-buy-us-stock-guide: 美股指南
https://github.com/zetaodu/China-US-Stock-Guide | zetaodu/China-US-Stock-Guide: 2026年最新中国大陆居民炒美股合规指南。包含嘉信理财(Charles Schwab)、第一证券(Firstrade)、盈透证券(IBKR)开户评测；华美银行Velo、Wise无损出入金及防风控、避开CRS风险实操教程。｜by neclo0752（财哥笔记）
https://arxiv.org/abs/2606.20246 | [2606.20246] Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
https://humanego-ai.github.io | HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos
https://arxiv.org/pdf/2605.24934 | HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos
https://developers.openai.com/codex/security | Security – Codex | OpenAI Developers
https://github.com/zetaodu/Research-Paper-Writing-Skills | zetaodu/Research-Paper-Writing-Skills: Skill package for ML/CV/NLP paper writing, curated and adapted from Prof. Peng Sida's open notes for Codex, Claude Code, and Gemini.
https://chatgpt.com | ChatGPT
https://arxiv.org/abs/2605.27284 | [2605.27284] FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action Policies
https://arxiv.org/abs/2001.07685 | [2001.07685] FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/google-research/fixmatch | google-research/fixmatch: A simple method to perform semi-supervised learning with limited data.
https://arxiv.org/pdf/2001.07685 | 2001.07685
https://www.google.com/search?q=MimicDreamer&sourceid=chrome&ie=UTF-8 | www.google.com
https://openreview.net/profile?id=%7EHaoyun_Li1 | Haoyun Li | OpenReview
https://openreview.net/forum?id=xCAum9gOkj | MimicDreamer: Aligning Human and Robot Demonstrations for Scalable VLA Training | OpenReview
https://hjfy.top | 幻觉翻译 - arXiv 及文档翻译
https://hjfy.top/arxiv/2509.22199 | MimicDreamer: Aligning Human and Robot Demonstrations for Scalable VLA Training
https://www.google.com/search?q=mimicDreamer&sca_esv=bcc915fd4b92abab&sxsrf=ANbL-n6sL8UDgxCX3slD-7R7ej5MFYKXpA%3A1781017021476&ei=vSkoavvaHKLl4-EPi_PkmQU&biw=2277&bih=1083&ved=0ahUKEwi7rZ23tfqUAxWi8jgGHYs5OVMQ4dUDCBA&uact=5&oq=mimicDreamer&gs_lp=Egxnd3Mtd2l6LXNlcnAiDG1pbWljRHJlYW1lcjIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzIKEAAYRxjWBBiwAzINEAAYgAQYigUYQxiwAzINEAAYgAQYigUYQxiwAzINEAAYgAQYigUYQxiwAzINEAAYgAQYigUYQxiwAzIOEAAY5AIY1gQYsAPYAQEyDhAAGOQCGNYEGLAD2AEBMg4QABjkAhjWBBiwA9gBATIXEC4Y3AYYuAYY2gYY2AIYyAMYsAPYAQEyFxAuGNgCGLgGGNoGGNwGGMgDGLAD2AEBMhcQLhjYAhi4BhjaBhjcBhjIAxiwA9gBATIXEC4Y2AIYuAYY2gYY3AYYyAMYsAPYAQFIwyFQ1SBY1SBwBHgBkAEAmAEAoAEAqgEAuAEDyAEA-AEBmAIEoAIMmAMA4gMFEgExIECIBgGQBhO6BgYIARABGAmSBwE0oAcAsgcAuAcAwgcFMC4zLjHIBwqACAE&sclient=gws-wiz-serp | www.google.com
https://fugumt.com/fugumt/paper_check/2509.22199v2_enmode | Related papers: MimicDreamer: Aligning Human and Robot Demonstrations for Scalable VLA Training
https://openreview.net/forum/de11ceea59ac7d006e767ff330f8ab6d12bea124.pdf | Forum | OpenReview
https://hjfy.top/arxiv/2605.27284 | FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action Policies
https://chatgpt.com/c/6a26ef9f-6d30-8321-8f9b-3dc1777c282c?mweb_fallback=1 | chatgpt.com
https://drive.google.com/drive/folders/1yZq0GV9Rd7hjS9FEuyy3PJKpGnWZlolU | analysis - Google 云端硬盘
https://research.nvidia.com/labs/eai/blogs/scaling-video-training-with-parallelism/ | Scaling Video Training with Parallelism
https://arxiv.org/abs/2506.01583 | [2506.01583] FreqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens
https://hjfy.top/arxiv/2506.08822 | FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency
https://gemini.google.com/app/e4005e94aea56792 | 频域流匹配在机器人控制中的应用 - Google Gemini
https://openreview.net/pdf?id=NKNryrCGYn | pdf
https://hjfy.top/arxiv/2605.01581v4 | Hyper-DP3: Frequency-Aware Right-Sizing of 3D Diffusion Policies for Visuomotor Control
https://gemini.google.com/app/13e3efc924182d68 | 机器人强化学习新视角 - Google Gemini
https://gemini.google.com/app/34b1dbcac9daff52 | Google Gemini
https://www.google.com/search?q=reqPolicy%3A+Frequency+Autoregressive+Visuomotor+Policy+with+Continuous+Tokens&sourceid=chrome&ie=UTF-8 | reqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens - Google Search
https://openreview.net/forum?id=eKFrXWb0sT¬eId=QfzK1HG574 | Frequency-Enhanced Data Augmentation for Vision-and-Language Navigation | OpenReview
https://arxiv.org/pdf/2512.01031 | VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference
https://hjfy.top/arxiv/2604.13001 | XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios
https://github.com/X-Square-Robot/XRZero-G0 | GitHub - X-Square-Robot/XRZero-G0: XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios · GitHub
https://huggingface.co/datasets/x-square-robot/XRZero-G0-3K/tree/main | x-square-robot/XRZero-G0-3K at main
https://www.google.com/search?q=self+forcing%E8%A7%A3%E8%AF%BB&sca_esv=4a455b0f3ad2390b&sxsrf=ANbL-n6FdBzvgtcVSVhFvbEXQWH3B9x1Gg%3A1781070268157&ei=vPkoaoGVCZya4-EPw67GiQM&biw=2277&bih=1083&ved=0ahUKEwiB45zl-_uUAxUczTgGHUOXMTEQ4dUDCBA&uact=5&oq=self+forcing%E8%A7%A3%E8%AF%BB&gs_lp=Egxnd3Mtd2l6LXNlcnAiEnNlbGYgZm9yY2luZ-ino-ivuzIFEAAYgAQyBRAAGO8FMgUQABjvBTIFEAAY7wVInw9Q-A1Y-A1wAXgBkAEAmAGFAaABhQGqAQMwLjG4AQPIAQD4AQGYAgKgAosBwgIKEAAYRxjWBBiwA8ICDRAAGIAEGIoFGEMYsAPCAhcQLhjcBhi4BhjaBhjYAhjIAxiwA9gBAZgDAIgGAZAGFLoGBggBEAEYGZIHAzEuMaAHxgGyBwMwLjG4B4cBwgcFMC4xLjHIBwSACAE&sclient=gws-wiz-serp | self forcing解读 - Google Search
https://github.com/thu-ml/Causal-Forcing | GitHub - thu-ml/Causal-Forcing：[ICML 2026] “Causal Forcing：用于高质量实时交互式视频生成的自回归扩散蒸馏”和 Causal Forcing++ 的官方代码库 · GitHub
https://gemini.google.com/app/67e523babc0d5947 | 视频扩散模型技术演进解读 - Google Gemini
https://gemini.google.com/app/fb0f506bcd2a2866 | FAST 架构中 DCT 技术详解 - Google Gemini
https://arxiv.org/pdf/2606.02274v2 | Dexterity-BEV: Aligning 3D World and Actions for Generalizable Robot Policies Learning
https://hjfy.top/arxiv/2606.02274v2 | Dexterity-BEV: Aligning 3D World and Actions for Generalizable Robot Policies Learning
https://cshizhe.github.io/index.html#publication | Shizhe Chen
https://hjfy.top/arxiv/2605.21414 | PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction
https://pointcept.github.io/Concerto/ | Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations
https://arxiv.org/pdf/2510.23607 | Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations
https://hjfy.top/arxiv/1806.07366 | Neural Ordinary Differential Equations
https://arxiv.org/html/2606.08242v1 | Light-WAM: Efficient World Action Models with State-Fusion Action Decoding
https://arxiv.org/abs/2605.27919 | [2605.27919] Frequency-Guided Action Diffusion via Sub-Frequency Manifold Traversal
https://x.com/search?q=research%20idea&src=typed_query | research idea - 搜索 / X
https://lilianweng.github.io/page/5/ | 小日志
https://gemini.google.com/app/722a627b4026df5a | Apple Vision Pro 手腕坐标系解析 - Google Gemini
https://docs.google.com/document/d/1b3P0aOPkgSBUuQF76xwyK_Cgg5tszxswOt3m01HvgfE/edit?tab=t.0 | VLA 跨实施形态动作表征与时空谱域解耦流匹配的完美融合架构 - Google 文档
https://gemini.google.com/app/fb9bc57451d35114 | PointACT 使用 Concerto 模型 - Google Gemini
https://x.com/home | (21) 主页 / X
https://blog.jxmo.io/p/zen-and-the-art-of-machine-learning | Zen and the Art of Machine Learning Research
https://tml.stanford.edu/cloak/ | Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA
https://www.google.com/search?q=Cloak%3A+Zero-Shot+Cross-Embodiment+Manipulation+by+Masking+the+End-Effector+from+the+VLA&sourceid=chrome&ie=UTF-8 | Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA - Google Search
https://openaccess.thecvf.com/content/CVPR2026W/GigaBrainChallenge/papers/Ge_CrossZero_Zero-Shot_of_Unseen_Embodiment_Manipulation_via_Object-Centric_Hierarchical_Diffusion_CVPRW_2026_paper.pdf | CrossZero: Zero-Shot of Unseen Embodiment Manipulation via Object-Centric Hierarchical Diffusion Models
https://hjfy.top | 幻觉翻译 - arXiv 及文档翻译
https://hjfy.top/arxiv/2606.22836 | Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA
https://www.doubao.com/chat/38432276931077378 | 开源问题 - 豆包
https://crs.ruinique.com/keys | Ruinique API - AI API Gateway
https://arxiv.org/pdf/2606.11087 | Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning
https://hjfy.top/arxiv/2606.11087 | Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning
https://github.com/starVLA/starVLA | GitHub - starVLA/starVLA: StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing · GitHub
https://gemini.google.com/app/cc8b43e10e0762d2 | 深度学习中的级联模型结构 - Google Gemini
https://www.google.com/search?q=factr2&oq=factr2&gs_lcrp=EgZjaHJvbWUyBggAEEUYOdIBCDE1ODFqMGo3qAIIsAIB&sourceid=chrome&ie=UTF-8 | factr2 - Google Search
https://x.com/robotsdigest/status/2067240473648136260 | (21) X 上的 Robots Digest 🤖：“LAGO Policy tackles two practical issues in diffusion-based robot control: jerky motion from asynchronous inference and unsafe behavior around unseen obstacles. The key idea is to combine latency-aware diffusion guidance with goal-directed trajectory planning, producing https://t.co/t3NH3OE4dr” / X
https://egoman-project.github.io | EgoMAN: Flowing from Reasoning to Motion
https://arxiv.org/pdf/2606.17982 | LAGO Policy: Latency-Aware Asynchronous Diffusion Policies with Goal-Directed Collision-Free Planning for Smooth Manipulation
https://interlatent.com/blog/interlatent-modern-ai-robotics-first-principles | An Overview of Modern AI Robotics from First Principles | Interlatent Blog
https://hjfy.top/arxiv/2606.17200 | ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining
https://docs.ikuncode.cc/deploy/claude-code | Claude Code Deployment Guide | IKunCode Docs
https://www.packyapi.com/console | PackyAPI
https://docs.packyapi.com/docs/ | Docs | PackyAPI 使用文档
https://www.runoob.com/claude-code/claude-code-install.html | Claude Code 安装与使用 | 菜鸟教程
https://x.com/robotsdigest/status/2067240473648136260 | (21) X 上的 Robots Digest 🤖：“LAGO Policy tackles two practical issues in diffusion-based robot control: jerky motion from asynchronous inference and unsafe behavior around unseen obstacles. The key idea is to combine latency-aware diffusion guidance with goal-directed trajectory planning, producing https://t.co/t3NH3OE4dr” / X
https://egoman-project.github.io | EgoMAN: Flowing from Reasoning to Motion
https://arxiv.org/pdf/2606.17982 | LAGO Policy: Latency-Aware Asynchronous Diffusion Policies with Goal-Directed Collision-Free Planning for Smooth Manipulation
https://interlatent.com/blog/interlatent-modern-ai-robotics-first-principles | An Overview of Modern AI Robotics from First Principles | Interlatent Blog
https://hjfy.top/arxiv/2606.17200 | ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining
https://arxiv.org/abs/2606.15631 | [2606.15631] Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time
https://www.google.com/search?q=pointact&oq=pointact&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIGCAEQRRg8MgYIAhBFGD0yBggDEEUYPdIBCDEwMDdqMGo3qAIIsAIB&sourceid=chrome&ie=UTF-8 | pointact - Google Search
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=11395153&tag=1 | IEEE Xplore Full-Text PDF:
https://arxiv.org/html/2605.21414v1#bib.bib36 | PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction
https://arxiv.org/pdf/2605.27919 | arxiv.org/pdf/2605.27919
https://arxiv.org/pdf/2506.01583 | arxiv.org/pdf/2506.01583
https://arxiv.org/pdf/2506.08822 | arxiv.org/pdf/2506.08822
https://arxiv.org/pdf/2512.07212 | arxiv.org/pdf/2512.07212
https://github.com/facebookresearch/flow_matching | facebookresearch/flow_matching: A PyTorch library for implementing flow matching algorithms, featuring continuous and discrete flow matching implementations. It includes practical examples for both text and image modalities.
https://gemini.google.com/app | Google Gemini
https://github.com/starVLA/starVLA/blob/starVLA_dev/docs/starVLA_guideline.md | starVLA/docs/starVLA_guideline.md at starVLA_dev · starVLA/starVLA
https://github.com/xpeng-robotics/UniT/issues/3 | 关于 eef 动作格式！· 第 3 期 · xpeng-robotics/UniT
http://127.0.0.1:18789/chat?session=main | OpenClaw Control
https://hjfy.top | 幻觉翻译 - arXiv 及文档翻译
https://hjfy.top/arxiv/2606.14048 | WAM4D: Fast 4D World Action Model via Spatial Register Tokens
https://www.google.com/search?q=DA3-GIANT-1.1&sourceid=chrome&ie=UTF-8 | DA3-GIANT-1.1 - Google Search
https://huggingface.co/depth-anything/DA3-GIANT-1.1 | depth-anything/DA3-GIANT-1.1 · 拥抱脸
https://arxiv.org/pdf/2606.14048 | WAM4D: Fast 4D World Action Model via Spatial Register Tokens
https://hjfy.top/arxiv/2605.21414 | PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction
https://www.google.com/search?q=PointWorld&oq=PointWorld&gs_lcrp=EgZjaHJvbWUyBggAEEUYOdIBBzU2N2owajeoAgiwAgE&sourceid=chrome&ie=UTF-8 | PointWorld - Google Search
https://hjfy.top/arxiv/2601.03782 | PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation
https://gemini.google.com/app/e2143489a6c74f64 | FGO: 频域动作生成详解 - Google Gemini
https://www.google.com/search?q=Rectified+Flow%E6%98%AF%E4%BB%80%E4%B9%88&sca_esv=2a9cf1d07f23bfb1&sxsrf=ANbL-n7drAhlaeIzU39LlOxAJzdDSo8Jmw%3A1781167064265&ei=2HMqavrID5qt4-EPpZrXsAc&biw=2277&bih=1083&ved=0ahUKEwj6lJqx5P6UAxWa1jgGHSXNFXYQ4dUDCBA&uact=5&oq=Rectified+Flow%E6%98%AF%E4%BB%80%E4%B9%88&gs_lp=Egxnd3Mtd2l6LXNlcnAiF1JlY3RpZmllZCBGbG935piv5LuA5LmIMgoQABhHGNYEGLADMgoQABhHGNYEGLADMgoQABhHGNYEGLADMgoQABhHGNYEGLADMgoQABhHGNYEGLADMgoQABhHGNYEGLADMgoQABhHGNYEGLADMgoQABhHGNYEGLADMg0QABiABBiKBRhDGLADMg0QABiABBiKBRhDGLADMg0QABiABBiKBRhDGLADMg0QABiABBiKBRhDGLADMhcQLhjcBhi4BhjaBhjYAhjIAxiwA9gBATIXEC4Y3AYYuAYY2gYY2AIYyAMYsAPYAQEyFxAuGNgCGLgGGNoGGNwGGMgDGLAD2AEBMhcQLhjcBhi4BhjaBhjYAhjIAxiwA9gBATIXEC4Y3AYYuAYY2gYY2AIYyAMYsAPYAQFI1SJQjiJYjiJwAXgBkAEAmAEAoAEAqgEAuAEDyAEA-AEBmAIBoAIDmAMAiAYBkAYRugYGCAEQARgZkgcBMaAHALIHALgHAMIHAzItMcgHA4AIAQ&sclient=gws-wiz-serp | Rectified Flow是什么 - Google Search
https://zhuanlan.zhihu.com/p/15125790338 | Rectified Flow 原理简介与示例代码解读 - 知乎
https://x.com/TongMutianTMT/status/2064755905539998131 | (21) X 上的 Mutian Tong：“🚀 Excited to share PointAction: A new Video-Point-Action Model that uses dynamic 3D pointmaps as a universal, geometry-grounded action representation for robot control. VLA → VAM → ? Lift RGB to RGB+XYZ, then decode robot-specific actions. https://t.co/z6sB8aUQaS [1/6] https://t.co/skSwfbqjgA” / X
https://arxiv.org/pdf/2606.03943 | PointAction: 3D Points as Universal Action Representations for Robot Control
https://chatgpt.com/c/6a2da272-5b2c-8323-b643-89176e378e41 | 人机数据时序对齐分析
https://gemini.google.com/app | Google Gemini
https://x.com/home | (21) 主页 / X
https://arxiv.org/pdf/2509.02055 | Align-Then-stEer: Adapting the Vision-Language Action Models through Unified Latent Guidance
https://github.com/TeleHuman/Align-Then-Steer/tree/main | TeleHuman/Align-Then-Steer: Official Implementation of "Align-Then-stEer: Adapting the Vision-Language Action Models through Unified Latent Guidance".
https://cshizhe.github.io/projects/pointact.html | PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction
https://www.google.com/search?q=RTC&oq=RTC&gs_lcrp=EgZjaHJvbWUyBggAEEUYOdIBBzMyN2owajeoAgiwAgE&sourceid=chrome&ie=UTF-8 | RTC - Google Search
https://x.com/physical_int/status/1932113400575963162 | (21) RTC - 搜索 / X
https://www.pi.website | Physical Intelligence (π)
https://hjfy.top | 幻觉翻译 - arXiv 及文档翻译
https://hjfy.top/arxiv/2602.12978 | Learning Native Continuation for Action Chunking Flow Policies
https://lyfeng001.github.io/Legato/ | Legato：学习动作分块流程策略的原生延续
https://github.com/lyfeng001/Legato-kinetix | lyfeng001/Legato-kinetix：Legato 的 Kinetix 模拟：学习动作分块流策略的原生延续（RSS 2026）
https://github.com/Physical-Intelligence/real-time-chunking-kinetix | Physical-Intelligence/real-time-chunking-kinetix: Simulated experiments for "Real-Time Execution of Action Chunking Flow Policies".
https://arxiv.org/pdf/2504.15517 | 2504.15517
https://github.com/MingC715/FSAIL | MingC715/FSAIL: Code of FSAIL
https://x.com/HuggingPapers/status/2066389313827262802 | (21) X 上的 DailyPapers：“Tencent just released a massive bimanual robot manipulation dataset on Hugging Face 2,163 hours of high-fidelity demonstrations 250K+ episodes across 70+ tasks Multi-view egocentric teleoperation data Compatible with LeRobot v3.0 https://t.co/Tgm0AF4t8G” / X
https://flow-reversal-steering.github.io | Improving Robotic Generalist Policies via Flow Reversal Steering
https://arxiv.org/pdf/2604.02028 | Why Gaussian Diffusion Models Fail on Discrete Data and How to Prevent It?
https://huggingface.co/datasets/tencent/Hy-Embodied-0.5-VLA-Data | 腾讯/Hy-Embodied-0.5-VLA-Data · Hugging Face 的数据集
https://arxiv.org/pdf/2606.14409 | Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack
https://github.com/Tencent-Hunyuan/Hy-Embodied-0.5-VLA | Tencent-Hunyuan/Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack
https://hjfy.top | 幻觉翻译 - arXiv 及文档翻译
https://hjfy.top/arxiv/2606.14409 | Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack
https://www.google.com/search?q=robot+UMI+buy&sca_esv=69714617a512e5ac&biw=2245&bih=1041&sxsrf=ANbL-n7IGk6O7YX5jtbjh-jMpYD1BhcCPQ%3A1781514581263&ei=VcEvaujND73F4-EPh7jS8Q0&ved=0ahUKEwiozJz-8oiVAxW94jgGHQecNN4Q4dUDCBI&uact=5&oq=robot+UMI+buy&gs_lp=Egxnd3Mtd2l6LXNlcnAiDXJvYm90IFVNSSBidXkyChAAGEcY1gQYsAMyChAAGEcY1gQYsAMyChAAGEcY1gQYsAMyChAAGEcY1gQYsAMyChAAGEcY1gQYsAMyChAAGEcY1gQYsAMyChAAGEcY1gQYsAMyChAAGEcY1gQYsANIlRtQrxlYlhpwAXgBkAEAmAHBAaABwQGqAQMwLjG4AQPIAQD4AQGYAgGgAgGYAwCIBgGQBgiSBwExoAf2AbIHALgHAMIHAzAuMcgHAYAIAQ&sclient=gws-wiz-serp | robot UMI buy - Google Search
https://github.com/real-stanford/universal_manipulation_interface | real-stanford/universal_manipulation_interface: Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots
https://hjfy.top/arxiv/2509.02055 | Align-Then-stEer: Adapting the Vision-Language Action Models through Unified Latent Guidance
https://www.cnblogs.com/duzetao/p/19730124 | h2r - 张达嘛 - 博客园
https://x.com/home | (21) 主页 / X
https://arxiv.org/abs/2605.25979 | [2605.25979] LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence
https://crs.ruinique.com/login | Login - Ruinique API
https://api.ikuncode.cc/console/token | IKunCode
https://docs.ikuncode.cc/zh/guide/model-selection | 模型选择 | IKunCode 使用文档
http://47.101.201.121:3000/console/token | LAB
https://arxiv.org/pdf/2606.14665 | EgoGuide: Egocentric Guidance for Efficient Robot-Free Demonstration Collection and Learning
https://gemini.google.com/app | Google Gemini
https://chatgpt.com/c/6a310294-6054-8321-ac2b-cd6eb34a2ec5 | ATE方法解读
https://x.com/w0nderfulof/status/2066624312602169459 | （21）X 上的 NAVI w0nderful🇺🇦：“抱歉各位” / X
https://www.cnblogs.com/duzetao/p/20242527 | 贝叶斯公式 - 张达嘛 - 博客园
https://github.com/starVLA/starVLA | GitHub - starVLA/starVLA: StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing · GitHub
https://github.com/starVLA/starVLA/blob/starVLA_dev/docs/starVLA_guideline.md | starVLA/docs/starVLA_guideline.md at starVLA_dev · starVLA/starVLA
https://x.com/home | (21) 主页 / X
file:///media/duzt/88B8CD0A191F37021/piper/unit_data/gr1_egodex_analysis/eefplus_opencv_fulltraj_smoke/trajectories_3d_opencv_fulltraj_smoke.html | GR1 vs EgoDex EEF+ 3D Trajectories
https://gemini.google.com/app | Google Gemini
https://github.com/portal-cornell/X-Diffusion | GitHub - portal-cornell/X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations · GitHub
https://gemini.google.com/app/e7d35ab63e8f9219 | 机器人模仿学习的ICRA论文解读 - Google Gemini
https://openreview.net/forum?id=DbBD2aT1OG | 统一的潜在导向和残差细化方法用于在线改进扩散策略模型 | OpenReview
https://www.google.com/search?q=Residual+Diffusion+Bridge+Model+for+Image+Restoration&sourceid=chrome&ie=UTF-8 | Residual Diffusion Bridge Model for Image Restoration - Google Search
https://www.google.com/search?q=PrismFlow%3A+Residual+Dynamics+for+Flow+Matching+in+Time-Series+Generation&sourceid=chrome&ie=UTF-8 | PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation - Google Search
https://gemini.google.com/app/82f794553399bed2 | PrismFlow: 时序生成深度解析 - Google Gemini
https://www.google.com/search?q=cotraining+lbm&sca_esv=6c41df8803dda356&sxsrf=ANbL-n5HFOU5yz13OY5uBoE-CqMyqm-8QA%3A1781010299756&ei=ew8oatXULd3V4-EPneq_kQY&biw=2277&bih=1083&ved=0ahUKEwiV_oeynPqUAxXd6jgGHR31L2IQ4dUDCBA&uact=5&oq=cotraining+lbm&gs_lp=Egxnd3Mtd2l6LXNlcnAiDmNvdHJhaW5pbmcgbGJtMgcQABiABBgNMgcQABiABBgNMgcQABiABBgNMgcQABiABBgNMgYQABgeGA0yBhAAGB4YDTIGEAAYHhgKSN7uAVD_3gFY7u0BcAJ4AZABAJgBswGgAdUDqgEDMC4zuAEDyAEA-AEBmAIFoALhA8ICChAAGEcY1gQYsAPCAhcQLhjcBhi4BhjaBhjYAhjIAxiwA9gBAcICCBAAGAcYHhgKwgIFEAAYgATCAggQABiABBiiBMICBRAAGO8FwgIIEAAYiQUYogSYAwDiAwUSATEgQIgGAZAGC7oGBAgBGBmSBwMyLjOgB9wNsgcDMC4zuAfbA8IHAzAuNcgHCIAIAQ&sclient=gws-wiz-serp | cotraining lbm - Google Search
https://arxiv.org/pdf/2602.01067v1 | A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation
https://hjfy.top | 幻觉翻译 - arXiv 及文档翻译
https://hjfy.top/arxiv/2602.01067v1 | A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation
https://gemini.google.com/app/0d68654be5f4d31d | 机器人大模型：数据与方法分析 - Google Gemini
https://arxiv.org/pdf/2606.05254 | Flash-WAM: Modality-Aware Distillation for World Action Models
https://flashwam.github.io | Flash-WAM: Modality-Aware Distillation for World Action Models
https://x.com/home | (21) 主页 / X
https://egopipaper.github.io/resources/ego_pi_2026.pdf | ego_pi_2026.pdf
https://arxiv.org/abs/2511.04671 | [2511.04671] X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations
https://chatgpt.com/codex/ | Codex | AI Assistant for Work and Code
https://openai.com/form/codex-app/ | Codex app sign up | OpenAI | OpenAI
https://gemini.google.com/app/e7d35ab63e8f9219 | 机器人模仿学习的ICRA论文解读 - Google Gemini
https://github.com/HKUSTDial/Supervisor-Skills | GitHub - HKUSTDial/Supervisor-Skills: 将博导十年科研经验炼化为可直接调用的 AI 技能。从 Idea 构思到论文投稿，你的 AI 科研副导师。 · GitHub
https://github.com/xpeng-robotics/UniT/ | GitHub - xpeng-robotics/UniT · GitHub
file:///media/duzt/88B8CD0A191F37021/piper/unit_data/gr1_egodex_analysis/eefplus_layer1_layer2_sample/report.html | GR1 vs EgoDex EEF+ Layer 1/2 Analysis
https://hjfy.top | 幻觉翻译 - arXiv 及文档翻译
https://hjfy.top/arxiv/2603.29844 | DIAL: Decoupling Intent and Action via Latent World Modeling for End-to-End VLA
https://github.com/xpeng-robotics/DIAL | GitHub - xpeng-robotics/DIAL · GitHub
https://arxiv.org/abs/2603.29844 | [2603.29844] DIAL: Decoupling Intent and Action via Latent World Modeling for End-to-End VLA
https://github.com/AlbertHuyb?tab=repositories | AlbertHuyb / Repositories
https://openreview.net/profile?id=%7EYi_Chen9 | Yi Chen | OpenReview
https://www.alphaxiv.org/abs/2511.16886 | Latent Reasoning in TRMs is Secretly a Policy Improvement Operator | alphaXiv
https://arxiv.org/pdf/2606.17054 | Human Universal Grasping
https://arxiv.org/pdf/2604.19734 | UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling
https://arxiv.org/pdf/2602.12978 | Learning Native Continuation for Action Chunking Flow Policies
https://x.com/furongh | (21) Furong Huang (@furongh) / X
https://xpeng-robotics.github.io/unit/ | UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling
https://github.com/xpeng-robotics/UniT | About eef action format! · Issue #3 · xpeng-robotics/UniT
https://github.com/xpeng-robotics/UniT/blob/main/examples/environment_setup.sh | UniT/examples/environment_setup.sh at main · xpeng-robotics/UniT
https://gemini.google.com/app | Google Gemini
https://www.google.com/search?q=robot+few+shot&oq=robot+few+shot&gs_lcrp=EgZjaHJvbWUyBggAEEUYOdIBCDMxNTFqMGo3qAIIsAIB&sourceid=chrome&ie=UTF-8 | robot few shot - Google Search
https://roboticsproceedings.org/rss19/p011.pdf | p011.pdf
https://colab.research.google.com/#scrollTo=rSC7tsioxDdX | Welcome To Colab - Colab
https://arxiv.org | arXiv.org e-Print archive
https://github.com/RainbowNebula/robot-paper-daily/blob/main/paper_daily.py | robot-paper-daily/paper_daily.py at main · RainbowNebula/robot-paper-daily · GitHub
https://context7.com | Context7 - Up-to-date documentation for LLMs and AI code editors
https://gemini.google.com/app | Google Gemini
https://docs.google.com/document/d/1yCVddjSDm7W49Siu5iT_K5VGLN3OnJWEOVG9XUoMADk/edit?tab=t.0 | Spatio-Temporal Spectral-Decoupled Flow Matching for VLA (V3 Full Mathematical Derivation) - Google 文档
https://crs.ruinique.com/keys | Ruinique API - AI API Gateway
https://docs.ikuncode.cc | IKunCode Docs
https://arxiv.org/pdf/2510.10125 | Ctrl-World: A Controllable Generative World Model for Robot Manipulation
https://hjfy.top | 幻觉翻译 - arXiv 及文档翻译
https://hjfy.top/arxiv/2510.10125 | Ctrl-World: A Controllable Generative World Model for Robot Manipulation
https://xpeng-robotics.github.io/fe0/ | Inside Fe₀: What Cross-Embodiment Data Teaches an Embodied Foundation Model
https://hjfy.top/arxiv/2511.04812 | 幻觉翻译 - arXiv 及文档翻译
https://github.com/starVLA/starVLA | GitHub - starVLA/starVLA: StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing · GitHub
https://hjfy.top/arxiv/2606.12396 | 幻觉翻译 - arXiv 及文档翻译
https://hjfy.top/arxiv/2604.00813 | DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale
https://arxiv.org/pdf/2606.13102 | arxiv.org/pdf/2606.13102
https://github.com/xpeng-robotics/UniT | About eef action format! · Issue #3 · xpeng-robotics/UniT
https://www.genrobot.ai/data/open-dataset | 10Kh RealOmni-Open DataSet | GenRobot Open Data - Genrobot AI Platform for Robotics
https://zgc-embodyai.github.io/PhysBrain/ | PhysBrain: Human Egocentric Data as a Bridge from Vision Language Models to Physical Intelligence
https://github.com/ZGC-EmbodyAI/LangForce | ZGC-EmbodyAI/LangForce: [ICML 2026] This repo is the official implementation of "LangForce : Bayesian Decomposition of Vision Language Action Models via Latent Action Queries"
https://www.alphaxiv.org/abs/2601.15197 | LangForce: Bayesian Decomposition of Vision Language Action Models via Latent Action Queries | alphaXiv
https://x.com/home | (21) 主页 / X
https://cshizhe.github.io | Shizhe Chen
https://cshizhe.github.io/projects/pointact.html | PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction
https://shiyao-li.github.io | Shiyao Li
https://cshizhe.github.io/projects/hdr.html | HDR Defense | Shizhe Chen
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