<video src=\"https://cdn-uploads.huggingface.co/production/uploads/60f1abe7544c2adfd699860c/Cn9DWMibNRPtMkkUUvmhg.mp4\" controls=\"\" class=\"max-w-full!\"></video></p>","updatedAt":"2026-05-29T16:22:43.734Z","author":{"_id":"60f1abe7544c2adfd699860c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg","fullname":"AK","name":"akhaliq","type":"user","isPro":false,"isHf":true,"isHfAdmin":false,"isMod":false,"followerCount":9587,"isUserFollowing":false,"primaryOrg":{"avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1583856921041-5dd96eb166059660ed1ee413.png","fullname":"Hugging Face","name":"huggingface","type":"org","isHf":true,"details":"The AI community building the future.","plan":"team"}}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.5007597804069519},"editors":["akhaliq"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg"],"reactions":[],"isReport":false}},{"id":"6a1a410aa233d2ba7da34228","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":359,"isUserFollowing":false},"createdAt":"2026-05-30T01:44:42.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [LIDEA: Human-to-Robot Imitation Learning via Implicit Feature Distillation and Explicit Geometry Alignment](https://huggingface.co/papers/2604.10677) (2026)\n* [Mitigating State Aliasing in Vision-Language-Action Models via Inverse Dynamics Learning](https://huggingface.co/papers/2605.29577) (2026)\n* [UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling](https://huggingface.co/papers/2604.19734) (2026)\n* [TriRelVLA: Triadic Relational Structure for Generalizable Embodied Manipulation](https://huggingface.co/papers/2605.05714) (2026)\n* [OASIS: Observation-Action Space Alignment via SE(3) Trajectory Prediction for Robotic Manipulation](https://huggingface.co/papers/2605.25829) (2026)\n* [GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization](https://huggingface.co/papers/2605.12369) (2026)\n* [BridgeACT: Bridging Human Demonstrations to Robot Actions via Unified Tool-Target Affordances](https://huggingface.co/papers/2604.23249) (2026)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"<p>This is an automated message from the <a href=\"https://huggingface.co/librarian-bots\">Librarian Bot</a>. I found the following papers similar to this paper. </p>\n<p>The following papers were recommended by the Semantic Scholar API </p>\n<ul>\n<li><a href=\"https://huggingface.co/papers/2604.10677\">LIDEA: Human-to-Robot Imitation Learning via Implicit Feature Distillation and Explicit Geometry Alignment</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.29577\">Mitigating State Aliasing in Vision-Language-Action Models via Inverse Dynamics Learning</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.19734\">UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.05714\">TriRelVLA: Triadic Relational Structure for Generalizable Embodied Manipulation</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.25829\">OASIS: Observation-Action Space Alignment via SE(3) Trajectory Prediction for Robotic Manipulation</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.12369\">GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.23249\">BridgeACT: Bridging Human Demonstrations to Robot Actions via Unified Tool-Target Affordances</a> (2026)</li>\n</ul>\n<p> Please give a thumbs up to this comment if you found it helpful!</p>\n<p> If you want recommendations for any Paper on Hugging Face checkout <a href=\"https://huggingface.co/spaces/librarian-bots/recommend_similar_papers\">this</a> Space</p>\n<p> You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: <code><span class=\"SVELTE_PARTIAL_HYDRATER contents\" data-target=\"UserMention\" data-props=\"{&quot;user&quot;:&quot;librarian-bot&quot;}\"><span class=\"inline-block\"><span class=\"contents\"><a href=\"/librarian-bot\">@<span class=\"underline\">librarian-bot</span></a></span> </span></span> recommend</code></p>\n","updatedAt":"2026-05-30T01:44:42.702Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":359,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.6946402192115784},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.30350","authors":[{"_id":"6a19bd20808ddbc3c7d42dc2","name":"Jusuk Lee","hidden":false},{"_id":"6a19bd20808ddbc3c7d42dc3","name":"Seungjae Lee","hidden":false},{"_id":"6a19bd20808ddbc3c7d42dc4","name":"Jonghun Shin","hidden":false},{"_id":"6a19bd20808ddbc3c7d42dc5","name":"Hoseong Jung","hidden":false},{"_id":"6a19bd20808ddbc3c7d42dc6","name":"Sungha Kim","hidden":false},{"_id":"6a19bd20808ddbc3c7d42dc7","name":"Daesol Cho","hidden":false},{"_id":"6a19bd20808ddbc3c7d42dc8","name":"H. Jin Kim","hidden":false},{"_id":"6a19bd20808ddbc3c7d42dc9","name":"Jia-Bin Huang","hidden":false},{"_id":"6a19bd20808ddbc3c7d42dca","name":"Furong Huang","hidden":false}],"publishedAt":"2026-05-28T00:00:00.000Z","submittedOnDailyAt":"2026-05-29T00:00:00.000Z","title":"DynaFLIP: Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation","submittedOnDailyBy":{"_id":"60f1abe7544c2adfd699860c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg","isPro":false,"fullname":"AK","user":"akhaliq","type":"user","name":"akhaliq"},"summary":"Robot manipulation critically depends on perception that preserves the action-relevant aspects of a scene. Yet most robot learning pipelines are built upon visual encoders pre-trained for static recognition or vision-language alignment, leaving motion understanding to downstream policies. We introduce DynaFLIP, a dynamics-aware multimodal pre-training framework that pushes motion understanding upstream into perception. We construct image-language-3D flow triplets from heterogeneous human and robot videos, and use these triplets as training-time supervision to shape an image-only encoder. Our key idea is to encourage the three modalities to span a small simplex volume in the shared hyperspherical space -- a smaller simplex volume indicating stronger alignment. To avoid the geometric ambiguity and trivial collapse of naive volume minimization, we combine simplex-volume minimization with a cosine regularizer and a contrastive objective. Our analyses show that DynaFLIP focuses on control-relevant regions critical for manipulation. The resulting dynamics-aware representations serve as reusable visual backbones and consistently outperform baselines across diverse downstream policies, including VLAs. We validate this across diverse simulation and real-world setups, with gains reaching +22.5% under out-of-distribution scenarios. Our results suggest that robot generalization improves when visual representations are trained to encode not just what is present, but how the world changes under action.","upvotes":5,"discussionId":"6a19bd20808ddbc3c7d42dcb","ai_summary":"DynaFLIP is a dynamics-aware multimodal pre-training framework that enhances robot manipulation by integrating motion understanding into visual perception through image-language-3D flow triplets and geometric regularization techniques.","ai_keywords":["dynamics-aware multimodal pre-training","visual encoders","image-language-3D flow triplets","shared hyperspherical space","simplex volume","cosine regularizer","contrastive objective","control-relevant regions","visual backbones","variational language-action models"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"64cbc3e2a257a3212c00a115","avatarUrl":"/avatars/836e61be4aeda2080ddf2db9f2626cc6.svg","isPro":false,"fullname":"Furong Huang Lab at UMD","user":"furongh-lab","type":"user"},{"_id":"638f26bb3783be5e1d04a86b","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/638f26bb3783be5e1d04a86b/iLDzwTKPAQcZJv7s6ZLcp.jpeg","isPro":false,"fullname":"Sy-Tuyen Ho","user":"hosytuyen","type":"user"},{"_id":"67d553ca188cb393f7bb4cbc","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/5P9fCuIBwGQGlHTGoCgZq.png","isPro":false,"fullname":"Jusuk Lee","user":"jlee-larr","type":"user"},{"_id":"68cb936bbe60dd93cf5b7a0b","avatarUrl":"/avatars/35c5006e3e87aceb89eb13ff18b15fc8.svg","isPro":false,"fullname":"Jonghun Shin","user":"jhshin00","type":"user"},{"_id":"648961d150c003881f1a10c3","avatarUrl":"/avatars/1eb3784c39f7ced2e952d11a410933ae.svg","isPro":false,"fullname":"Harshita Sharma","user":"hdsharma","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2605/2605.30350.md"}">

Get this paper in your agent:

Don't have the latest CLI?

Cite arxiv.org/abs/2605.30350 in a dataset README.md to link it from this page.

Cite arxiv.org/abs/2605.30350 in a Space README.md to link it from this page.