PoLAR factorizes latent robot actions into radial extent and directional mode, so latent actions can separately capture how far a transition moves and what mode of behavior it follows. This gives a more structured latent space for downstream policy learning, and improves performance across simulated benchmarks and real-robot manipulation tasks.</p>\n<p>Project page: <a href=\"https://joon-stack.github.io/PoLAR/\" rel=\"nofollow\">https://joon-stack.github.io/PoLAR/</a><br>Code: <a href=\"https://github.com/joon-stack/PoLAR\" rel=\"nofollow\">https://github.com/joon-stack/PoLAR</a></p>\n","updatedAt":"2026-06-23T06:34:38.105Z","author":{"_id":"65404870deee4716f1bb6af6","avatarUrl":"/avatars/1d0de3762867474dd29ae350a0701002.svg","fullname":"Youngjoon Jeong","name":"quiet-storm","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":1,"isUserFollowing":false}},"numEdits":2,"identifiedLanguage":{"language":"en","probability":0.7664197087287903},"editors":["quiet-storm"],"editorAvatarUrls":["/avatars/1d0de3762867474dd29ae350a0701002.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.21139","authors":[{"_id":"6a3a074ffdcd3514343bb5e0","user":{"_id":"65404870deee4716f1bb6af6","avatarUrl":"/avatars/1d0de3762867474dd29ae350a0701002.svg","isPro":true,"fullname":"Youngjoon Jeong","user":"quiet-storm","type":"user","name":"quiet-storm"},"name":"Youngjoon Jeong","status":"claimed_verified","statusLastChangedAt":"2026-06-23T13:56:22.141Z","hidden":false},{"_id":"6a3a074ffdcd3514343bb5e1","name":"Jihwan Yu","hidden":false},{"_id":"6a3a074ffdcd3514343bb5e2","name":"Minsoo Jo","hidden":false},{"_id":"6a3a074ffdcd3514343bb5e3","name":"Junha Chun","hidden":false},{"_id":"6a3a074ffdcd3514343bb5e4","name":"Taesup Kim","hidden":false}],"publishedAt":"2026-06-19T00:00:00.000Z","submittedOnDailyAt":"2026-06-23T00:00:00.000Z","title":"PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning","submittedOnDailyBy":{"_id":"65404870deee4716f1bb6af6","avatarUrl":"/avatars/1d0de3762867474dd29ae350a0701002.svg","isPro":true,"fullname":"Youngjoon Jeong","user":"quiet-storm","type":"user","name":"quiet-storm"},"summary":"Latent action pretraining learns representations of visual change from pairs of observations, but existing methods typically encode each transition as a single unstructured representation that entangles transition extent and transition mode. We introduce Polar Latent Actions with Radial structure (PoLAR), which imposes a radial-direction structure on latent actions, encouraging radius to encode transition extent and direction to retain transition mode. PoLAR uses temporal offset between two observations as a weak proxy for transition extent, encouraging latent action from observation pairs separated by larger temporal gaps to occupy larger radii. We instantiate this structure in hyperbolic space, whose expanding volume with radius offers a natural fit for more diverse transition modes at larger extents. Across in-task and large-scale pretraining settings, PoLAR improves downstream policy performance in simulation and real-world robot experiments, outperforming latent action baselines and strong pretrained VLAs. These results suggest that the geometry of the latent action space is an important design choice for transferring visual pretraining to downstream robot policy learning.","upvotes":7,"discussionId":"6a3a0750fdcd3514343bb5e5","projectPage":"https://joon-stack.github.io/PoLAR","githubRepo":"https://github.com/joon-stack/PoLAR","githubRepoAddedBy":"user","ai_summary":"PoLAR introduces a geometrically structured latent action representation in hyperbolic space that separates transition extent from transition mode, improving robotic policy learning performance.","ai_keywords":["latent action pretraining","polar latent actions","radial structure","hyperbolic space","transition extent","transition mode","temporal offset","downstream policy performance","visual pretraining","robotic policy learning"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct","githubStars":1,"organization":{"_id":"66d54dc8033492801db2bf5a","name":"SeoulNatlUniv","fullname":"Seoul National University","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/659ccc9d18897eb6594e897f/_-0BM-1UyM-d-lRiahFnf.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"65404870deee4716f1bb6af6","avatarUrl":"/avatars/1d0de3762867474dd29ae350a0701002.svg","isPro":true,"fullname":"Youngjoon Jeong","user":"quiet-storm","type":"user"},{"_id":"6858c289b8646f42a8be81ec","avatarUrl":"/avatars/d6b7b2a78a10d40114b4468db43be6d8.svg","isPro":false,"fullname":"junhachun","user":"nikriz","type":"user"},{"_id":"655f856e18e09c839bbd51da","avatarUrl":"/avatars/30440b9ce8a796afe7324a1371ccd251.svg","isPro":false,"fullname":"Yujin Jo","user":"zoyzin","type":"user"},{"_id":"6735e23255a98f9a6e71dee2","avatarUrl":"/avatars/01b985c2a87634b83799c0fbae2116a3.svg","isPro":false,"fullname":"Hanseul","user":"k1seul","type":"user"},{"_id":"670e23c7afdfbae6e4da303a","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/B8QS0b17nMRGyW0GkOiPY.png","isPro":false,"fullname":"joonki min","user":"joon0822","type":"user"},{"_id":"631c386bc73939ffc0716a37","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1662793811119-noauth.jpeg","isPro":false,"fullname":"SeongWan Kim","user":"idgmatrix","type":"user"},{"_id":"6a2da6c8ca070ee12c6e396c","avatarUrl":"/avatars/0355287dcabaa67dbc7f0b10b87451f9.svg","isPro":false,"fullname":"Joe Mama","user":"JoeMama123123123","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"66d54dc8033492801db2bf5a","name":"SeoulNatlUniv","fullname":"Seoul National University","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/659ccc9d18897eb6594e897f/_-0BM-1UyM-d-lRiahFnf.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2606/2606.21139.md","query":{}}">
PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning
Abstract
PoLAR introduces a geometrically structured latent action representation in hyperbolic space that separates transition extent from transition mode, improving robotic policy learning performance.
Latent action pretraining learns representations of visual change from pairs of observations, but existing methods typically encode each transition as a single unstructured representation that entangles transition extent and transition mode. We introduce Polar Latent Actions with Radial structure (PoLAR), which imposes a radial-direction structure on latent actions, encouraging radius to encode transition extent and direction to retain transition mode. PoLAR uses temporal offset between two observations as a weak proxy for transition extent, encouraging latent action from observation pairs separated by larger temporal gaps to occupy larger radii. We instantiate this structure in hyperbolic space, whose expanding volume with radius offers a natural fit for more diverse transition modes at larger extents. Across in-task and large-scale pretraining settings, PoLAR improves downstream policy performance in simulation and real-world robot experiments, outperforming latent action baselines and strong pretrained VLAs. These results suggest that the geometry of the latent action space is an important design choice for transferring visual pretraining to downstream robot policy learning.
Community
PoLAR factorizes latent robot actions into radial extent and directional mode, so latent actions can separately capture how far a transition moves and what mode of behavior it follows. This gives a more structured latent space for downstream policy learning, and improves performance across simulated benchmarks and real-robot manipulation tasks.
Project page: https://joon-stack.github.io/PoLAR/
Code: https://github.com/joon-stack/PoLAR
Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.
Tap or paste here to upload images
Cite arxiv.org/abs/2606.21139 in a model README.md to link it from this page.
Cite arxiv.org/abs/2606.21139 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2606.21139 in a Space README.md to link it from this page.
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.