AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
[Submitted on 19 Oct 2026] Title:Latent Projection for Latent Planning View a PDF of the paper titled Latent Projection for Latent Planning, by Yoo Sung Jang and 1 other authors View PDF HTML (experimental) Abstract:Latent world models let an agent plan in latent space. The planner searches for an action sequence that minimizes a cost, and that cost is often the $\ell_2$ distance between the predicted terminal latent and the goal latent. The latent, however, is partly noise: the regularizer that prevents collapse during world model training promotes variance in the directions carrying no signal. We show that the same regularizer also supplies the structure to prune that noise in the latent. SIGReg drives the latent marginal to a standard Gaussian at every step, and assuming further that the latent sequence is a Gauss-Markov process, the transition is linear with its covariance pinned by the map itself, $z' \mid z \sim \mathcal{N}(Az,\, I - AA^\top)$. Fitting the state dynamics $A$ therefore yields the covariance at no additional cost, and in its eigenbasis each latent direction carries its own uncertainty. Furthermore, constraining $A$ to be a normal matrix fixes that basis across timesteps, so one eigenbasis measures the uncertainty over an arbitrary number of steps and serves as a stable basis to compute the planning cost on. We then restrict the $\ell_2$ cost to the $k$-dimensional subspace the state dynamics carry forward with the least uncertainty. The correction is post hoc: the encoder, the predictor, the rollout, and the planner are all left untouched, and only a single constrained matrix, $A$, is fit. We evaluate across six environments and show large gains on the 2D navigation tasks, TwoRoom and PointMaze, raising success rate over the plain $\ell_2$ cost by up to $+54.7$ points under a harder planning setting. Code is available at this https URL. Comments: Accepted to the CoRL 2026 Workshop on Modeling Uncertainty in Robotic World Models Subjects: Machine Learning (cs.LG); Robotics (cs.RO) Cite as: arXiv:2610.26979 [cs.LG] (or arXiv:2610.26979v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.26979 arXiv-issued DOI via DataCite Submission history From: Yoo Sung Jang [view email] [v1] Mon, 19 Oct 2026 01:51:14 UTC (797 KB) Full-text links: Access Paper: View a PDF of the paper titled Latent Projection for Latent Planning, by Yoo Sung Jang and 1 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.LG new | recent | 2026-10 Change to browse by: cs cs.RO References & Citations SciX Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)