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 15 Oct 2026] Title:FCTS: Failure-Guided Curriculum Tree Search Over Generated Terrain for Legged Parkour View a PDF of the paper titled FCTS: Failure-Guided Curriculum Tree Search Over Generated Terrain for Legged Parkour, by Narayanan Palghat Parameswaran (1) and 7 other authors View PDF HTML (experimental) Abstract:Quadruped parkour policies handle canonical obstacles well, but their training curricula often miss the irregular, compound geometry of industrial and disaster-response sites, where agile locomotion matters most. Such environments pose a hard generalization problem: even training directly on reconstructed geometry from the target site does not reliably yield policies that traverse it. Hand-designed curricula span only a few terrain families, and recent LLM-generated curricula offer little control over which policy weaknesses they target. We propose FCTS, a failure-conditioned terrain curriculum framework that uses localized traversal failures to automatically generate the terrains a policy should train on next. Each failure's location, termination mode, robot state, and surrounding terrain geometry condition this generation, and Monte Carlo Tree Search explores alternative curriculum trajectories under a fixed training budget. On terrains built from scanned real-world construction sites, FCTS raises success rate from 28.4% to 85.8% on a medium-difficulty course and from 14.9% to 44.7% on a hard course relative to the strongest parkour or LLM-curriculum baselines, and maintains higher progress on extreme terrains where full traversal is rare. Hardware experiments on a quadruped robot confirm that the learned FCTS policies transfer to the real world. Comments: 9 pages, 5 figures, 4 tables. Under review. Project page with videos: this https URL Subjects: Robotics (cs.RO) ACM classes: I.2.9; I.2.6; I.2.8; I.2.7 Cite as: arXiv:2610.22619 [cs.RO] (or arXiv:2610.22619v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2610.22619 arXiv-issued DOI via DataCite (pending registration) Submission history From: Harmish Khambhaita [view email] [v1] Thu, 15 Oct 2026 20:22:39 UTC (3,787 KB) Full-text links: Access Paper: View a PDF of the paper titled FCTS: Failure-Guided Curriculum Tree Search Over Generated Terrain for Legged Parkour, by Narayanan Palghat Parameswaran (1) and 7 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.RO new | recent | 2026-10 Change to browse by: cs References & Citations NASA ADS 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?) 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?)