[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
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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)
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