GuideWalk unifies traversability-aware navigation and terrain-adaptive locomotion into a single policy for humanoid robots via teacher distillation and RL refinement.
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Pith papers citing it
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cs.RO 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
T-GMP learns a terrain-conditioned latent motion manifold via CVAE from demonstrations and integrates it into an adversarial pipeline with a foothold penalty for versatile, natural humanoid locomotion.
Proposes a Logistic-Exponential precursor model coupled with trust calibration (trust as inverse accident probability) for dynamic risk perception in humanoid robots, supported by analysis of 126 events and a simulation case study.
citing papers explorer
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GuideWalk: Learning Unified Autonomous Navigation and Locomotion for Humanoid Robots across Versatile Terrains
GuideWalk unifies traversability-aware navigation and terrain-adaptive locomotion into a single policy for humanoid robots via teacher distillation and RL refinement.
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T-GMP: Terrain-conditioned Generative Motion Priors for Versatile and Natural Humanoid Locomotion
T-GMP learns a terrain-conditioned latent motion manifold via CVAE from demonstrations and integrates it into an adversarial pipeline with a foothold penalty for versatile, natural humanoid locomotion.
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Toward Machine Risk Perception: Integrating Trust Calibration and Precursor-Based Risk Estimation for Humanoid
Proposes a Logistic-Exponential precursor model coupled with trust calibration (trust as inverse accident probability) for dynamic risk perception in humanoid robots, supported by analysis of 126 events and a simulation case study.