DreamPolicy integrates an autoregressive diffusion world model with policy learning to produce a single scalable policy that generalizes to unseen composite terrains for humanoid locomotion.
Vb-com: Learning vision-blind composite humanoid locomotion against deficient perception
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GuideWalk unifies traversability-aware navigation and terrain-adaptive locomotion into a single policy for humanoid robots via teacher distillation and RL refinement.
TA-WBC is a terrain-aware RL policy for legged loco-manipulation using exteroception, contact-plane sampling, and distillation to improve reachable space, tracking, and stability across terrains.
A two-stage distillation plus reinforced fine-tuning approach produces a single humanoid locomotion controller that adapts across skills and irregular terrains.
A one-shot adaptation technique for humanoid whole-body motion that computes order-preserving optimal transport distances between walking and target sequences, interpolates geodesic intermediate poses, optimizes for collision-free retargeting, and adapts via reinforcement learning.
citing papers explorer
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DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion
DreamPolicy integrates an autoregressive diffusion world model with policy learning to produce a single scalable policy that generalizes to unseen composite terrains for humanoid locomotion.
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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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Learning Terrain-Aware Whole-Body Control for Perceptive Legged Loco-Manipulation
TA-WBC is a terrain-aware RL policy for legged loco-manipulation using exteroception, contact-plane sampling, and distillation to improve reachable space, tracking, and stability across terrains.
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Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning
A two-stage distillation plus reinforced fine-tuning approach produces a single humanoid locomotion controller that adapts across skills and irregular terrains.
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One-shot Adaptation of Humanoid Whole-body Motion with Walking Priors
A one-shot adaptation technique for humanoid whole-body motion that computes order-preserving optimal transport distances between walking and target sequences, interpolates geodesic intermediate poses, optimizes for collision-free retargeting, and adapts via reinforcement learning.