HumanoidArena is a new benchmark of 7 leg-critical HOI/HSI tasks that evaluates egocentric hierarchical whole-body policies in humanoids and finds performance is strongly conditioned on the low-level GMT used.
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Hiking in the wild: A scalable perceptive parkour framework for humanoids
14 Pith papers cite this work. Polarity classification is still indexing.
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SceneBot conditions a humanoid tracking policy on motion references and contact labels, using reconstructed scene-interaction data to unify free-space locomotion with contact-rich manipulation and terrain tasks.
A three-stage RL framework with cross-attention, spatial memory, and depth-noise augmentation lets a Unitree Go2 climb 55° hollow stairs zero-shot from simulation.
Perceptive BFM grounds human motion priors in robot terrain perception via terrain-conformal reference synthesis and teacher-student transfer from adapted to raw-reference tracking.
TAGA learns terrain-aware active gaze behaviors for humanoid robots via RL alone, enabling generalizable locomotion with 1.2m real-world gap traversal.
A modular system uses motion matching to compose long-horizon human skill chains, trains RL experts, and distills them into a depth-based policy that lets a Unitree G1 humanoid autonomously climb, vault, and roll over obstacles up to 1.25 m tall.
GuideWalk unifies traversability-aware navigation and terrain-adaptive locomotion into a single policy for humanoid robots via teacher distillation and RL refinement.
MARCH combines simplified-model trajectory generation with CLF-guided teacher RL and vision-policy distillation to enable stable humanoid locomotion over sparse terrain with better sample efficiency than pure model-free methods.
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.
A hybrid motion-tracking and imitation-reinforcement pipeline produces a depth-based visuomotor policy that lets humanoids climb varied ladders zero-shot on hardware and perform teleoperated manipulation while climbing.
CoRe-MoE uses a two-stage RL framework with contrastive reweighting in a Mixture-of-Experts architecture to enable gait transitions and multi-terrain adaptation for humanoid locomotion.
SSR is an end-to-end vision-based framework for humanoid traversal that learns imagined foothold guidance, equivariant latent-space symmetry augmentation, and terrain-specific multi-discriminator motion priors to enable safe locomotion on diverse real-world terrains.
ParkourFormer achieves 93.85% average success on multi-terrain humanoid parkour by fusing Transformer sequence modeling with supervised future-state prediction.
citing papers explorer
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HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning
HumanoidArena is a new benchmark of 7 leg-critical HOI/HSI tasks that evaluates egocentric hierarchical whole-body policies in humanoids and finds performance is strongly conditioned on the low-level GMT used.
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SceneBot: Contact-Prompted General Humanoid Whole Body Tracking with Scene-Interaction
SceneBot conditions a humanoid tracking policy on motion references and contact labels, using reconstructed scene-interaction data to unify free-space locomotion with contact-rich manipulation and terrain tasks.
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StairMaster: Learning to Conquer Risky Hollow Stairs for Agile Quadrupedal Robots
A three-stage RL framework with cross-attention, spatial memory, and depth-noise augmentation lets a Unitree Go2 climb 55° hollow stairs zero-shot from simulation.
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Perceptive Behavior Foundation Model: Adapting Human Motion Priors to Robot-Centric Terrain
Perceptive BFM grounds human motion priors in robot terrain perception via terrain-conformal reference synthesis and teacher-student transfer from adapted to raw-reference tracking.
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TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion
TAGA learns terrain-aware active gaze behaviors for humanoid robots via RL alone, enabling generalizable locomotion with 1.2m real-world gap traversal.
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Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching
A modular system uses motion matching to compose long-horizon human skill chains, trains RL experts, and distills them into a depth-based policy that lets a Unitree G1 humanoid autonomously climb, vault, and roll over obstacles up to 1.25 m tall.
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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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MARCH: Model-Assisted Reinforcement Learning for the Perceptive Control of Humanoids over Sparse Footholds
MARCH combines simplified-model trajectory generation with CLF-guided teacher RL and vision-policy distillation to enable stable humanoid locomotion over sparse terrain with better sample efficiency than pure model-free methods.
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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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LadderMan: Learning Humanoid Perceptive Ladder Climbing
A hybrid motion-tracking and imitation-reinforcement pipeline produces a depth-based visuomotor policy that lets humanoids climb varied ladders zero-shot on hardware and perform teleoperated manipulation while climbing.
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CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation
CoRe-MoE uses a two-stage RL framework with contrastive reweighting in a Mixture-of-Experts architecture to enable gait transitions and multi-terrain adaptation for humanoid locomotion.
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SSR: Scaling Surefooted and Symmetric Humanoid Traversal to the Open World
SSR is an end-to-end vision-based framework for humanoid traversal that learns imagined foothold guidance, equivariant latent-space symmetry augmentation, and terrain-specific multi-discriminator motion priors to enable safe locomotion on diverse real-world terrains.
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ParkourFormer: Integrating Predictive Supervision and Sequence Modeling into Parkour Locomotion
ParkourFormer achieves 93.85% average success on multi-terrain humanoid parkour by fusing Transformer sequence modeling with supervised future-state prediction.
- Global-Local Attention Decomposition for Terrain Encoding in Humanoid Perceptive Locomotion