SHOW is a mask-promptable framework coupling feed-forward scene reconstruction with human mesh recovery in a unified metric space to resolve scale ambiguity and improve human-scene alignment from monocular video.
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Physhsi: Towards a real-world generalizable and natural humanoid-scene interaction system
13 Pith papers cite this work. Polarity classification is still indexing.
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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.
BeyondMimic combines compact motion tracking with a unified guided latent diffusion model to master diverse agile behaviors from human demos and solve unseen downstream tasks via test-time classifier guidance.
An auto-regressive diffusion planner trained with scheduled prefix sampling, coupled asynchronously to a pretrained universal tracker, enables closed-loop humanoid whole-body control with replanning under disturbances and zero-shot moving-target reaching.
OmniContact introduces contact flow as a shared representation of body trajectories and contact signals to learn and chain loco-manipulation meta-skills, reporting 98.7% success on box carrying and 76.5% on push-stack tasks.
A humanoid robot can carry out diverse manipulation tasks in a zero-shot way by imitating one AI-generated video, using contact-aware trajectory optimization instead of task-specific policy training.
Imagine2Real enables zero-shot humanoid-object interaction by unifying motions as 4D point trajectories, tracking only base/hands/object keypoints inside a BFM latent space, and training with progressive simple rewards for mocap deployment.
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.
HAIC enables robust humanoid interactions with underactuated objects by predicting their dynamics from proprioceptive history and using a world model for adaptive control.
HUSKY combines humanoid-skateboard dynamics modeling with adversarial motion priors and physics-guided lean-to-steer strategies to achieve real-world stable skateboarding on a humanoid robot.
VAIC distills a teacher policy into a vision-and-proprioception student policy using recurrent adaptation and decoupled commands, enabling diverse real-robot tasks like box carrying and skateboarding that outperform baselines.
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.
SplitAdapter factorizes adaptation into load-aware and dynamics-aware encoders using split world-model objectives, GRL regularization, and hierarchical FiLM, reporting higher full-task success than baselines across 2-6 kg masses and 0-60 cm heights.
citing papers explorer
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Scene and Human in One World: Reconstruction in a Feedforward Pass
SHOW is a mask-promptable framework coupling feed-forward scene reconstruction with human mesh recovery in a unified metric space to resolve scale ambiguity and improve human-scene alignment from monocular video.
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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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BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion
BeyondMimic combines compact motion tracking with a unified guided latent diffusion model to master diverse agile behaviors from human demos and solve unseen downstream tasks via test-time classifier guidance.
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ReactiveBFM: Reactive Closed-Loop Motion Planning Towards Universal Humanoid Whole-Body Control
An auto-regressive diffusion planner trained with scheduled prefix sampling, coupled asynchronously to a pretrained universal tracker, enables closed-loop humanoid whole-body control with replanning under disturbances and zero-shot moving-target reaching.
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OmniContact: Chaining Meta-Skills via Contact Flow for Generalizable Humanoid Loco-Manipulation
OmniContact introduces contact flow as a shared representation of body trajectories and contact signals to learn and chain loco-manipulation meta-skills, reporting 98.7% success on box carrying and 76.5% on push-stack tasks.
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GenHOI: Contact-Aware Humanoid-Object Interaction by Imitating Generated Videos without Task-Specific Training
A humanoid robot can carry out diverse manipulation tasks in a zero-shot way by imitating one AI-generated video, using contact-aware trajectory optimization instead of task-specific policy training.
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Imagine2Real: Towards Zero-shot Humanoid-Object Interaction via Video Generative Priors
Imagine2Real enables zero-shot humanoid-object interaction by unifying motions as 4D point trajectories, tracking only base/hands/object keypoints inside a BFM latent space, and training with progressive simple rewards for mocap deployment.
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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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HAIC: Humanoid Agile Object Interaction Control via Dynamics-Aware World Model
HAIC enables robust humanoid interactions with underactuated objects by predicting their dynamics from proprioceptive history and using a world model for adaptive control.
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HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control
HUSKY combines humanoid-skateboard dynamics modeling with adversarial motion priors and physics-guided lean-to-steer strategies to achieve real-world stable skateboarding on a humanoid robot.
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VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands
VAIC distills a teacher policy into a vision-and-proprioception student policy using recurrent adaptation and decoupled commands, enabling diverse real-robot tasks like box carrying and skateboarding that outperform baselines.
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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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SplitAdapter: Load-Aware Humanoid Loco-Manipulation via Factorized Adaptation
SplitAdapter factorizes adaptation into load-aware and dynamics-aware encoders using split world-model objectives, GRL regularization, and hierarchical FiLM, reporting higher full-task success than baselines across 2-6 kg masses and 0-60 cm heights.