Retrieval from motion datasets combined with LLM task parsing and reward-guided noise initialization enables training-free diffusion optimization to satisfy severe spatiotemporal constraints in human motion generation.
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Gmt: General motion tracking for humanoid whole-body control
28 Pith papers cite this work. Polarity classification is still indexing.
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Rhythm transfers interactive whole-body behaviors from simulation to real dual Unitree G1 humanoids via interaction-aware retargeting and graph-reward RL.
Switch-JustDance turns Nintendo Switch Just Dance into a reproducible benchmark for evaluating humanoid whole-body controllers on real hardware using the game's scoring system.
Generates 48,000 synthetic VLK trajectories in 3D-reconstructed scenes to train a policy for egocentric perception-based humanoid navigation and object transport, shown on physical Unitree G1 robot.
X-Morph retargets human motions to kinematically plausible references for multiple legged morphologies, trains privileged RL trackers, and distills them into deployable policies that generalize and enable teleoperation and text-conditioned generation.
AnyBody distills a privileged teacher tracker into a latent unit-sphere representation and uses a masked transformer to drive humanoid control from arbitrary keypoint subsets.
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.
Stubborn introduces a unified RL framework with yaw-aligned representation, Bernoulli probabilistic termination, and adaptive sampling for robust humanoid motion tracking and fall recovery.
A data-centric approach shows that less than 3% of AMASS motion data, filtered by physics feasibility, diversity, and complexity, yields better humanoid tracking policies than the full dataset.
A multi-condition latent diffusion model transfers human motion styles to diverse humanoid robot contents with physics regularizations, achieving 96% success in real-robot trials on Unitree G1.
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.
LineRides enables commandable bicycle robot stunts via line-guided RL that uses spatial guidelines, a tracking margin for feasibility, distance-based progress, and sparse key-orientations.
AWARE is a hierarchical RL framework that enables wheeled-legged robots to perform high-dynamic reflexive obstacle evasion with emergent gaits in simulation and on the real M20 platform.
A diffusion-based motion generator combined with an RL motion tracker enables terrain-aware whole-body locomotion on a humanoid robot by adapting reference motions online from perception.
HEX introduces a state-centric framework with humanoid-aligned representations and mixture-of-experts proprioceptive prediction for coordinated whole-body control on bipedal humanoids.
AssistMimic is the first multi-agent RL method that successfully tracks assistive human-human interaction motions in simulation by using partner-aware policies, single-agent initialization, dynamic reference retargeting, and contact-promoting rewards.
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.
TeleGate achieves high-precision real-time whole-body teleoperation of humanoid robots by dynamically gating between expert policies and using a VAE motion prior to infer future intent from history, outperforming distillation baselines on dynamic motions with only 2.5 hours of mocap data.
Scaling motion tracking models along size, data volume, and compute produces a foundation model for natural, robust humanoid whole-body control with downstream uses in kinematic planning and vision-language-action models.
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.
HANDOFF is a distilled mixture-of-experts humanoid whole-body controller that follows a compact task-space interface, matches SOTA velocity tracking, provides large manipulation workspace on Unitree G1, and supports VLM-driven agentic planning with no task-specific data.
Humanoid-GPT is a causal Transformer pre-trained on a unified billion-scale motion dataset that tracks dynamic behaviors with zero-shot generalization to unseen motions and tasks.
Human2Humanoid is an unsupervised motion retargeting framework using CycleGAN, skeleton-aware GCN, end-effector consistency loss, and physics-aware constraints to transfer human motions to humanoid robots without paired data.
citing papers explorer
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Towards Highly-Constrained Human Motion Generation with Retrieval-Guided Diffusion Noise Optimization
Retrieval from motion datasets combined with LLM task parsing and reward-guided noise initialization enables training-free diffusion optimization to satisfy severe spatiotemporal constraints in human motion generation.
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Rhythm: Learning Interactive Whole-Body Control for Dual Humanoids
Rhythm transfers interactive whole-body behaviors from simulation to real dual Unitree G1 humanoids via interaction-aware retargeting and graph-reward RL.
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Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game
Switch-JustDance turns Nintendo Switch Just Dance into a reproducible benchmark for evaluating humanoid whole-body controllers on real hardware using the game's scoring system.
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VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
Generates 48,000 synthetic VLK trajectories in 3D-reconstructed scenes to train a policy for egocentric perception-based humanoid navigation and object transport, shown on physical Unitree G1 robot.
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X-Morph: Human Motion Priors for Scalable Robot Learning Across Morphologies
X-Morph retargets human motions to kinematically plausible references for multiple legged morphologies, trains privileged RL trackers, and distills them into deployable policies that generalize and enable teleoperation and text-conditioned generation.
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AnyBody: Free-Form Whole-Body Humanoid Control from Arbitrary Keypoint Guidance
AnyBody distills a privileged teacher tracker into a latent unit-sphere representation and uses a masked transformer to drive humanoid control from arbitrary keypoint subsets.
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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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Stubborn: A Streamlined and Unified Reinforcement Learning Framework for Robust Motion Tracking and Fall Recovery for Humanoids
Stubborn introduces a unified RL framework with yaw-aligned representation, Bernoulli probabilistic termination, and adaptive sampling for robust humanoid motion tracking and fall recovery.
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LIMMT: Less is More for Motion Tracking
A data-centric approach shows that less than 3% of AMASS motion data, filtered by physics feasibility, diversity, and complexity, yields better humanoid tracking policies than the full dataset.
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Bionic Human-Motion Style Transfer for Physically Executable Whole-Body Control of Humanoid Robots
A multi-condition latent diffusion model transfers human motion styles to diverse humanoid robot contents with physics regularizations, achieving 96% success in real-robot trials on Unitree G1.
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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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LineRides: Line-Guided Reinforcement Learning for Bicycle Robot Stunts
LineRides enables commandable bicycle robot stunts via line-guided RL that uses spatial guidelines, a tracking margin for feasibility, distance-based progress, and sparse key-orientations.
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Unleashing the Agility of Wheeled-Legged Robots for High-Dynamic Reflexive Obstacle Evasion
AWARE is a hierarchical RL framework that enables wheeled-legged robots to perform high-dynamic reflexive obstacle evasion with emergent gaits in simulation and on the real M20 platform.
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Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking
A diffusion-based motion generator combined with an RL motion tracker enables terrain-aware whole-body locomotion on a humanoid robot by adapting reference motions online from perception.
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HEX: Humanoid-Aligned Experts for Cross-Embodiment Whole-Body Manipulation
HEX introduces a state-centric framework with humanoid-aligned representations and mixture-of-experts proprioceptive prediction for coordinated whole-body control on bipedal humanoids.
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Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning
AssistMimic is the first multi-agent RL method that successfully tracks assistive human-human interaction motions in simulation by using partner-aware policies, single-agent initialization, dynamic reference retargeting, and contact-promoting rewards.
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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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TeleGate: Whole-Body Humanoid Teleoperation via Gated Expert Selection with Motion Prior
TeleGate achieves high-precision real-time whole-body teleoperation of humanoid robots by dynamically gating between expert policies and using a VAE motion prior to infer future intent from history, outperforming distillation baselines on dynamic motions with only 2.5 hours of mocap data.
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SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
Scaling motion tracking models along size, data volume, and compute produces a foundation model for natural, robust humanoid whole-body control with downstream uses in kinematic planning and vision-language-action models.
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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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HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers
HANDOFF is a distilled mixture-of-experts humanoid whole-body controller that follows a compact task-space interface, matches SOTA velocity tracking, provides large manipulation workspace on Unitree G1, and supports VLM-driven agentic planning with no task-specific data.
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Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking
Humanoid-GPT is a causal Transformer pre-trained on a unified billion-scale motion dataset that tracks dynamic behaviors with zero-shot generalization to unseen motions and tasks.
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Human2Humanoid: Physics-Aware Cross-Morphology Motion Retargeting for Humanoid Robots
Human2Humanoid is an unsupervised motion retargeting framework using CycleGAN, skeleton-aware GCN, end-effector consistency loss, and physics-aware constraints to transfer human motions to humanoid robots without paired data.
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SPRINT: Efficient Spectral Priors for Humanoid Athletic Sprints
SPRINT generates sprint trajectories for humanoids via spectral priors from five human motion sequences, achieving 6 m/s peak velocity with zero-shot sim-to-real transfer on Unitree G1.
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Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking
Any2Any transfers humanoid whole-body tracking models across embodiments via kinematic alignment followed by targeted PEFT, matching full-training performance with 1% of the data and compute on tested platforms.
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HoloMotion-1 Technical Report
HoloMotion-1 trains a MoE Transformer policy on hybrid video and MoCap motion data to achieve robust zero-shot tracking that transfers directly to real humanoid robots.
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Switch: Learning Agile Skills Switching for Humanoid Robots
Switch enables humanoid robots to perform agile, seamless transitions between locomotion skills via a kinematic skill graph, DRL tracking policy, and real-time graph-search scheduler.