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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Twist2: Scalable, portable, and holistic humanoid data collection system
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A multi-source 16,074-clip quadruped motion library plus a flow-matching generalist tracker shows empirical data scaling and zero-shot unseen tracking, integrated with all-terrain locomotion and real-robot deployment.
A force-aware humanoid benchmark pairs synchronized human motion-force data with simulation-based force replay to evaluate whole-body control policies under realistic physical disturbances.
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.
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.
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.
PressMimic fuses RGB and pressure for pose estimation via FRAPPE++ and uses pressure signals in RL policy PSP, backed by the MotionPRO dataset, to achieve physically consistent humanoid motion imitation.
TaskNPoint lets humanoid robots learn dynamic skills such as tennis backhands from single short human video demonstrations plus under one hour of single-GPU simulation training, achieving zero-shot generalization to new goal locations without per-task reward tuning.
CoorDex distills privileged body and hand motion teachers into proprioceptive latent priors and composes them via shared-context residual RL heads to enable continuous high-DoF dexterous loco-manipulation.
OpenHLM is an empirical recipe yielding a whole-body humanoid VLA model that outperforms GR00T N1.6 and Ψ0 baselines on long-horizon tasks using less than half the demonstration time.
Stubborn introduces a unified RL framework with yaw-aligned representation, Bernoulli probabilistic termination, and adaptive sampling for robust humanoid motion tracking and fall recovery.
MotionWAM conditions a policy on intermediate features from a video world model to predict unified whole-body motion tokens, enabling real-time humanoid loco-manipulation that outperforms VLA baselines by over 30% on nine Unitree G1 tasks.
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.
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.
CEER proposes a compliant end-effector and root control interface that unifies loco-manipulation for humanoids via a distilled low-level policy and hierarchical planners.
BifrostUMI enables robot-free human demonstration capture via VR and wrist cameras to train visuomotor policies that predict keypoint trajectories for transfer to humanoid whole-body control through retargeting.
SixthSense infers whole-body contact events and wrenches in humanoids from proprioception and IMU data alone by tokenizing histories and estimating a sparse contact-event flow with conditional flow 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.
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.
Humanoid-LLA converts unconstrained natural language commands into stable whole-body motions for humanoid robots using a unified motion vocabulary and two-stage supervised-plus-reinforcement fine-tuning.
HEFT enables tracking of human motions including locomotion and squats on a 175cm 65kg humanoid under up to 24kg payloads by combining Privileged Motion Guidance from noisy VR data with a Windowed Payload Curriculum.
RL policy biases MPPI sampling for humanoid control, enabling online correction of drift and new whole-body references in simulation.
VQActFlow discretizes action chunks via vector quantization, generates code sequences with variational flow matching, and applies inference-time guidance to steer multi-task robot policies toward instructed and feasible modes.
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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Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report
A multi-source 16,074-clip quadruped motion library plus a flow-matching generalist tracker shows empirical data scaling and zero-shot unseen tracking, integrated with all-terrain locomotion and real-robot deployment.
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ThorArena: Benchmarking Humanoid Physical Interaction with Human Motion-Force Demonstrations
A force-aware humanoid benchmark pairs synchronized human motion-force data with simulation-based force replay to evaluate whole-body control policies under realistic physical disturbances.
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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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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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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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PressMimic: Pressure-Guided Motion Capture and Control for Humanoid Robot Imitation
PressMimic fuses RGB and pressure for pose estimation via FRAPPE++ and uses pressure signals in RL policy PSP, backed by the MotionPRO dataset, to achieve physically consistent humanoid motion imitation.
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TaskNPoint: How to Teach Your Humanoid to Hit a Backhand in Minutes
TaskNPoint lets humanoid robots learn dynamic skills such as tennis backhands from single short human video demonstrations plus under one hour of single-GPU simulation training, achieving zero-shot generalization to new goal locations without per-task reward tuning.
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CoorDex: Coordinating Body and Hand Priors for Continuous Dexterous Humanoid Loco-Manipulation
CoorDex distills privileged body and hand motion teachers into proprioceptive latent priors and composes them via shared-context residual RL heads to enable continuous high-DoF dexterous loco-manipulation.
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OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation
OpenHLM is an empirical recipe yielding a whole-body humanoid VLA model that outperforms GR00T N1.6 and Ψ0 baselines on long-horizon tasks using less than half the demonstration time.
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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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MotionWAM: Towards Foundation World Action Models for Real-Time Humanoid Loco-Manipulation
MotionWAM conditions a policy on intermediate features from a video world model to predict unified whole-body motion tokens, enabling real-time humanoid loco-manipulation that outperforms VLA baselines by over 30% on nine Unitree G1 tasks.
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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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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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CEER: Compliant End-Effector and Root Control as a Unified Interface for Hierarchical Humanoid Loco-Manipulation
CEER proposes a compliant end-effector and root control interface that unifies loco-manipulation for humanoids via a distilled low-level policy and hierarchical planners.
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BifrostUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation
BifrostUMI enables robot-free human demonstration capture via VR and wrist cameras to train visuomotor policies that predict keypoint trajectories for transfer to humanoid whole-body control through retargeting.
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SixthSense: Task-Agnostic Proprioception-Only Whole-Body Wrench Estimation for Humanoids
SixthSense infers whole-body contact events and wrenches in humanoids from proprioception and IMU data alone by tokenizing histories and estimating a sparse contact-event flow with conditional flow matching.
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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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Commanding Humanoid by Free-form Language: A Large Language Action Model with Unified Motion Vocabulary
Humanoid-LLA converts unconstrained natural language commands into stable whole-body motions for humanoid robots using a unified motion vocabulary and two-stage supervised-plus-reinforcement fine-tuning.
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HEFT: Heavy-Payload Full-size Humanoid Teleoperation with Privileged Motion Guidance and Windowed Payload Curriculum
HEFT enables tracking of human motions including locomotion and squats on a 175cm 65kg humanoid under up to 24kg payloads by combining Privileged Motion Guidance from noisy VR data with a Windowed Payload Curriculum.
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RGB: RL Guided Whole-Body MPPI for Humanoid Control
RL policy biases MPPI sampling for humanoid control, enabling online correction of drift and new whole-body references in simulation.
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VQActFlow: Vector-Quantized Action Mode Steering for Multi-Task Robot Manipulation
VQActFlow discretizes action chunks via vector quantization, generates code sequences with variational flow matching, and applies inference-time guidance to steer multi-task robot policies toward instructed and feasible modes.
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Duet: Dual-Robot Understanding via Efficient Teaching
DUET pretrains collaborative policies on human-human VR demonstrations then fine-tunes on minimal robot teleoperation data, achieving equal or better performance than robot-only baselines with 5.4x faster collection across four tasks.
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OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation
OASIS generates scalable simulation data for humanoid loco-manipulation via 3D generative asset reconstruction and domain randomization, yielding a policy with higher zero-shot real-world success than real-robot teleoperation data.
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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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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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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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M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking
M3imic unifies heterogeneous motion modalities via encoders into a shared latent space for a single RL-trained whole-body controller achieving high sim success and sim-to-real transfer on Unitree G1.
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Constrained Whole-Body Tracking for Humanoid Robots
ConstrainedMimic integrates operational space control and control barrier functions into RL tracking policies to enforce arbitrary runtime constraints on humanoid kinematics and dynamics while preserving contact modes and tracking goals.
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PRIME: Physically-consistent Robotic Inertial and Motion Estimation for Legged and Humanoid Robots
PRIME is a MAP optimization framework that refines onboard kinematics into dynamically consistent trajectories for legged robots while jointly estimating contact forces and inertial parameters using differentiable smoothed contact dynamics.
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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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World Model for Robot Learning: A Comprehensive Survey
A comprehensive survey that organizes the literature on world models in robot learning, their roles in policy learning, planning, simulation, and video-based generation, with connections to navigation, driving, datasets, and benchmarks.
- Learning Versatile Humanoid Manipulation with Touch Dreaming