Any2Any transfers pretrained humanoid whole-body tracking policies to new embodiments with 1% of original training cost via kinematic alignment and parameter-efficient fine-tuning.
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Omnih2o: Universal and dexterous human-to-humanoid whole-body teleoperation and learning
15 Pith papers cite this work. Polarity classification is still indexing.
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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.
The Weightlessness Mechanism lets humanoid robots imitate non-self-stabilizing motions by dynamically relaxing specific joints to exploit passive environmental contacts, generalizing from single demonstrations to varied setups.
NMR uses VAE-based clustered expert physics refinement and a CNN-Transformer to learn dynamics-aware retargeting, eliminating joint jumps and self-collisions on Unitree G1 while accelerating downstream control policies.
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.
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.
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.
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.
RPG trains a single policy with transition and timing randomization for stable multi-skill fighting on humanoids, integrated with locomotion for arbitrary-duration combat.
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.
HTD, a multimodal transformer policy trained with behavioral cloning and touch dreaming to predict future tactile latents, achieves a 90.9% relative success rate improvement over baselines on five real-world contact-rich humanoid loco-manipulation tasks.
A literature review of pHHI that proposes a taxonomy of interaction types by modality and engagement level while outlining pathways to integrate control, intent, and modeling for more seamless humanoid-human collaboration.
A complete IMU-based whole-body teleoperation system for the Unitree G1 humanoid achieves stable synchronized motion reproduction across walking, gestures, and other actions via direct mapping validated in MuJoCo sim2sim and on physical hardware sim2real.
citing papers explorer
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Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking
Any2Any transfers pretrained humanoid whole-body tracking policies to new embodiments with 1% of original training cost via kinematic alignment and parameter-efficient fine-tuning.
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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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Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot
The Weightlessness Mechanism lets humanoid robots imitate non-self-stabilizing motions by dynamically relaxing specific joints to exploit passive environmental contacts, generalizing from single demonstrations to varied setups.
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Make Tracking Easy: Neural Motion Retargeting for Humanoid Whole-body Control
NMR uses VAE-based clustered expert physics refinement and a CNN-Transformer to learn dynamics-aware retargeting, eliminating joint jumps and self-collisions on Unitree G1 while accelerating downstream control policies.
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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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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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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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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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RPG: Robust Policy Gating for Smooth Multi-Skill Transitions in Humanoid Fighting
RPG trains a single policy with transition and timing randomization for stable multi-skill fighting on humanoids, integrated with locomotion for arbitrary-duration combat.
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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.
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Learning Versatile Humanoid Manipulation with Touch Dreaming
HTD, a multimodal transformer policy trained with behavioral cloning and touch dreaming to predict future tactile latents, achieves a 90.9% relative success rate improvement over baselines on five real-world contact-rich humanoid loco-manipulation tasks.
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Toward Seamless Physical Human-Humanoid Interaction: Insights from Control, Intent, and Modeling with a Vision for What Comes Next
A literature review of pHHI that proposes a taxonomy of interaction types by modality and engagement level while outlining pathways to integrate control, intent, and modeling for more seamless humanoid-human collaboration.
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Real-Time Whole-Body Teleoperation of a Humanoid Robot Using IMU-Based Motion Capture with Sim2Sim and Sim2Real Validation
A complete IMU-based whole-body teleoperation system for the Unitree G1 humanoid achieves stable synchronized motion reproduction across walking, gestures, and other actions via direct mapping validated in MuJoCo sim2sim and on physical hardware sim2real.