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.
Agility meets stability: Ver- satile humanoid control with heterogeneous data
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.RO 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
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.
A modular RL framework separates basic gaits from task actions via an oscillator plus feedback and uses a posture state machine to switch between ball-seeking/kicking and fall recovery for bipedal soccer robots in simulation.
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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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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Reinforcement Learning Enabled Adaptive Multi-Task Control for Bipedal Soccer Robots
A modular RL framework separates basic gaits from task actions via an oscillator plus feedback and uses a posture state machine to switch between ball-seeking/kicking and fall recovery for bipedal soccer robots in simulation.