CWI decouples MoCap data for upper-body manipulation and lower-body locomotion, using dual discriminators and multi-critic training plus distillation to produce a policy that works from hand poses and velocity commands alone.
Agility meets stability: Versatile humanoid control with heterogeneous data
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
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cs.RO 5years
2026 5verdicts
UNVERDICTED 5representative 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.
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.
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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CWI: Composite Humanoid Whole-Body Imitation System for Loco-manipulation
CWI decouples MoCap data for upper-body manipulation and lower-body locomotion, using dual discriminators and multi-critic training plus distillation to produce a policy that works from hand poses and velocity commands alone.
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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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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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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.