# Motion Retargeting

3 related articles

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researchsquare.com Nov 2, 2026
Splitting a humanoid's upper and lower body into two controllers cuts upper-body joint error to about 0.1 radians on H1 and H1-2
Humanoid whole-body control usually drives the upper and lower body with one policy, so large arm motions can disturb balance and the two tasks interfere during training. JAEGER splits the body into two independent controllers that share observations and rewards, initializes the upper body with supervised learning, then trains the whole body with reinforcement learning while supporting both root-velocity and joint-angle command modes. In simulation comparisons on the H1 and H1-2 platforms, JAEGER's upper-body joint tracking error is lower than HumanPlus, ExBody and OmniH2O, with pose-mode joint errors about 30% to 60% of those methods.
Humanoid whole-body control usually drives the upper and lower body with one policy, so large arm motions can disturb balance and the two tasks interfere during training. JAEGER splits the body into two independent controllers that share observations and rewards, initializes the upper body with supervised learning, then trains the whole body with reinforcement learning while supporting both root-velocity and joint-angle command modes. In simulation comparisons on the H1 and H1-2 platforms, JAEGER's upper-body joint tracking error is lower than HumanPlus, ExBody and OmniH2O, with pose-mode joint errors about 30% to 60% of those methods.
Beijing Academy of Artificial Intelligence
Ziluo Ding（First author, Corresponding author）
JAEGER Humanoid Whole-Body Control Curriculum Learning Motion Retargeting
arXiv Oct 1, 2026
TERRA reconstructs terrain from kinematics alone, letting a muscle-actuated body complete stairs, ramps and seats
TERRA presents an end-to-end pipeline that, from scene-less kinematic trajectories alone, combines terrain priors, estimated contacts and negative free-space evidence to recover task-relevant support geometry, adds anatomical, tendon-continuity and contact constraints during retargeting, and uses motion-terrain pairs from five datasets to train a single muscle-actuated control policy, improving terrain accuracy, sharply reducing anatomical and interaction violations, and achieving the highest completion rate over supported terrain families across reconstruction, retargeting and held-out tracking benchmarks.
TERRA presents an end-to-end pipeline that, from scene-less kinematic trajectories alone, combines terrain priors, estimated contacts and negative free-space evidence to recover task-relevant support geometry, adds anatomical, tendon-continuity and contact constraints during retargeting, and uses motion-terrain pairs from five datasets to train a single muscle-actuated control policy, improving terrain accuracy, sharply reducing anatomical and interaction violations, and achieving the highest completion rate over supported terrain families across reconstruction, retargeting and held-out tracking benchmarks.
Merkourios Simos（First author）
TERRA Terrain Reconstruction Motion Retargeting Proximal Policy Optimization
arXiv Sep 30, 2026
CrossBFM distills Unitree G1's latent behavior space onto three humanoids in under one GPU-hour, losing only 0.025 rad in tracking
CrossBFM freezes the backward map of a Behavior Foundation Model pretrained on the Unitree G1 and, using the frame-level cross-embodiment correspondence supplied by retargeted data, distills that latent space onto three humanoids (Inhouse M3, Booster T1, Fourier N1) with a unified encoder that has no robot-specific parameters, training the encoder in under one GPU-hour and each tracker in about 10 more GPU-hours; all three prompting modes transfer (motion tracking, goal reaching between poses, reward optimization), the latent-conditioned policy loses only 0.
CrossBFM freezes the backward map of a Behavior Foundation Model pretrained on the Unitree G1 and, using the frame-level cross-embodiment correspondence supplied by retargeted data, distills that latent space onto three humanoids (Inhouse M3, Booster T1, Fourier N1) with a unified encoder that has no robot-specific parameters, training the encoder in under one GPU-hour and each tracker in about 10 more GPU-hours; all three prompting modes transfer (motion tracking, goal reaching between poses, reward optimization), the latent-conditioned policy loses only 0.
Tan-Dzung Do（First author）
CrossBFM Forward-Backward Representations Knowledge Distillation Motion Retargeting Humanoid Whole-Body Control

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Splitting a humanoid's upper and lower body into two controllers cuts upper-body joint error to about 0.1 radians on H1 and H1-2

TERRA reconstructs terrain from kinematics alone, letting a muscle-actuated body complete stairs, ramps and seats

CrossBFM distills Unitree G1's latent behavior space onto three humanoids in under one GPU-hour, losing only 0.025 rad in tracking