Jun 2026· arXiv.org· Vol abs/2606.29209· 1 citation· 43 references
Computer Science
TL;DR
The effectiveness of Any body, a unified whole-body humanoid controller driven by an arbitrary subset of body keypoints chosen at deploy time, is demonstrated by tracking large-scale human motions from arbitrary keypoint subsets, free-form control, flexibly teleoperating, and learning downstream behaviors including locomotion, in-air writing, and obstacle-reach.
Abstract
We present AnyBody, a unified whole-body humanoid controller driven by an arbitrary subset of body keypoints chosen at deploy time. Prior physics-based trackers either rely on expensive full-body motion capture and error-prone trajectory retargeting, which bottleneck scalable data collection and policy learning, or decompose upper- and lower-body control into separate hierarchical representations, sacrificing the coordinated whole-body motions that loco-manipulation requires. We close this gap by learning a single latent motion representation that any keypoint subset can address. To achieve this, we first train a privileged teacher tracker on a large unstructured motion corpus and distill it online into a deterministic encoder-decoder student whose latent space is a unit sphere. We then train a transformer keypoint encoder that admits any subset of body keypoints through masked self-attention, aligning it to the privileged latent. Additionally, we treat the frozen decoder as a motor prior and specialize downstream tasks with a lightweight residual corrector in the latent space. We demonstrate the effectiveness of AnyBody by tracking large-scale human motions from arbitrary keypoint subsets, free-form control, flexibly teleoperating, and learning downstream behaviors including locomotion, in-air writing, and obstacle-reach.
This work proposes a prior-first, condition-second framework for body-conditioned hand motion completion that improves kinematic plausibility, robustness, and controllability compared to end-to-end conditioned baselines, particularly in low-resource and cross-dataset settings.
GigaBrain-WBC-0.5, the first Behavior World Model for humanoid whole-body control, is presented, which trains a causal Transformer to jointly predict its next action, next state, and the distribution over its next latent behavior command, so the network that acts also models how the environment shapes what it can do next.
Ziyang Cheng, Tianshu Tang, Jinxi Lan et al.· 0 citations
HuMiT is presented, a whole-body teleoperation system built on a minimal reference target that requires only a minimal reference target, consisting only of root height, root velocity, and sparse keypoint positions at the current frame, yet achieves competitive or superior tracking superiority compared to methods relying on more diverse reference states.
Athena-WBC is proposed, a compact teacher-student pipeline with capability-aligned policy experts for long-tail humanoid whole-body control that shows improved recovery of training-set long-tail motions and better held-out tracking than a strong SONIC-recipe baseline, using only a small number of experts.
Yuanchang Jiang, Ningyuan Zhang, Xicun Yang et al.· 1 citation
DECOWAM is introduced, a whole-body world-action model that separates camera ego-motion from base and arm actions through dedicated conditional interfaces and shows that embodiment-aware factorization can support parameter-efficient joint visual prediction and whole-body control under moving viewpoints.
Siyuan Ma, Boshi Zhang, Yutian Zhang et al.· 1 citation
Humanoid general motion tracking requires policies that can follow diverse whole-body references while maintaining balance. Building such policies involves many practical design choices, and their individual effects are often hard to assess. We address this issue with an empirical study of common modeling and training factors used in recent humanoid motion-imitation pipelines. To make the study controlled and reproducible, we developed YAHMP, an open-source modular framework for training, evaluating, and deploying whole-body motion tracking policies on the Unitree G1. Within YAHMP, we define a nominal configuration and compare variants that differ in motion-command representation, observation history, action representation, actuation profile, hand-force randomization during training, and training approach. We evaluate the resulting policies on a test set of retargeted human motions and compare the nominal policy with TWIST2 as an external baseline trained on the same motion set. The results distinguish choices with clear tracking effects from choices that mainly change actuation effort, training complexity, or physical interaction capability. Finally, we deploy YAHMP policies zero-shot on the real Unitree G1, demonstrating diverse whole-body motion tracking, balance under external perturbations, and forceful interaction.