  Humanoid control is an important research challenge offering avenues for
integration into human-centric infrastructures and enabling physics-driven
humanoid animations. The daunting challenges in this field stem from the
difficulty of optimizing in high-dimensional action spaces and the instability
introduced by the bipedal morphology of humanoids. However, the extensive
collection of human motion-captured data and the derived datasets of humanoid
trajectories, such as MoCapAct, paves the way to tackle these challenges. In
this context, we present Humanoid Generalist Autoencoding Planner (H-GAP), a
state-action trajectory generative model trained on humanoid trajectories
derived from human motion-captured data, capable of adeptly handling downstream
control tasks with Model Predictive Control (MPC). For 56 degrees of freedom
humanoid, we empirically demonstrate that H-GAP learns to represent and
generate a wide range of motor behaviours. Further, without any learning from
online interactions, it can also flexibly transfer these behaviors to solve
novel downstream control tasks via planning. Notably, H-GAP excels established
MPC baselines that have access to the ground truth dynamics model, and is
superior or comparable to offline RL methods trained for individual tasks.
Finally, we do a series of empirical studies on the scaling properties of
H-GAP, showing the potential for performance gains via additional data but not
computing. Code and videos are available at
https://ycxuyingchen.github.io/hgap/.
