  We present a method for controlling a simulated humanoid to grasp an object
and move it to follow an object trajectory. Due to the challenges in
controlling a humanoid with dexterous hands, prior methods often use a
disembodied hand and only consider vertical lifts or short trajectories. This
limited scope hampers their applicability for object manipulation required for
animation and simulation. To close this gap, we learn a controller that can
pick up a large number (>1200) of objects and carry them to follow randomly
generated trajectories. Our key insight is to leverage a humanoid motion
representation that provides human-like motor skills and significantly speeds
up training. Using only simplistic reward, state, and object representations,
our method shows favorable scalability on diverse object and trajectories. For
training, we do not need dataset of paired full-body motion and object
trajectories. At test time, we only require the object mesh and desired
trajectories for grasping and transporting. To demonstrate the capabilities of
our method, we show state-of-the-art success rates in following object
trajectories and generalizing to unseen objects. Code and models will be
released.
