  We address the longstanding challenge of producing flexible, realistic
humanoid character controllers that can perform diverse whole-body tasks
involving object interactions. This challenge is central to a variety of
fields, from graphics and animation to robotics and motor neuroscience. Our
physics-based environment uses realistic actuation and first-person perception
-- including touch sensors and egocentric vision -- with a view to producing
active-sensing behaviors (e.g. gaze direction), transferability to real robots,
and comparisons to the biology. We develop an integrated neural-network based
approach consisting of a motor primitive module, human demonstrations, and an
instructed reinforcement learning regime with curricula and task variations. We
demonstrate the utility of our approach for several tasks, including
goal-conditioned box carrying and ball catching, and we characterize its
behavioral robustness. The resulting controllers can be deployed in real-time
on a standard PC. See overview video, https://youtu.be/2rQAW-8gQQk .
