Humanoid robots are compelling because they share the physical interface of the human world, including stairs, doors, tools, shelves, and workspaces designed around human embodiment. However, this same embodiment also makes humanoids uniquely difficult to control, since locomotion, balance, manipulation, perception, and hardware reliability are tightly coupled. This thesis studies how to build general-purpose humanoid skills through scalable sim-to-real learning.

The dissertation develops this theme in three parts. First, it establishes the motor foundation for sim-to-real whole-body humanoid control, including teleoperation, dexterous loco-manipulation, generalist whole-body control, and explicit sim-to-real alignment for agile behaviors. Second, it shows that robust mobility in unstructured environments requires perception-aware control rather than blind locomotion. Third, it extends this framework to perceptive loco-manipulation, demonstrating that large-scale visual sim-to-real learning can enable onboard RGB-based humanoid policies for navigation, object transport, placement, and articulated interactions such as door opening.

Taken together, these contributions suggest a scalable recipe for general-purpose humanoid skills in which human motion provides priors, simulation provides scale, control abstractions provide reuse, alignment preserves transfer, and perception enables embodied interaction. More broadly, this thesis argues that the path toward useful humanoids is cumulative: general-purpose capability becomes plausible when skill acquisition, policy design, and deployment are treated as one continuous sim-to-real systems problem.

Guanya Shi (co-chair)

Changliu Liu (co-chair)

Kris Kitani

Marco Hutter (ETH Zurich)

Pieter Abbeel (UC Berkeley)