We tackle the problem of developing humanoid loco-manipulation skills with deep imitation learning. The challenge of collecting human demonstrations for humanoids, in conjunction with the difficulty of policy training under a high degree of freedom, presents substantial challenges. We introduce TRILL, a data-efficient framework for learning humanoid loco-manipulation policies from human demonstrations. In this framework, we collect human demonstration data through an intuitive Virtual Reality (VR) interface. We employ the whole-body control formulation to transform task-space commands from human operators into the robot’s joint-torque actuation while stabilizing its dynamics. By employing high-level action abstractions tailored for humanoid robots, our method can efficiently learn complex loco-manipulation skills. We demonstrate the effectiveness of TRILL in simulation and on a real-world robot for performing various types of tasks.