Abstract: Current humanoid teleoperation systems either lack reliable low-level control policies, or struggle to acquire accurate whole-body control commands, making it difficult to teleoperate humanoids for loco-manipulation tasks. To solve these issues, we propose HOMIE, a novel humanoid teleoperation system that integrates a humanoid loco-manipulation policy and a low-cost exoskeleton-based cockpit. The policy enables humanoid robots to walk and squat to specific heights while accommodating arbitrary upper-body poses. This is achieved through our novel RL-based training framework that incorporates upper-body poses curriculum, height-tracking reward, and symmetry utilization, without relying on any motion priors. Complementing the policy, the cockpit integrates isomorphic exoskeleton arms, hands, and a pedal, allowing a single operator to achieve full control of the humanoid robot. Our experiments show our system facilitates more stable, rapid, and precise humanoid loco-manipulation teleoperation, accelerating task completion and eliminating retargeting errors compared to inverse kinematics-based methods. We also validate the effectiveness of the data collected by our system for imitation learning.