Expert-Guided Imitation for Learning Humanoid Loco-Manipulation from Motion Capture

Despite significant advances in bipedal locomotion, enabling humanoid robots to perform general whole-body tasks through meaningful interaction with their environments remains a challenging open problem. While deep reinforcement learning (RL) has recently demonstrated impressive results in dynamic walking — even on complex and unpredictable terrain — real-world utility demands that humanoids go beyond locomotion to execute task-oriented behaviors. In this work, we propose a framework for teaching humanoid robots to imitate humans doing useful tasks by training policies for tracking human motion references. Our approach leverages high-quality in-house motion capture (MoCap) data, from which we perform kinematic retargeting to project human trajectories onto a humanoid platform. Crucially, we adopt a hybrid learning paradigm: the policy is trained to track upper-body and root motions from the MoCap data, and receives additional supervision from a pre-trained omnidirec- tional walking expert. This expert guidance, implemented via a Behavior Cloning (BC) objective, ensures that leg motion respects dynamics and kinematic constraints of the humanoid. We train policies entirely in simulation and successfully transfer them to a real humanoid robot. We validate our method on a box loco-manipulation task, demonstrating effective sim-to-real transfer and marking a step toward more capable, task-driven humanoid behavior.