PMG: Parameterized Motion Generator for Human-like Locomotion Control
This work addresses practical deployment issues for humanoid robots by enabling more adaptable and natural locomotion control, though it appears incremental as it builds on existing data-driven and imitation-learning methods.
The paper tackled the challenge of adapting whole-body reference-guided methods for humanoid locomotion to higher-level commands and diverse tasks by proposing the Parameterized Motion Generator (PMG), which synthesizes reference trajectories using compact parameterized data and high-dimensional control inputs, and validated it on the ZERITH Z1 humanoid prototype to produce natural, human-like locomotion with precise response to inputs like VR teleoperation.
Recent advances in data-driven reinforcement learning and motion tracking have substantially improved humanoid locomotion, yet critical practical challenges remain. In particular, while low-level motion tracking and trajectory-following controllers are mature, whole-body reference-guided methods are difficult to adapt to higher-level command interfaces and diverse task contexts: they require large, high-quality datasets, are brittle across speed and pose regimes, and are sensitive to robot-specific calibration. To address these limitations, we propose the Parameterized Motion Generator (PMG), a real-time motion generator grounded in an analysis of human motion structure that synthesizes reference trajectories using only a compact set of parameterized motion data together with high-dimensional control commands. Combined with an imitation-learning pipeline and an optimization-based sim-to-real motor parameter identification module, we validate the complete approach on our humanoid prototype ZERITH Z1 and show that, within a single integrated system, PMG produces natural, human-like locomotion, responds precisely to high-dimensional control inputs-including VR-based teleoperation-and enables efficient, verifiable sim-to-real transfer. Together, these results establish a practical, experimentally validated pathway toward natural and deployable humanoid control. Website: https://pmg-icra26.github.io/