Whole-Body Offline-to-Online Planning for Robust Jumping of Full-Sized Humanoid Robot

Dynamic bipedal jumping is highly sensitive to takeoff momentum and landing configuration, making the direct execution of purely offline-optimized trajectories unreliable on full-sized humanoid robots in the presence of modeling inaccuracies and execution uncertainties. A key challenge is the gap between dynamic feasibility predicted offline using simplified models and executability on physical hardware, because such models cannot fully capture full-body dynamics, actuator behavior, contact transitions, and execution uncertainty. This paper proposes an offline-to-online planning and whole-body control framework for robust in-place jumping of full-sized humanoid robots. The framework integrates phase-consistent offline trajectory optimization, lightweight online reference reshaping, constraint-aware whole-body control, and actuator-level command mapping to improve execution robustness without online re-optimization. In the offline stage, centroidal-dynamics-based trajectory optimization generates jumping references subject to kinematic-consistency and contact-feasibility constraints. During execution, these references are adapted online using real-time state estimates to compensate for takeoff deviations and regulate the landing state; a weighted quadratic-programming whole-body controller then tracks the adapted references. Hardware experiments on a 79.5 kg humanoid robot demonstrate repeatable in-place vertical jumps with a height of approximately 30 cm and stable landings. The results show that robust jumping on a full-sized humanoid robot can be achieved by combining offline nominal trajectory generation with online execution adaptation rather than relying on exact reproduction of the offline trajectories.