Dynamic and continuous jumping remains an open yet challenging problem in bipedal robot control. The choice of dynamic models in trajectory optimization (TO) problems plays a huge role in trajectory accuracy and computation efficiency, which normally cannot be ensured simultaneously. In this letter, we propose a novel adaptive-model optimization approach, a unified framework of Adaptive-model TO and Adaptive-frequency Model Predictive Control (MPC), to effectively realize continuous and robust jumping on HECTOR bipedal robot. The proposed Adaptive-model TO fuses adaptive-fidelity dynamics modeling of bipedal jumping motion for model fidelity necessities in different jumping phases to ensure trajectory accuracy and computation efficiency. In addition, conventional approaches have unsynchronized sampling frequencies in TO and real-time control, causing the framework to have mismatched modeling resolutions. We adapt MPC sampling frequency based on TO trajectory resolution in different phases for effective trajectory tracking. In hardware experiments, we have demonstrated robust and dynamic jumps covering a distance of up to 40 cm (57% of robot height). To verify the repeatability of this experiment, we run 53 jumping experiments and achieve 90% success rate. In continuous jumps, we demonstrate continuous bipedal jumping with terrain height perturbations (up to 5 cm) and discontinuities (up to 20 cm gap).

翻译：动态连续跳跃一直是双足机器人控制中尚未解决且极具挑战性的问题。轨迹优化（TO）问题中动力学模型的选择对轨迹精度和计算效率起着至关重要的作用，而这两者通常无法同时保证。本文提出一种新颖的自适应模型优化方法——一个融合自适应模型轨迹优化与自适应频率模型预测控制（MPC）的统一框架——以有效实现HECTOR双足机器人的连续鲁棒跳跃。自适应模型轨迹优化融合了双足跳跃运动的自适应精度动力学建模，根据跳跃不同阶段对模型保真度的需求，确保轨迹精度与计算效率。此外，传统方法中轨迹优化与实时控制的采样频率不同步，导致框架出现建模分辨率不匹配的问题。我们根据不同阶段轨迹优化轨迹的分辨率，自适应调整MPC采样频率，以实现有效的轨迹跟踪。在硬件实验中，我们实现了覆盖40厘米（占机器人身高的57%）距离的鲁棒动态跳跃。为验证实验可重复性，我们进行了53次跳跃实验，成功率达90%。在连续跳跃中，我们演示了带有地形高度扰动（最高5厘米）和不连续地形（最大20厘米间隙）的双足连续跳跃。