发表机构
Rutgers University; Yale University（罗格斯大学; 耶鲁大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对张拉整体机器人接触丰富动力学建模难的问题，提出结合接触感知GNN动力学模型与混合MPPI控制器的导航方法，在复杂环境中显著提升预测精度与导航性能。
AI 中文摘要
张拉整体机器人具有轻量化、柔顺的移动能力，可在复杂地形上运动，但由于其复杂的接触丰富动力学和部分可观测性，对其建模和控制仍然困难。本工作提出了一种模型预测路径积分（MPPI）控制器，用于由学习得到的图神经网络（GNN）动力学模型驱动的三杆张拉整体机器人。本工作首先通过可微分的接触检测模块扩展了先前基于GNN的模型。该扩展使动力学模型能够推理非水平平面地形、障碍物以及自碰撞。然后，学习得到的动力学模型和MPPI控制器在闭环数据采集循环中运行，迭代提高模型精度和控制性能。本工作进一步引入了一种混合MPPI策略，将MPPI与转向运动基元相结合，以提高机动性。实验在MuJoCo中跨五个导航任务进行，包括墙壁障碍物、斜坡、狭窄走廊、低净空结构和复合3D障碍赛道。实验表明，在学习得到的GNN动力学模型上运行的混合MPPI控制器相比平面地面基线模型提高了预测精度，并且相比基于A*的重新规划和仅MPPI变体实现了更优的导航性能。结果表明，接触感知的学习动力学与基于采样的模型预测控制相结合，能够在复杂的接触丰富环境中实现稳健的张拉整体机器人导航。
英文摘要
Tensegrity robots offer lightweight, compliant mobility over challenging terrain but remain difficult to model and control due to complex contact-rich dynamics and partial observability. This work presents a model predictive path integral (MPPI) controller for a three-bar tensegrity robot driven by a learned graph neural network (GNN) dynamics model. This work first extends prior GNN-based models with a differentiable contact detection module. The extension allows the dynamics model to reason over non-horizontal planar terrains, obstacles, as well as self-collisions. Then, the learned dynamics model and the MPPI controller operate in a closed data-collection loop, iteratively improving model accuracy and control performance. This work further introduces a hybrid MPPI strategy that combines MPPI with turning motion primitives to improve maneuverability. Experiments are performed in MuJoCo across five navigation tasks, which include, wall obstacles, inclines, narrow corridors, low-clearance structures, and a composite 3D obstacle course. The experiments demonstrate that the hybrid MPPI controller operating over the learned GNN dynamics model improves predictive accuracy over a flat-ground baseline model and achieves superior navigation performance compared to $A^*$-based re-planning and MPPI-only variants. Results show that the contact-aware learned dynamics combined with the sampling-based model predictive control enable robust tensegrity navigation in complex, contact-rich environments.
