用图神经网络提升张拉整体机器人运动控制的效率与稳定性
Morphology-Aware Graph Reinforcement Learning for Tensegrity Robot Locomotion
- 将机器人结构建模为图，通过图神经网络捕捉部件间耦合关系
- 在3种运动模式下实现更快学习、更高精度和更强抗干扰能力
- 策略可直接从仿真部署到实物，无需调参
张拉整体机器人由刚性杆和弹性缆索组成，具备高鲁棒性和可展开性，但其欠驱动且高度耦合的动力学特性给运动控制带来巨大挑战。本文提出一种形态感知强化学习框架，将图神经网络（GNN）融入软演员-评论家（SAC）算法中。通过将机器人的物理拓扑表示为图结构，所提出的GNN策略能够捕捉组件间的耦合关系，相较于传统多层感知机（MLP）策略，实现了更快、更稳定的训练。该方法在一台三杆张拉整体机器人上进行了验证，覆盖直线跟踪和双向转向三种运动范式。结果表明，该方法具有更高的样本效率，对噪声和刚度变化更具鲁棒性，并提升了轨迹精度。此外，学习到的策略可直接从仿真迁移到硬件，无需微调，实现了稳定的真实世界运动。这些结果展示了在强化学习中引入结构先验对张拉整体机器人控制的优势。
原文摘要 · Abstract (English)
Tensegrity robots combine rigid rods and elastic cables, offering high resilience and deployability but at the same time posing major challenges for locomotion control due to their underactuated and highly coupled dynamics. This paper introduces a morphology-aware reinforcement learning framework that integrates a graph neural network (GNN) into the Soft Actor-Critic (SAC) algorithm. By representing the robot's physical topology as a graph, the proposed GNN-based policy captures coupling among components, enabling faster and more stable learning than conventional multilayer perceptron (MLP) policies. The method is validated on a physical 3-bar tensegrity robot across three locomotion primitives, including straight-line tracking and bidirectional turning. It shows superior sample efficiency, robustness to noise and stiffness variations, and improved trajectory accuracy. Additionally, the learned policies transfer directly from simulation to hardware without fine-tuning, achieving stable real-world locomotion. These results demonstrate the advantages of incorporating structural priors into reinforcement learning for tensegrity robot control.
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