Accurate full-body motion prediction is essential for the safe, autonomous navigation of legged robots, enabling critical capabilities like limb-level collision checking in cluttered environments. Simplified kinematic models often fail to capture the complex, closed-loop dynamics of the robot and its low-level controller, limiting their predictions to simple planar motion. To address this, we present a learning-based observer-predictor framework that accurately predicts this motion. Our method features a neural observer with provable UUB guarantees that provides a reliable latent state estimate from a history of proprioceptive measurements. This stable estimate initializes a computationally efficient predictor, designed for the rapid, parallel evaluation of thousands of potential trajectories required by modern sampling-based planners. We validated the system by integrating our neural predictor into an MPPI-based planner on a Vision 60 quadruped. Hardware experiments successfully demonstrated effective, limb-aware motion planning in a challenging, narrow passage and over small objects, highlighting our system's ability to provide a robust foundation for high-performance, collision-aware planning on dynamic robotic platforms.

翻译：精确的全身运动预测对于足式机器人的安全自主导航至关重要，它使得在杂乱环境中进行肢体级碰撞检测等关键能力成为可能。简化的运动学模型往往无法捕捉机器人及其底层控制器的复杂闭环动力学特性，将其预测能力局限于简单的平面运动。为解决这一问题，我们提出了一种基于学习的观测器-预测器框架，能够准确预测此类运动。我们的方法采用具有可证明一致最终有界性保证的神经观测器，该观测器能够根据本体感知测量历史提供可靠的潜状态估计。这一稳定估计用于初始化计算高效的预测器，该预测器专为现代采样规划器所需的数千条潜在轨迹的快速并行评估而设计。我们通过将神经预测器集成至Vision 60四足机器人的MPPI规划器中验证了系统性能。硬件实验成功展示了在挑战性狭窄通道及小型障碍物场景中有效的肢体感知运动规划，凸显了本系统为动态机器人平台实现高性能碰撞感知规划提供稳健基础的能力。