用神经网络提升足式机器人运动预测精度，实现避障规划
Learning Neural Observer-Predictor Models for Limb-level Sampling-based Locomotion Planning
- 用神经观测器从传感器数据中稳定估计机器人状态
- 可快速并行评估上千条轨迹，适配采样规划器
- 在真实四足机器人上验证了复杂环境避障能力
精确的全身运动预测对足式机器人安全自主导航至关重要，支持在复杂环境中进行肢体级碰撞检测。传统简化运动模型难以捕捉机器人及其底层控制器的复杂闭环动力学，预测能力受限于平面运动。为此，我们提出一种基于学习的观测-预测框架，能准确预测实际运动。该方法包含一个具有理论保证的神经观测器，可从本体感知数据历史中提供可靠的隐状态估计；该稳定估计用于初始化一个计算高效的预测器，适用于现代采样式规划器所需的快速、并行轨迹评估（数千条）。我们将该神经预测器集成到基于MPPI的规划器中，在Vision 60四足机器人上进行了验证。硬件实验成功展示了在狭窄通道和小障碍物上实现有效的肢体感知运动规划，凸显系统为动态机器人平台提供高性能、防碰撞规划的稳健基础。
原文摘要 · Abstract (English)
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.
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