用神经哈密顿-雅可比方法实现多机械臂安全运动规划，实时高效且抗干扰。
NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning
- 基于神经哈密顿-雅可比理论学习安全值函数，捕捉最坏情况下的碰撞约束
- 构建去中心化轨迹优化框架，支持实时规划并保持高安全性
- 数据高效、可扩展，适用于不同机械臂系统，适合复杂协作场景
多机械臂安全运动规划因高维状态空间、耦合配置空间和复杂碰撞约束而极具挑战。集中式规划虽能协调所有机械臂，但存在可扩展性问题，难以满足实时需求。去中心化方法虽具可扩展性，现有基于深度学习的方法依赖准确行为预测或协调协议，在其他机械臂行为不可预测时易失效。为此，本文提出一种基于神经哈密顿-雅可比可达性（NeHMO）的学习方法，用于近似刻画最坏情况下机械臂间安全约束的安全值函数。进一步设计了基于学习的去中心化轨迹优化框架，实现实时规划。所提方法具有良好的可扩展性和数据效率，能跨多机械臂系统泛化，并在多个复杂多臂运动规划任务中优于现有先进基线方法。
原文摘要 · Abstract (English)
Safe multi-arm motion planning is a challenging problem in robotics due to its high dimensionality, coupled configuration space, and complex collision constraints. Centralized planners are capable of coordinating all arms but often face scalability limitations, restricting applicability in real-time settings. On the other hand, decentralized methods are scalable and recent deep learning-based approaches have shown promising results. However, these depend on accurate behavior prediction or coordination protocols and may fail when other arms act unpredictably. To address these challenges, we introduce a neural Hamilton-Jacobi Reachability (HJR) learning-based approach to approximate a safety value function that captures worst-case inter-arm safety constraints. We further develop a decentralized trajectory optimization framework that uses the learned HJR representation for real-time planning. The proposed method is scalable and data-efficient, generalizes across multi-manipulator systems, and outperforms state-of-the-art baselines on challenging multi-arm motion planning tasks.
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