提出新方法让多机器人在任务约束下安全协作，无需预设他人行为。
Manifold-constrained Hamilton-Jacobi Reachability Learning for Decentralized Multi-Agent Motion Planning
- 基于流形约束的哈密顿-雅可比可达性学习，捕捉任务相关安全条件。
- 在多种任务场景中表现优于现有方法，支持高维多机协同规划。
- 适合需动态避障与任务约束的机器人系统，如服务机器人持物行走。
在任务诱导约束下实现安全的多智能体运动规划（MAMP）是机器人领域的关键挑战。许多现实场景要求机器人在动态环境中导航，同时满足由任务施加的流形约束。例如，服务机器人在运送杯子时必须保持杯身直立，同时避开人或其他机器人。尽管近期在高维系统去中心化MAMP方面取得进展，但融入流形约束仍具难度。为此，本文提出一种流形约束下的哈密顿-雅可比可达性（HJR）学习框架，用于去中心化MAMP。该方法求解带有流形约束的HJR问题，以捕获任务感知的安全条件，并将其集成到去中心化轨迹优化规划器中。这使机器人能够在不假设其他智能体策略的前提下，生成既安全又任务可行的运动计划。该方法可泛化至多样化的流形约束任务，并有效扩展至高维多机器人操作问题。实验表明，本方法在性能上超越现有约束型运动规划器，且运行速度满足真实应用需求。视频演示见 https://youtu.be/RYcEHMnPTH8。
原文摘要 · Abstract (English)
Safe multi-agent motion planning (MAMP) under task-induced constraints is a critical challenge in robotics. Many real-world scenarios require robots to navigate dynamic environments while adhering to manifold constraints imposed by tasks. For example, service robots must carry cups upright while avoiding collisions with humans or other robots. Despite recent advances in decentralized MAMP for high-dimensional systems, incorporating manifold constraints remains difficult. To address this, we propose a manifold-constrained Hamilton-Jacobi reachability (HJR) learning framework for decentralized MAMP. Our method solves HJR problems under manifold constraints to capture task-aware safety conditions, which are then integrated into a decentralized trajectory optimization planner. This enables robots to generate motion plans that are both safe and task-feasible without requiring assumptions about other agents' policies. Our approach generalizes across diverse manifold-constrained tasks and scales effectively to high-dimensional multi-agent manipulation problems. Experiments show that our method outperforms existing constrained motion planners and operates at speeds suitable for real-world applications. Video demonstrations are available at https://youtu.be/RYcEHMnPTH8 .
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