CoRL-MPPI: 通过可学习行为增强MPPI以实现高效且可证明安全的多机器人避障
CoRL-MPPI: Enhancing MPPI With Learnable Behaviours For Efficient And Provably-Safe Multi-Robot Collision Avoidance
AI总结：
CoRL-MPPI通过结合协同强化学习与MPPI，提升多机器人避障的效率与安全性。
摘要：
去中心化的避障是可扩展的多机器人系统的核心挑战之一。解决这个问题的一种有前途的方法是模型预测路径积分（MPPI）--一个能够自然处理任意运动模型并提供强理论保证的框架。然而，在实践中，基于MPPI的控制器可能会提供次优轨迹，因为其性能严重依赖于无信息的随机采样。在本工作中，我们引入了CoRL-MPPI，一种将协同强化学习与MPPI相结合的新型方法，以解决这一限制。我们在仿真中训练了一个动作策略（近似为深度神经网络），学习局部协作避障行为。该学习的策略随后被嵌入到MPPI框架中，以引导其采样分布，使其偏向更智能和协作的动作。值得注意的是，CoRL-MPPI保留了常规MPPI的所有理论保证。我们在密集、动态的仿真环境中评估了我们的方法，与ORCA、BVC、RL-RVO-NAV和经典MPPI等最先进的基线进行比较。我们的结果表明，CoRL-MPPI显著提高了导航效率（通过成功率和完成时间衡量）和安全性，实现了敏捷且稳健的多机器人导航。
英文摘要：
Decentralized collision avoidance is a core challenge for scalable multi-robot systems. A promising approach to this problem is Model Predictive Path Integral (MPPI) control - a framework that naturally handles arbitrary motion models and provides strong theoretical guarantees. Still, in practice an MPPI-based controller may produce suboptimal trajectories because its performance relies heavily on uninformed random sampling. We introduce CoRL-MPPI, a fusion of Cooperative Reinforcement Learning and MPPI that addresses this limitation. We train an action policy, approximated by a deep neural network, in simulation to learn local cooperative collision-avoidance behaviors. This learned policy is then embedded into the MPPI framework to guide its sampling distribution, biasing it toward more intelligent and cooperative actions in scenarios that may differ substantially from those used during training. Moreover, CoRL-MPPI preserves the theoretical guarantees of regular MPPI. We evaluate our approach in dense, dynamic setups against classical and learning-based state-of-the-art baselines. Our results demonstrate that CoRL-MPPI outperforms competing methods and significantly improves navigation efficiency, measured by success rate and delay, as well as safety, enabling agile and robust multi-robot navigation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
