用大规模仿真训练通用运动规划模型，实现实时安全路径生成
Neural MP: A Generalist Neural Motion Planner
- 通过仿真构建复杂场景，从专家规划器收集数据并提炼为通用策略
- 在4个真实环境的64项任务中，成功率比现有方法提升79%
- 适合需要快速、安全运动规划的机器人部署场景
当前运动规划方法对每个新问题都从头计算，耗时长且资源消耗大。在复杂杂乱场景中，传统方法常需数分钟才能生成解，而人类凭借经验可在数秒内准确安全到达目标。我们借鉴人类经验，通过大规模数据驱动学习解决运动规划问题。方法在仿真中构建大量复杂场景，利用运动规划器生成专家数据，再将其蒸馏为可实时响应的通用策略，并结合轻量优化获得可用于真实世界的安全路径。我们在四个不同环境中对64个随机姿态、场景与障碍物的任务进行真实世界评估，结果显示，相比最先进的采样、优化和基于学习的规划方法，成功率分别提升23%、17%和79%。视频结果见 mihdalal.github.io/neuralmotionplanner
原文摘要 · Abstract (English)
The current paradigm for motion planning generates solutions from scratch for every new problem, which consumes significant amounts of time and computational resources. For complex, cluttered scenes, motion planning approaches can often take minutes to produce a solution, while humans are able to accurately and safely reach any goal in seconds by leveraging their prior experience. We seek to do the same by applying data-driven learning at scale to the problem of motion planning. Our approach builds a large number of complex scenes in simulation, collects expert data from a motion planner, then distills it into a reactive generalist policy. We then combine this with lightweight optimization to obtain a safe path for real world deployment. We perform a thorough evaluation of our method on 64 motion planning tasks across four diverse environments with randomized poses, scenes and obstacles, in the real world, demonstrating an improvement of 23%, 17% and 79% motion planning success rate over state of the art sampling, optimization and learning based planning methods. Video results available at mihdalal.github.io/neuralmotionplanner
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