Neural MP: A Generalist Neural Motion Planner
This addresses the problem of slow and resource-intensive motion planning for robotics and autonomous systems, offering a more efficient solution.
The paper tackles the inefficiency of traditional motion planning by developing a data-driven neural motion planner that learns from expert data in simulation and combines it with lightweight optimization for real-world deployment. It demonstrates improvements of 23%, 17%, and 79% in success rates over state-of-the-art methods across diverse environments.
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