Effective motion planning in high dimensional spaces is a long-standing open problem in robotics. One class of traditional motion planning algorithms corresponds to potential-based motion planning. An advantage of potential based motion planning is composability -- different motion constraints can be easily combined by adding corresponding potentials. However, constructing motion paths from potentials requires solving a global optimization across configuration space potential landscape, which is often prone to local minima. We propose a new approach towards learning potential based motion planning, where we train a neural network to capture and learn an easily optimizable potentials over motion planning trajectories. We illustrate the effectiveness of such approach, significantly outperforming both classical and recent learned motion planning approaches and avoiding issues with local minima. We further illustrate its inherent composability, enabling us to generalize to a multitude of different motion constraints.

翻译：在高维空间中进行有效的运动规划是机器人学中长期存在的开放性问题。一类传统的运动规划算法对应于基于势能的运动规划方法。基于势能的运动规划具有可组合性优势——通过叠加相应的势能函数，可以轻松组合不同的运动约束。然而，从势能函数构造运动路径需要在构型空间的势能场中进行全局优化，这往往容易陷入局部极小值。我们提出了一种学习基于势能的运动规划的新方法，通过训练神经网络来捕获和学习运动规划轨迹上易于优化的势能函数。我们展示了该方法的有效性，其性能显著优于经典方法和近期基于学习的运动规划方法，并成功避免了局部极小值问题。我们进一步证明了其固有的可组合性，使其能够泛化到多种不同的运动约束场景。