This paper describes Motion Planning Networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems. MPNet uses neural networks to learn general near-optimal heuristics for path planning in seen and unseen environments. It takes environment information such as raw point-cloud from depth sensors, as well as a robot's initial and desired goal configurations and recursively calls itself to bidirectionally generate connectable paths. In addition to finding directly connectable and near-optimal paths in a single pass, we show that worst-case theoretical guarantees can be proven if we merge this neural network strategy with classical sample-based planners in a hybrid approach while still retaining significant computational and optimality improvements. To train the MPNet models, we present an active continual learning approach that enables MPNet to learn from streaming data and actively ask for expert demonstrations when needed, drastically reducing data for training. We validate MPNet against gold-standard and state-of-the-art planning methods in a variety of problems from 2D to 7D robot configuration spaces in challenging and cluttered environments, with results showing significant and consistently stronger performance metrics, and motivating neural planning in general as a modern strategy for solving motion planning problems efficiently.

中文翻译：

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运动规划网络：弥合基于学习和经典运动规划器之间的差距

本文介绍了运动规划网络 (MPNet)，这是一种计算效率高、基于学习的神经规划器，用于解决运动规划问题。MPNet 使用神经网络来学习在可见和不可见环境中进行路径规划的一般近似最优启发式方法。它从深度传感器获取原始点云等环境信息，以及机器人的初始和期望目标配置，并递归调用自身以双向生成可连接路径。除了在单次通过中找到可直接连接和接近最优的路径之外，我们还表明，如果我们将这种神经网络策略与经典的基于样本的规划器以混合方法合并，同时仍然保留大量的计算和计算能力，则可以证明最坏情况的理论保证。优化改进。要训​​练 MPNet 模型，我们提出了一种主动的持续学习方法，使 MPNet 能够从流数据中学习，并在需要时主动请求专家演示，从而大大减少了用于训练的数据。我们在具有挑战性和混乱的环境中从 2D 到 7D 机器人配置空间的各种问题中，针对黄金标准和最先进的规划方法验证 MPNet，结果显示出显着且持续增强的性能指标，并在一般作为有效解决运动规划问题的现代策略。