利用神经可行性检查加速集成任务和运动规划
Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking
摘要：
随着机器人在工业中发挥越来越重要的作用，人们对它们在日常生活任务中的应用的期望也越来越高。机器人需要执行由几个需要完成的子任务组成的长期任务。任务和运动规划（TAMP）提供了一个分层的框架来处理操作任务的顺序性质，通过交错符号任务规划器来生成可能的动作序列，运动规划器检查几何世界中的运动学可行性，如果满足几个约束，则生成机器人轨迹，例如，从一个状态到另一个状态的无碰撞轨迹。因此，关于任务计划的几何基础的推理由运动计划器接管。然而，运动规划是计算密集的，并且是可用的，因为可行性检查器将TAMP方法铸造成不适用于现实世界场景。在本文中，我们介绍了神经可行性分类器（NFC），一个简单而有效的视觉启发式分类的可行性建议的行动在TAMP。即，NFC将识别任务规划器的不可行动作而不需要昂贵的运动规划，因此减少了多步操纵任务中的规划时间。NFC通过卷积神经网络（CNN）将机器人工作空间的图像编码为特征图。我们使用TAMP问题的模拟数据训练NFC，并基于IK可行性检查对实例进行标记。我们在不同的模拟操作任务的实证结果表明，我们的NFC推广到整个机器人的工作空间，即使在多个障碍物的场景具有很高的预测精度。当与最先进的集成TAMP相结合时，我们的NFC增强了其性能，同时减少了规划时间。
英文摘要：
As robots play an increasingly important role in the industrial, the expectations about their applications for everyday living tasks are getting higher. Robots need to perform long-horizon tasks that consist of several sub-tasks that need to be accomplished. Task and Motion Planning (TAMP) provides a hierarchical framework to handle the sequential nature of manipulation tasks by interleaving a symbolic task planner that generates a possible action sequence, with a motion planner that checks the kinematic feasibility in the geometric world, generating robot trajectories if several constraints are satisfied, e.g., a collision-free trajectory from one state to another. Hence, the reasoning about the task plan's geometric grounding is taken over by the motion planner. However, motion planning is computationally intense and is usability as feasibility checker casts TAMP methods inapplicable to real-world scenarios. In this paper, we introduce neural feasibility classifier (NFC), a simple yet effective visual heuristic for classifying the feasibility of proposed actions in TAMP. Namely, NFC will identify infeasible actions of the task planner without the need for costly motion planning, hence reducing planning time in multi-step manipulation tasks. NFC encodes the image of the robot's workspace into a feature map thanks to convolutional neural network (CNN). We train NFC using simulated data from TAMP problems and label the instances based on IK feasibility checking. Our empirical results in different simulated manipulation tasks show that our NFC generalizes to the entire robot workspace and has high prediction accuracy even in scenes with multiple obstructions. When combined with state-of-the-art integrated TAMP, our NFC enhances its performance while reducing its planning time.
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