基于不完全领域知识网格表示和体素-网格估计的布料操作规划
Cloth Manipulation Planning on Basis of Mesh Representations with Incomplete Domain Knowledge and Voxel-to-Mesh Estimation
摘要：
我们考虑机器人布料操作的开放目标规划问题。我们的系统的核心是一个神经网络训练作为一个前向模型的布料行为下操纵，规划通过反向传播执行。我们引入了一个基于神经网络的例程，用于从体素输入估计网格表示，并在内部以网格格式执行规划。我们解决的问题，规划与不完整的领域知识，通过一个明确的认知不确定性信号。该信号是根据前向模型网络的两个实例之间的预测偏差计算的，并用于避免规划期间的认知不确定性。最后，我们介绍了逻辑处理限制的把握点的一组离散的候选人，以适应机器人硬件施加的可抓取性约束。我们评估系统的网格估计，预测和规划能力模拟布序列的一到三个操作。比较实验证实，估计网格的基础上的规划，提高精度相比，基于体素的规划，和认知不确定性的避免提高性能的条件下，不完整的领域知识。规划的时间成本是几秒钟。我们还提出了机器人硬件的定性结果。
英文摘要：
We consider the problem of open-goal planning for robotic cloth manipulation. Core of our system is a neural network trained as a forward model of cloth behaviour under manipulation, with planning performed through backpropagation. We introduce a neural network-based routine for estimating mesh representations from voxel input, and perform planning in mesh format internally. We address the problem of planning with incomplete domain knowledge by means of an explicit epistemic uncertainty signal. This signal is calculated from prediction divergence between two instances of the forward model network and used to avoid epistemic uncertainty during planning. Finally, we introduce logic for handling restriction of grasp points to a discrete set of candidates, in order to accommodate graspability constraints imposed by robotic hardware. We evaluate the system's mesh estimation, prediction, and planning ability on simulated cloth for sequences of one to three manipulations. Comparative experiments confirm that planning on basis of estimated meshes improves accuracy compared to voxel-based planning, and that epistemic uncertainty avoidance improves performance under conditions of incomplete domain knowledge. Planning time cost is a few seconds. We additionally present qualitative results on robot hardware.
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