论文标题
学习对流动的运动计划的平等限制
Learning Equality Constraints for Motion Planning on Manifolds
论文作者
论文摘要
受限的机器人运动计划是一种广泛使用的技术，可以解决复杂的机器人任务。我们考虑通过深层神经网络从演示中学习的学习表示的问题，我们称之为平等约束歧管神经网络（Ecomann）。关键思想是学习适合集成到基于采样的运动计划者中的约束的级别函数。学习通过将网络中的子空间与数据子空间保持一致。我们将学习的约束结合在一起，并分析地描述了计划者，并使用基于投影的策略来查找有效要点。我们评估了Ecomann的约束歧管的表示能力，其个体损失条款的影响以及将其纳入计划者时产生的动议。
Constrained robot motion planning is a widely used technique to solve complex robot tasks. We consider the problem of learning representations of constraints from demonstrations with a deep neural network, which we call Equality Constraint Manifold Neural Network (ECoMaNN). The key idea is to learn a level-set function of the constraint suitable for integration into a constrained sampling-based motion planner. Learning proceeds by aligning subspaces in the network with subspaces of the data. We combine both learned constraints and analytically described constraints into the planner and use a projection-based strategy to find valid points. We evaluate ECoMaNN on its representation capabilities of constraint manifolds, the impact of its individual loss terms, and the motions produced when incorporated into a planner.
