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.

中文翻译：

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流形运动规划的学习等式约束

约束机器人运动规划是一种广泛用于解决复杂机器人任务的技术。我们考虑使用深度神经网络从演示中学习约束表示的问题，我们称之为等式约束流形神经网络 (ECoMaNN)。关键思想是学习适合集成到基于约束采样的运动规划器中的约束的水平集函数。通过将网络中的子空间与数据的子空间对齐来进行学习。我们将学习到的约束和分析描述的约束结合到规划器中，并使用基于投影的策略来找到有效点。我们评估 ECoMaNN 对约束流形的表示能力、其各个损失项的影响以及合并到规划器时产生的运动。