Learning Equality Constraints for Motion Planning on Manifolds

2020/09/24 by Giovanni Sutanto, Isabel M. Rayas Fernández, Sutanto, Giovanni +7
Computer Science · Engineering · #AI-based Problem Solving and Planning #Computational Geometry (cs.CG) #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Robot Manipulation and Learning #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2009.11852

openalex publication_date 2020/09/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• A survey of robot learning from demonstration
• Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors
• Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
• On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration
• Learning the Solution Manifold in Optimization and Its Application in Motion Planning

Related

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• Constrained Motion Planning Networks X
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