Learning Continuous Cost-to-Go Functions for Non-holonomic Systems

• URL: http://arxiv.org/abs/2103.11168v1
• Date: Sat, 20 Mar 2021 12:31:08 GMT
• Title: Learning Continuous Cost-to-Go Functions for Non-holonomic Systems
• Authors: Jinwook Huh, Daniel D. Lee and Volkan Isler
• Abstract summary: This paper presents a supervised learning method to generate continuous cost-to-go functions of non-holonomic systems. We show that our network can generate near-optimal trajectories for non-holonomic systems while avoiding obstacles.
• Score: 40.443409760112395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a supervised learning method to generate continuous cost-to-go functions of non-holonomic systems directly from the workspace description. Supervision from informative examples reduces training time and improves network performance. The manifold representing the optimal trajectories of a non-holonomic system has high-curvature regions which can not be efficiently captured with uniform sampling. To address this challenge, we present an adaptive sampling method which makes use of sampling-based planners along with local, closed-form solutions to generate training samples. The cost-to-go function over a specific workspace is represented as a neural network whose weights are generated by a second, higher order network. The networks are trained in an end-to-end fashion. In our previous work, this architecture was shown to successfully learn to generate the cost-to-go functions of holonomic systems using uniform sampling. In this work, we show that uniform sampling fails for non-holonomic systems. However, with the proposed adaptive sampling methodology, our network can generate near-optimal trajectories for non-holonomic systems while avoiding obstacles. Experiments show that our method is two orders of magnitude faster compared to traditional approaches in cluttered environments.

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