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Generation of Geodesics with Actor-Critic Reinforcement Learning to Predict Midpoints

Created by
  • Haebom

Author

Kazumi Kasaura

Outline

This paper presents a novel framework for finding the shortest path for all pairs on a manifold with a metric defined as infinitesimal. This framework generates shortest paths by recursively predicting midpoints. We propose an actor-critic approach for midpoint prediction, and experimentally demonstrate the validity of the proposed method, outperforming existing methods on several planning tasks, including path planning for agents with complex kinematics and motion planning for multi-degree-of-freedom robot arms.

Takeaways, Limitations

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Takeaways:
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An efficient and accurate solution to the shortest path planning problem on a manifold with an infinitesimal metric.
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Demonstrating applicability to various systems such as agents with complex kinematics and multi-degree-of-freedom robotic arms.
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An effective learning method for midpoint prediction using the actor-critic approach is presented.
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Limitations:
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The performance of the proposed method may depend significantly on the quality and quantity of training data.
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Further research is needed on scalability and computational cost for high-dimensional manifolds.
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Further verification of robustness and generalizability in real-world environments is needed.
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