Generation of Geodesics with Actor-Critic Reinforcement Learning to Predict Midpoints
Kazumi Kasaura
TL;DR
This paper presents an actor-critic reinforcement learning framework to generate geodesics by recursively predicting midpoints, improving path planning on complex manifolds and robotic systems.
Contribution
It introduces a novel actor-critic method for midpoint prediction in geodesic generation, with theoretical proof and superior experimental performance.
Findings
Outperforms existing path planning methods
Effective on complex kinematic agents
Applicable to multi-DOF robot arms
Abstract
To find the shortest paths for all pairs on manifolds with infinitesimally defined metrics, we introduce a framework to generate them by predicting midpoints recursively. To learn midpoint prediction, we propose an actor-critic approach. We prove the soundness of our approach and show experimentally that the proposed method outperforms existing methods on several planning tasks, including path planning for agents with complex kinematics and motion planning for multi-degree-of-freedom robot arms.
Peer Reviews
Decision·Submitted to ICLR 2024
The paper presents a distinct approach to generating geodesics in reinforcement learning environments via a "midpoint tree" algorithm. The theoretical underpinnings are robust, complemented by a thorough experimental evaluation. The articulation is commendable, with the authors elucidating complex ideas succinctly. This work's originality and potential applicability are clear, indicating its prospective value in advancing research within reinforcement learning and robotics.
The paper lacks a broader range of examples to demonstrate the applicability of the method to more common robotic tasks like locomotion and manipulation planning. The experimental results, while encouraging, do not showcase a significant advantage over existing methods, raising questions about the practical benefits of the proposed approach. It requires certain assumptions that may not be present in typical robotic environments, such as the need for global coordinate systems and uniform sampling
The paper is well-written and the method is well-motivated. The effectiveness of the proposed method is supported both theoretically and empirically. The generated waypoints with equal distances would be more useful than that of the previous method.
The novelty of the paper is not prominent compared to its base methods. The experimental setting is a bit simplified. In section 6, the authors propose a penalty term to be added to deal with obstacles. Wondering how easy is it to generalize the proposed method to environments with obstacles. The experiment results do not show clear performance improvements of the proposed method.
The overall writing is rigorous, principled and looks solid work. But I am not sure of its significance.
Perhaps the motivation of this work can be better written. As the authors pointed out in their experiments, generating geodesic (path planning) can be simply tackled by RL by specifying a reward function related to the difference in distance. But it may have instability or other issue compared to path planning approaches. Could you give some explanation why Car-like task favors your approach, while Matsumoto task not? The experiment scope is a bit narrow as only two toy tasks are evaluated.
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Taxonomy
TopicsDesign Education and Practice · BIM and Construction Integration · Manufacturing Process and Optimization