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SpaceOctopus: Decentralized Multi-Agent Reinforcement Learning for Space Robot Motion Planning

Core Concepts
Inspired by octopuses, a decentralized multi-agent reinforcement learning paradigm is proposed for trajectory planning and base reorientation tasks of space robots.
The content introduces a decentralized multi-agent reinforcement learning framework inspired by octopuses for trajectory planning and base reorientation tasks of space robots. It addresses the challenges faced by multi-arm space robots in motion planning due to complex coupling properties. The framework decomposes the optimization problem into multiple sub-problems, enabling efficient control of different arms. Experiments demonstrate the robustness and adaptability of the proposed method under various scenarios, including disturbances, varying base masses, and arm failures. The approach allows for flexible reassembly of trained policies to accomplish composite tasks without retraining. I. Introduction Importance of space robots in autonomous maintenance. Need for efficient trajectory planning and base reorientation. Inspiration from octopuses' distributed control mechanism. II. Related Work Previous studies on controlling base and robotic arms in low-gravity environments. Challenges with traditional methods like inverse kinematics solutions. III. Preliminary Utilization of MuJoCo simulation environment for a four-arm free-floating space robot. Description of observation vectors for agents controlling different joints. IV. Methodology Formulation of trajectory planning and base reorientation problems as multi-agent RL problems. Hierarchical division of motor joints into single-arm, multi-arm, and task levels. V. Optimization Algorithm and Training Details Adoption of Centralized Training with Decentralized Execution structure using MAPPO algorithm. Hyperparameters during training detailed in a table format. VI. Experiments Comparison with centralized training showing improved stability and rewards under MARL paradigm. Ablation experiments comparing MAPPO with MADDPG baseline methods. Evaluation of anti-disturbance ability through joint disturbances and varying base masses. Recombination of policies to achieve mixed tasks successfully. VII. Conclusions Proposal of a decentralized multi-agent reinforcement learning paradigm inspired by octopuses for space robot motion planning.
この研究は、中国国家自然科学基金会の助成を受けています。 実験結果では、エンドエフェクターの位置誤差が0.04m以下であり、方向誤差が0.045rad以下であることが示されています。
"Through coordination among its brains, an octopus can grasp prey with some tentacles while others adjust its position." "Our contribution lies in developing a hierarchical and distributed motion planning framework inspired by octopuses."

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by Wenbo Zhao,S... at 03-14-2024

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海洋生物からインスピレーションを得た技術は他のロボット工学分野でも革新的な影響を与える可能性があります。例えば、「オウムガイ」(Parrotfish)から学んだ防護メカニズムは耐久性向上への応用が期待されます。「マグロ」(Tuna)から得られた水力力学原則は水中移動装置設計へ活かせるかもしれません。「ナマコ」(Sea Cucumber)から学んだ柔軟性は災害時支援用途へ展開可能です。