設計図なしで安定構造物を自律組立するロボットの学習
Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans
強化学習により、事前設計図に頼らず目標と障害物だけから安定したブロック構造物を組み立てる自律ロボット組立フレームワークを提案し、実機実験で有効性を示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Jingwen Wang, Johannes Kirschner, Paul Rolland, Luis Salamanca, Stefana Parascho
分類: cs.RO, cs.LG
原文アブストラクト
This paper presents a novel autonomous robotic assembly framework for constructing stable structures without relying on predefined architectural blueprints. Instead of following fixed plans, construction tasks are defined through targets and obstacles, allowing the system to adapt more flexibly to environmental uncertainty and variations during the building process. A reinforcement learning (RL) policy, trained using deep Q-learning with successor features, serves as the decision-making component. As a proof of concept, we evaluate the approach on a benchmark of 15 2D robotic assembly tasks of discrete block construction. Experiments using a real-world closed-loop robotic setup demonstrate the feasibility of the method and its ability to handle construction noise. The results suggest that our framework offers a promising direction for more adaptable and robust robotic construction in real-world environments.
