机器人无需蓝图，靠目标与障碍自主建稳定结构
Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans
- 用目标和障碍定义任务，不依赖固定施工计划
- 在15个2D积木任务中成功构建稳定结构，真实机器人验证可行
- 适合应对环境不确定性和施工噪声的现实场景
本文提出一种新型自主机器人装配框架，可在无预先设定建筑蓝图的情况下构建稳定结构。任务通过目标和障碍定义，使系统能灵活应对环境不确定性与建造过程中的变化。采用基于后继特征的深度Q学习强化学习策略作为决策核心。以15个离散积木构建的2D基准任务为验证，结合真实世界闭环机器人系统实验，证明了该方法的可行性及其对建造噪声的鲁棒性。结果表明，该框架为现实环境中更适应性强、更可靠的机器人建造提供了有前景的方向。
原文摘要 · Abstract (English)
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.
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