[Submitted on 27 Feb 2026 (v1), last revised 2 Nov 2026 (this version, v3)]
Abstract: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.
Submission history
From: Jingwen Wang [view email]
[v1] Fri, 27 Feb 2026 11:31:49 UTC (1,511 KB)
[v2] Wed, 30 Sep 2026 10:19:52 UTC (2,376 KB)
[v3] Mon, 2 Nov 2026 08:59:52 UTC (2,388 KB)
