mlsanityData to 14 Oct 2026

Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans

arXiv 2602.23934 ↗First posted 27 Feb 2026cs.RO cs.LGcited by 1

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 27 references that are in the map. First posted on arXiv 27 Feb 2026.

Builds on

8 of 27 references.

References · 8

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2017Hindsight Experience Replaycited by 1,136
  3. 2016Successor Features for Transfer in Reinforcement Learningcited by 340
  4. 2019Structured agents for physical constructioncited by 50
  5. 2022Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learningcited by 22
  6. 2023Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planningcited by 8
  7. 2021Learning to Design and Construct Bridge without Blueprintcited by 6
  8. 2022Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resetscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Learning to build covering structures with continuous adjustmentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning framework
  2. 2024AssemblyComplete: 3D Combinatorial Construction with Deep Reinforcement Learning
  3. 2026Contact-Rich Robotic Manipulation in Construction via Zero-Shot Learning: A Diffusion Policy-Guided Adaptive Control ↗
  4. 2025Sample-Efficient Robot Skill Learning for Construction Tasks: Benchmarking Hierarchical Reinforcement Learning and Vision-Language-Action VLA Model
  5. 2026Visual Sim-to-Real Learning for Robotic Insertion under Geometric Variations: Application to Rebar Installation ↗
  6. 2024Subassembly to Full Assembly: Effective Assembly Sequence Planning through Graph-based Reinforcement Learning
  7. 2026EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly
  8. 2026Rollback-Free Stable Brick Structures Generation