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

arXiv: https://arxiv.org/abs/2602.23934 · HTML: https://mlsanity.com/paper/2602.23934/

## Key facts

- Cited by 1 papers 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Successor Features for Transfer in Reinforcement Learning](https://mlsanity.com/paper/1606.05312/) (2016)
- [Structured agents for physical construction](https://mlsanity.com/paper/1904.03177/) (2019)
- [Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning](https://mlsanity.com/paper/2203.13733/) (2022)
- [Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning](https://mlsanity.com/paper/2309.02547/) (2023)
- [Learning to Design and Construct Bridge without Blueprint](https://mlsanity.com/paper/2108.02439/) (2021)
- [Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resets](https://mlsanity.com/paper/2204.05509/) (2022)

## Built on by (most cited first, 1 of 1)

- [Learning to build covering structures with continuous adjustments](https://mlsanity.com/paper/2609.08669/) (2026)

## 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.