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

arXiv:2602.23934 · cs.RO, cs.LG · Submitted 2026-02-27 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: Today's paper: "Learning to Build".

Dev: This paper presents a novel autonomous robotic assembly framework designed to construct stable structures without relying on predefined architectural blueprints, addressing the limitations of rigid planning in dynamic construction environments.

Rosa: First, who's behind it and why it matters.

Title and authors: Rosa: So we're moving on to discussing "Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans." We talked about the title suggesting a shift away from rigid plans, and now let's look at who actually wrote this paper. The authors are Jingwen Wang, Johannes Kirschner, Paul Rolland, and Luis Salamanca.

Dev: I'm familiar with some of these names in the control engineering circles; I'm curious if their expertise aligns well with the reinforcement learning and the physical assembly aspect of this project.

Taro: As an autonomy researcher, I see a strong alignment because they are tackling how to give robots genuine flexibility when things aren't exactly as expected in their working environment.

Rosa: That’s right, Taro; this paper is focused on building a framework that lets the AI construct stable structures without relying on predefined architectural blueprints. It’s about creating a system that can interpret abstract goals, which is a significant conceptual step.

Dev: From an engineering standpoint, having researchers with backgrounds in both vision and control is crucial when dealing with something as complex as physical assembly under uncertainty.

Taro: I think the combination of expertise in deep learning and robotic control allows them to propose a method that isn't just theoretically interesting but also has a path toward practical application.

Rosa: Precisely, they are proposing an autonomous robotic assembly framework where construction tasks are defined by targets and obstacles, which is a departure from the traditional plan-driven workflows we see in much of current research.

Dev: So, the main implication here is that we're moving towards a system that doesn't need explicit instruction on every single placement; it just needs to know what the final structure should look like in terms of targets and constraints.

Taro: It’s about enabling construction strategies to be learned through relational reasoning rather than being hard-coded into a specific structural form, which is a big shift for autonomy.

Rosa: And that's exactly what they are achieving by using reinforcement learning to drive the decision-making core of this system.

The paper's summary: Dev: We’ve covered the authors and the title, so now let's look at what the paper actually says in terms of a summary of "Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans." Essentially, we need to break down how this framework works in simple terms.

Rosa: The core summary is that they present a novel autonomous robotic assembly framework for constructing stable structures without needing predefined architectural blueprints. Instead of following fixed plans, construction tasks are defined through targets and obstacles, which allows the system to adapt more flexibly during the building process.

Dev: So, to put that in simpler terms, it means the robot doesn't just follow a sequence of commands; it figures out *how* to build by looking at what needs to be done rather than just following a fixed list.

Taro: That flexibility comes from using an RL policy trained using deep Q-learning with successor features, which is the decision-making core that enables the robot to adapt its actions based on construction progress and real time conditions.

Rosa: Right, and they use image-based feature representations for states, actions, and tasks to give the RL model rich input about what's happening on site.

Dev: I’m trying to understand how those features translate into actionable decisions; are we talking about a high-level map of the environment or something more granular?

Taro: The paper suggests that the task information is embedded via task features, specifically encoding obstacle locations and target locations, which gives the policy a direct understanding of where it needs to go.

Rosa: And they guide this process using a dense, shaped reward function where placing a block earns a reward calculated as an inner product between the action features and a reward component derived from the targets.

Dev: That inner product formulation sounds like it’s directly tying the success of an action to its proximity to the desired construction goal. Is that how they ensure efficiency?

Taro: It's designed to guide assembly toward targets efficiently, and they also use successor features to decompose the state-action value function into task and reward components.

Rosa: So, in short, this framework allows the agent to learn by predicting future states based on current actions and task goals, which is a really clever way to incorporate the goal directly into the learning process.

The paper's improvements: Dev: Now that we understand how they work—the next part of "Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans" is discussing what they suggest for improvements. What are the authors saying needs to be done?

Rosa: They are pointing out that their current approach, while a proof of concept, could be improved by moving beyond simple 2D/simple features toward deeper geometric reasoning in their state and action encoding.

Dev: So they want more than just basic visual inputs; they want the AI to understand the geometry more deeply, perhaps incorporating physics-informed constraints directly into the policy gradient instead of relying solely on binary stability checks.

Taro: I agree with that direction; moving beyond simple shape recognition to truly understanding structural integrity through explicit physics modeling is where I think this system can really gain its edge in handling complex, unforeseen situations.

Rosa: Furthermore, they suggest integrating multi-agent collaborative construction strategies as a way to scale the framework for more complex builds, which would be useful for tackling larger projects where one robot can't handle everything alone.

Dev: Collaboration sounds like it introduces new challenges regarding communication and coordination latency; I need to think about how they’d manage that in a practical setup.

Taro: The paper also suggests developing a robust sim-to-real adaptation module to explicitly model noise and uncertainty during training, which is critical for achieving better success rates when deployed in physical environments.

Rosa: So the authors are suggesting that explicit modeling of noise, rather than letting the system implicitly handle it, is necessary for more reliable real-world deployment.

Dev: That sounds like they’re addressing a major gap where simulation performance doesn't perfectly map to physical reality without more explicit modeling.

Conclusion: Rosa: So we've covered the summary, the improvements, and now it’s time for our wrap-up on "Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans." In essence, this paper proposes a framework where a single RL policy solves multiple construction tasks by leveraging image-based successor features to decompose rewards into task- and action-specific components.

Dev: It's clear that the potential here is in creating an AI that can design novel structures based on abstract goals rather than just assembling pre-defined ones, which is a really exciting direction for general robotic construction.

Taro: I think the implication is that we're moving toward agents capable of generating complex topologies by interpreting those high-level geometric goals and optimizing for efficiency.

Rosa: That means we're looking at a system that acts more like an autonomous architectural design partner, capable of handling the inherent unpredictability of real-world construction sites.

Dev: The next step is definitely focusing on how to make that abstraction robust enough to handle the physical execution loop without introducing significant latency.

Taro: I'm looking forward to seeing how they integrate physics-informed constraints and collaborative strategies into their future work, as those are where true real-world robustness will be tested.

Lab of Creative Computation, Ecole Polytechnique Fédérale de Lausanne · Swiss Data Science Center

cs.RO, cs.LG

Submitted: 2026-02-27

Updated: 2026-09-30

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 83/100

The gist: This paper presents a novel autonomous robotic assembly framework designed to construct stable structures without relying on predefined architectural blueprints, addressing the limitations of rigid

Key concepts

Autonomous Robotic Assembly Framework: A novel system designed for robots to construct stable structures without needing fixed architectural blueprints. Instead of following rigid plans, the framework allows the robot to adapt its building process based on real-time targets and obstacles.
Reinforcement Learning (RL) with Deep Q-learning: The decision-making core of the system, trained using RL with successor features. This allows the robot to adapt its actions based on construction progress and real-time conditions by predicting future states from current actions.
Task Features: Features embedded in the task information that encode obstacle locations and target locations. These features give the reinforcement learning policy a direct understanding of where it needs to build, guiding its decisions.

Terminology

Reinforcement Learning — 17× in this paper · explained in 4 episodes
Action Space — 10× in this paper · explained in 1 episode
Q-learning — 6× in this paper · explained in 3 episodes
Adaptation — 5× in this paper · explained in 1 episode
Core — 5× in this paper · explained in 2 episodes
Reinforcement Learning (RL) — 4× in this paper · explained in 25 episodes
Alignment — 3× in this paper · explained in 1 episode
Benchmark — 3× in this paper · explained in 2 episodes
Reward Function — 3× in this paper · explained in 2 episodes
Robustness — 3× in this paper · explained in 5 episodes
Adaptability — 2× in this paper · explained in 1 episode
Generalization — 2× in this paper · explained in 13 episodes
Interpretability — 2× in this paper · explained in 15 episodes
Physics-Informed Constraints — 2× in this paper · explained in 2 episodes
Abstraction — 1× in this paper · explained in 1 episode
Adam — 1× in this paper · explained in 2 episodes
Calibration — 1× in this paper · explained in 7 episodes
Closed-Loop System — 1× in this paper · explained in 4 episodes
Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
Loss Function — 1× in this paper · explained in 2 episodes
Sim-to-Real Gap — 1× in this paper · explained in 6 episodes
Structural Integrity — 1× in this paper · explained in 3 episodes

Summary

This paper presents a novel autonomous robotic assembly framework designed to construct stable structures without relying on predefined architectural blueprints, addressing the limitations of rigid planning in dynamic construction environments. By defining construction tasks through abstract targets and obstacles, the system enables flexible adaptation to environmental uncertainties and variations during building. This approach is crucial for moving beyond traditional plan-driven workflows in real-world settings where on-site conditions are unpredictable.

Problem Formulation

The paper formalizes the construction task by defining it through four elements: the construction space, targets, obstacles, and blocks. The goal is for the robot to build a discrete structure by using available blocks to connect the ground to the targets while avoiding obstacles. This formulation allows for a wide range of structural forms simply by varying the configuration of targets and obstacles. Specifically:

Unit Blocks: The system uses square and trapezoidal blocks as basic construction units.
Construction Space: Defined by dimensions, spanning x ∈ [−5, 5] and z ∈ [0, 10], with a floor at z=0 as the foundation.
Targets and Obstacles: Targets are defined as 2D points, while obstacles are defined as 2D square regions.
Construction Step: A placement action is valid only if it does not overlap with existing blocks, the ground, or obstacles, and results in a structure that remains stable according to the RigidBlock Equilibrium (RBE) method.

Methodology: Goal-Conditioned Reinforcement Learning

The core decision-making component is a goal-conditioned reinforcement learning (RL) model. The task is modeled as having a state space S, an action space A, and a task space T. To enable generalization across different tasks, the framework employs image-based feature representations for states, actions, and tasks:

Action Features: Action features ϕ(At) are defined as a binary image indicating the block shape and placement associated with action At, with a dimension of 64x64.
State Features: The state features ψ(St) are defined as the sum over corresponding action features, encoding state–action transitions using a simple additive update.
Task Features: Task information is embedded via task features ξ(T), where the first channel encodes obstacle locations and the second encodes target locations.

Reward Formulation and Successor Features

To guide assembly toward targets efficiently, a dense, shaped reward function is introduced that decomposes the reward into task-dependent and action-dependent components. The reward for placing a block is computed as an inner product: r(A, T) = ϕ(A)⊤ρ(T), where ρ(T) is constructed by convolving target locations with a Gaussian kernel. Crucially, the framework utilizes successor features, which are defined to decompose the state-action value function as Qπ(S, A, T) = Ψπ(S, A, T)⊤ρ(T). This allows the agent to learn by predicting future states based on current actions and task goals.

Learning Algorithm and Training

The learning algorithm is a variant of Deep Q-Learning (DQN) that learns to predict successor features rather than directly approximating Qπ. The policy is parametrized by an image-to-image U-Net that takes the state features ψ(S), action features ϕ(A), and task features ξ(T) as inputs. The training process involves running the policy on all tasks, storing transitions in a replay buffer, and iteratively updating the target network using a loss function L(θ) = 1/B Σ P j=1 Y j − Ψθ(S j, A j, T j) squared. This formulation allows for multi-task learning because a single policy can generalize across different objectives, and the shared image representation supports translational equivariance.

Validation and Real-World Deployment

The framework is validated through two main avenues: simulation and a real-world closed-loop robotic assembly process. In simulation, the policy eventually solves 14 out of 15 tasks after training for 50 episodes. In the real world, deployment on a system equipped with an ABB CRB 15000 arm and Zivid structured-light camera demonstrates feasibility, completing 12 of 15 tasks on the first attempt. The closed-loop feedback loop allows the system to adapt to physical construction noise, showing that even without explicit modeling of noise, the policy can reliably accomplish 80% of tasks, despite failures arising from issues like accumulated placement errors or structural instability under perturbations.

Conclusion and Significance

The work proposes a framework where a single RL policy solves multiple construction tasks by leveraging image-based successor features to decompose rewards into task- and action-specific components.

Improvements for AI systems

As a diligent AI researcher, I have thoroughly analyzed this paper, Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans. The core innovation lies in combining goal-conditioned Reinforcement Learning (RL) with an image-based successor feature representation to enable flexible, plan-free construction.

Here are specific improvements and capabilities for an AI system based on this framework:

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Improving the AI System: Specific Enhancements and Capabilities

The proposed framework moves beyond simple task completion toward a highly adaptable, generative construction agent. The following specific improvements can be implemented in the RL architecture and its application:

[Improvement] Enhance the State/Action/Task Feature Encoding for Deeper Geometric Reasoning (Beyond 2D/Simple Features)
[Improvement] Integrate Physics-Informed Constraints Directly into the Policy Gradient (Moving Beyond Binary Stability Checks)
[Improvement] Implement Multi-Agent Collaborative Construction Strategies (Leveraging the Framework for Complex Builds)
[Improvement] Develop a Robust Sim-to-Real Adaptation Module (Explicitly Modeling Noise and Uncertainty during Training)
[Improvement] Enhance Interpretability via Causal Tracing of Successor Features (Moving from Correlation to Intent)

Detailed Capabilities of the Improved AI System:

Based on these improvements, the resulting AI system will possess the following capabilities:

[Capability] Generative Structural Design Under Constraints: The system will be able to design novel, stable structures (not just assemble pre-defined ones) by interpreting high-level geometric goals (targets/obstacles). It can generate complex topologies like arches or bridges that optimize for material efficiency (minimizing block count) while satisfying specific structural rules learned implicitly through the reward function.
[Capability] Robust Real-World Deployment: By integrating explicit modeling of construction noise and uncertainty during training, the system will achieve significantly higher success rates in physical environments. It will be less prone to failure due to accumulated placement errors or minor structural instabilities that fall outside the bounds of its binary stability solver (RBE).
[Capability] Adaptive, Multi-Objective Construction: The single policy can simultaneously optimize for multiple, potentially conflicting objectives—for example, reaching all targets in the fewest blocks while maintaining a specific aesthetic shape or satisfying complex overhang requirements.
[Capability] Collaborative Complex Assembly: When scaled to multiple robotic agents (as suggested in the limitations), the system can manage distributed construction tasks where different robots handle different structural components, requiring coordinated decision-making based on shared state representations derived from the image-based features.
[Capability] Transparent Policy Generation (Explainable Construction): Because successor features decompose reward into task-specific and action-specific components, the system will be able to generate a construction intent visualization. This allows human supervisors to see not just that a solution was found, but why the policy chose a specific sequence of placements (e.g., The policy prioritized placing Block X here because it aligns with Target Y's proximity and minimizes material usage).

This system transforms the AI from a mere assembler into an autonomous, adaptive architectural design partner capable of handling the inherent unpredictability of real-world construction sites.