cs.ROFeb 16, 2026

Simulation-based Learning of Electrical Cabinet Assembly Using Robot Skills

Authors: Arik Laemmle, Balázs András Bálint, Philipp Tenbrock, Frank Naegele, David Traunecker, József Váncza, Marco F. Huber

Organizations: formerly Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Stuttgart, Germany · formerly Institut für Strahlwerkzeuge IFSW, University of Stuttgart, Stuttgart, Germany · Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control SZTAKI, Budapest, Hungary · Institute of Industrial Manufacturing and Management IFF, University of Stuttgart, Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Stuttgart, Germany

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Abstract

This paper presents a simulation-driven approach for automating the force-controlled assembly of electrical terminals on DIN-rails, a task traditionally hindered by high programming effort and product variability. The proposed method integrates deep reinforcement learning (DRL) with parameterizable robot skills in a physics-based simulation environment. To realistically model the snap-fit assembly process, we develop and evaluate two types of joining models: analytical models based on beam theory and rigid-body models implemented in the MuJoCo physics engine. These models enable accurate simulation of interaction forces, essential for training DRL agents. The robot skills are structured using the pitasc framework, allowing modular, reusable control strategies. Training is conducted in simulation using Soft Actor-Critic (SAC) and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms. Domain randomization is applied to improve robustness. The trained policies are transferred to a physical UR10e robot system without additional tuning. Experimental results demonstrate high success rates (up to 100%) in both simulation and real-world settings, even under significant positional and rotational deviations. The system generalizes well to new terminal types and positions, significantly reducing manual programming effort. This work highlights the potential of combining simulation-based learning with modular robot skills for flexible, scalable automation in small-batch manufacturing. Future work will explore hybrid learning methods, automated environment parameterization, and further refinement of joining models for design integration.

Figures & tables

Figure 1: Structure of the electrical terminal assembly task, used in the presented work.
Figure 2: Proposed framework for learning robot skill parameters from simulation.
Figure 3: Unilaterally clamped bending beam and snap-hook (own illustration following [ 2 ] ).
Figure 4: Geometric relations (left) and acting forces (right) on the snap-hook head.
Figure 5: Rigid body joining model with one translational degree of freedom.
Figure 6: Rigid body joining model with one rotational degree of freedom.
Figure 7: Rigid body joining model with two rotational degrees of freedom.
Figure 8: Experimental setup for measuring the acting forces on the snap-hook.
Figure 9: Results from the experimental model evaluation and force progression for a snap-hook with Geometry II.
Figure 10: Lateral force (top) and joining force (bottom) values as recorded in the real-world scenario ( reference ) and provided by the rigid body joining models (1-DoF translational model " slide ", 1-DoF rotational model " one-hinge ", 2-DoF rotational model " two-hinge ") and the overlap-based extensions DRM ( measured ) and TFM ( calculated ). The vertical dashed line marks the point of the snap-in, i.e., the point until the models are considered valid.
Figure 11: Simulated model of the robotic test-bed including the integrated joining models.
Figure 12: Average return, success rate and number of used skills from the simulation-based training of a policy trained with SAC.
Figure 13: Success rates for a policy trained and evaluated on the WAGO 2002-1201 terminal during in-silico execution.
PolicyTrained forEvaluated on
Experiment IWAGO 2002-1201WAGO 2002-1201
Experiment IIWAGO 2102-1301WAGO 2102-1301
Experiment IIIWAGO 2102-1301WAGO 2016-1301
Table 1: Overview of trained and evaluated policies on the physical test-bed.
Figure 14: Success rates for a policy trained and evaluated on the WAGO 2002-1201 terminal during real-world execution.
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