Simulation-based Learning of Electrical Cabinet Assembly Using Robot Skills
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (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 33 references that are in the map. First posted on arXiv 16 Feb 2026.
Builds on
8 of 33 references.
References · 8
- 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 319
- 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 213
- 2019A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithmscited by 142
- 2023IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Realitycited by 53
- 2017Deep Reinforcement Learning for High Precision Assembly Taskscited by 39
- 2023Bridging the Sim-to-Real Gap with Dynamic Compliance Tuning for Industrial Insertioncited by 9
- 2024Generalize by Touching: Tactile Ensemble Skill Transfer for Robotic Furniture Assemblycited by 7
- 2018A Learning Framework for High Precision Industrial Assemblycited by 4
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- 2025SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks
- 2019Contact Skill Imitation Learning for Robot-Independent Assembly Programming
- 2026Knowledge-Guided Hierarchical Policy Learning for High-Precision Cylindrical Assembly under Tight Tolerances ↗
- 2025Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing
- 2024AssemblyComplete: 3D Combinatorial Construction with Deep Reinforcement Learning
- 2024Subassembly to Full Assembly: Effective Assembly Sequence Planning through Graph-based Reinforcement Learning
- 2026Learning-Based Strategy for Composite Robot Assembly Skill Adaptation ↗
- 2025Reinforcement Learning for Robotic Insertion of Flexible Cables in Industrial Settings