Papers › DSL-Assembly: A Robust and Safe Assembly Strategy
DSL-Assembly: A Robust and Safe Assembly Strategy
Yi Liu
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
A reinforcement learning (RL) based method that enables the robot to accomplish the assembly-type task with safety regulations is proposed. The overall strategy consists of grasping and assembly, and this paper mainly considers the assembly strategy. Force feedback is used instead of visual feedback to perceive the shape and direction of the hole in this paper. Furthermore, since the emergency stop is triggered when the force output is too large, a force-based dynamic safety lock (DSL) is proposed to limit the pressing force of the robot. Finally, we train and test the robot model with a simulator and build ablation experiments to illustrate the effectiveness of our method. The models are independently tested 500 times in the simulator, and we get an 88.57% success rate with a 4mm gap. These models are transferred to the real world and deployed on a real robot. We conducted independent tests and obtained a 79.63% success rate with a 4mm gap. Simulation environments: https://github.com/0707yiliu/peg-in-hole-with-RL.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections