2026-04-14T02:57:13Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/283742026-03-07T19:50:53Zcom_2072_452966com_2072_2054col_2072_452969

2026-03-07T19:50:53Z urn:hdl:10256/28374 Deep Reinforcement Learning for robot manipulation Mulia, Vania Katherine DRL (Deep Reinforcement Learning) Deep learning (Machine learning) Aprenentatge profund (Aprenentatge automàtic) Robots -- Control systems Sim-to-real transfer Peg-in-hole task Robots -- Sistemes de control Robotic manipulation continues to be an active area of research due to its broad range of real-world applications. Among its benchmark tasks, the peg-in hole problem remains particularly challenging, requiring high-precision control under environmental uncertainty. This thesis presents a framework based on Deep Reinforcement Learning (DRL) to train a robotic manipulator to autonomously solve the peg-in-hole task. The proposed approach uses curriculum learning to train a single policy capable of handling all phases of the task: approach, contact-based hole search, and insertion. The curriculum is further extended to incorporate observation noise and force penalization, encouraging the emergence of compliant behaviors during contact. Training is conducted in a custom-designed, physics-based simulation environment. Simulation results demonstrate that the learned policy can complete the peg-in-hole task, though it faces difficulties in balancing task success with compliant interaction. To evaluate the potential for real-world deployment, the trained policy is transferred to a physical robot. Tests reveal several sources of sim-to-real discrepancy, particularly in the modeling of contact dynamics. Nonetheless, partial success in real-world trials suggests the viability of sim-to-real transfer for DRL-trained policies. Overall, this work contributes to the understanding of DRL’s capabilities and limitations in solving complex robotic manipulation tasks such as peg-in-hole assembly. 9 2026-03-07T19:50:53Z 2026-03-07T19:50:53Z 2025-06 info:eu-repo/semantics/masterThesis https://hdl.handle.net/10256/28374 Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Universitat de Girona. Institut de Recerca en Visió per Computador i Robòtica Erasmus Mundus Joint Master in Intelligent Field Robotic Systems (IFROS)