2026-04-13T01:30:56Zhttps://recercat.cat/oai/requestoai:recercat.cat:10256/283742026-03-07T19:50:53Zcom_2072_452966com_2072_2054col_2072_452969
RECERCAT
author
Mulia, Vania Katherine
2026-03-07T19:50:53Z
2026-03-07T19:50:53Z
2025-06
https://hdl.handle.net/10256/28374
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
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess
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
Deep Reinforcement Learning for robot manipulation
info:eu-repo/semantics/masterThesis