Master Thesis
Hierarchical Reinforcment Learning for Agile Loco-manipulation
Master’s Thesis — Hierarchical Deep Reinforcement Learning for Loco-Manipulation
This master’s thesis was conducted at Regelungstechnik, TU Dortmund University and at the former Department of AI and Autonomous Systems, Fraunhofer IML.
The thesis introduces a novel hierarchical deep reinforcement learning methodology for end-to-end robot learning. A two-level policy architecture—comprising high-level decision-making and low-level control policies—is employed to enable autonomous robot navigation and is further extended to loco-manipulation tasks.
The experimental platform is the evoBOT, a robot characterized by highly complex and difficult-to-model dynamics. Training was performed using a GPU-accelerated deep reinforcement learning pipeline in NVIDIA Isaac Sim, as part of OmniIsaacGymEnvs (Isaac Lab).
The primary objective of the thesis was to develop a fully autonomous pick-and-place solution in which the evoBOT learns to transport an objects between locations, without any vision or external sensing. The results demonstrate that hierarchical policy learning enables robust coordination of navigation and manipulation behaviors.
