PhD ceremony Yongliang Wang: Learning target-driven robotic manipulation in constrained clutter
Robotic manipulation enables robots to interact with and transform their physical environment.
Although modern robots perform well in structured industrial settings, robust manipulation in cluttered and constrained environments remains a significant challenge. In such scenarios, robots must reason about complex object interactions, contact dynamics, and limited workspace while executing precise motions.
In his thesis, Yongliang Wang investigates learning-based approaches for enabling target-driven robotic manipulation in constrained and cluttered environments. Wang focuses on improving manipulation efficiency, robustness, and generalization through reinforcement learning, deep reinforcement learning, and diffusion-based policy learning.
The PhD ceremony can be followed via livestream.