Multi-Critic Reinforcement Learning for Garment Handling: Addressing Unpredictability in Temporal-Phase Continuous Contact Tasks

This research unveils a novel Multi-Critic Reinforcement Learning framework designed to navigate the multifaceted challenges associated with multi-phased garment handling tasks, notably marked by persistent contact and erratic deformations between textiles and solid bodies. These tasks, ubiquitous in domestic and industrial environments, encompass activities such as dressing, fabric printing, and pressing, and are complicated by the unpredictability of textile states and the intricacy of devising control strategies. Our reinforcement learning model combines multiple time-sequenced Critic networks with traditional Deep Deterministic Policy Gradient (DDPG) techniques, thereby equipping the system to adapt to the diverse effects of fabric distortions throughout various stages. The effectiveness of this approach is demonstrated through a multi-phase pre-printing operation and further validated by real-world implementations, showing significant improvements in coverage and a substantial reduction in wrinkle formation, with its versatility further confirmed by a complex vertical dressing task. We anticipate future applications of this framework in a range of complex problems, not just garment handling. The model used in this paper can be found at https://github.com/jkk5454/multiddpg.git.