Goal-Conditioned Flexible Object Manipulation by Self-Supervised Learning from Play

In various domains, including production and daily life environments, there is growing demand for robots capable of manipulating flexible objects such as cables and fabrics. Reinforcement learning and imitation learning are two of the most promising approaches for realizing such robots; however, they face issues related to the high cost of data collection and labeling, respectively. To address these issues, we have shifted our focus to “learning from play,” where play data refers to data collected by asking human operators to teleoperate a robot while maintaining a sense of curiosity. Specifically, we employ a framework called play-supervised latent motor plans (Play-LMP), which was originally evaluated for rigid object manipulation in a simulation environment. We extend this Play-LMP framework to the domain of flexible object manipulation in a real-world environment and evaluate its potential. Our experimental results on fixed-end rope manipulation demonstrate both the capabilities and limitations of the framework. In addition, we show that the latent plan space of the framework becomes structured according to specific tasks, even without explicit labeling of the data. These results suggest the effectiveness of our approach in flexible object manipulation because it achieves a reduction in data collection costs and enables task-unrestricted manipulations.