Multimodal Variational DeepMDP: An Efficient Approach for Industrial Assembly in High-Mix, Low-Volume Production

Abstract

Transferability, along with sample efficiency, is a critical factor for a reinforcement learning (RL) agent's successful application in real-world contact-rich manipulation tasks, such as product assembly. For instance, in the case of the industrial insertion task on high-mix, low-volume (HMLV) production lines, transferability could eliminate the need for machine retooling, thus reducing production line downtimes. In our work, we introduce a method called Multimodal Variational DeepMDP (MVDeepMDP) that demonstrates the ability to generalize to various environmental variations not encountered during training. The key feature of our approach involves learning a multimodal latent dynamic representation. We demonstrate the effectiveness of our method in the context of an electronic parts insertion task, which is challenging for RL agents due to the diverse physical properties of the non-standardized components, as well as simple 3D printed blocks insertion. Furthermore, we evaluate the transferability of MVDeepMDP and analyze the impact of the balancing mechanism of the generalized Product-of-Experts (gPoE), which is used to combine observable modalities. Finally, we explore the influence of separately processing state modalities of different physical quantities, such as pose and 6D force/torque (F/T) data.