STOW: Discrete-Frame Segmentation and Tracking of Unseen Objects for Warehouse Picking Robots
This work addresses the problem of robust object handling in dynamic industrial and domestic robotic settings, but it appears incremental as it builds on existing segmentation and tracking techniques with a novel paradigm.
The paper tackles the problem of segmenting and tracking unseen object instances in discrete frames for warehouse picking robots, addressing challenges like object rearrangement and occlusion, and introduces a new paradigm with a transformer module that significantly outperforms recent methods.
Segmentation and tracking of unseen object instances in discrete frames pose a significant challenge in dynamic industrial robotic contexts, such as distribution warehouses. Here, robots must handle object rearrangement, including shifting, removal, and partial occlusion by new items, and track these items after substantial temporal gaps. The task is further complicated when robots encounter objects not learned in their training sets, which requires the ability to segment and track previously unseen items. Considering that continuous observation is often inaccessible in such settings, our task involves working with a discrete set of frames separated by indefinite periods during which substantial changes to the scene may occur. This task also translates to domestic robotic applications, such as rearrangement of objects on a table. To address these demanding challenges, we introduce new synthetic and real-world datasets that replicate these industrial and household scenarios. We also propose a novel paradigm for joint segmentation and tracking in discrete frames along with a transformer module that facilitates efficient inter-frame communication. The experiments we conduct show that our approach significantly outperforms recent methods. For additional results and videos, please visit \href{https://sites.google.com/view/stow-corl23}{website}. Code and dataset will be released.