A pair of new robotics studies from Google and the University of California, Berkeley propose ways of finding occluded objects on shelves and solving “contact-rich” manipulation tasks like moving objects across a table. The UC Berkeley research introduces Lateral Access maXimal Reduction of occupancY support Area (LAX-RAY), a system that predicts a target object’s location, even when only a portion of that object is visible. As for the Google-coauthored paper, it proposes Contact-aware Online COntext Inference (COCOI), which aims to embed the dynamics properties of physical things in an easy-to-use framework.
While researchers have explored the robotics problem of searching for objects in clutter for quite some time, settings like shelves, cabinets, and closets are a less-studied area, despite their wide applicability. (For example, a service robot at a pharmacy might need to find supplies from a medical cabinet.) Contact-rich manipulation problems are just as ubiquitous in the physical world,