Robotics researchers propose AI that locates and safely moves items on shelves

Venture Beat | Nov 26, 2020 at 3:30 PM
  • 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 researchers, working out of the university’s AUTOLab department, focused on the challenge of finding occluded target objects in “lateral access environments,” or shelves.
  • In the Google work, which had contributions from researchers at Alphabet’s X, Stanford, and UC Berkeley, the coauthors designed a deep reinforcement learning method that takes multimodal data and uses a “deep representative structure” to capture contact-rich dynamics.