Reinforcement learning for robotic 3D bin packing

• ETH Zurich
• Computational robotics laboratory (Prof. Stelian Coros)

Many industrial assembly tasks require high accuracy when manipulating parts, e.g., when inserting an object somewhere, such as a box that we would like to put in a bin. Here, the tolerances that are required can vary wildly. While industrial manipulators are generally accurate in a controlled setting, as soon as the environment is not as structured as we would like it to be, we need to leverage feedback policies to correct for inaccuraies in estimation, and unexpected/unmodeled effects. The setting we are interested in in this work is robotic bin packing, i.e., enabling a robot to pick objects (initially boxes/parcels), and (the focus of this work) placing them in a box, where other previously placed object possibly obstruct the placement.

• Robotics and Mechatronics
• Master Thesis