Off-Policy Reinforcement Learning (RL) for a Production Robotics Application

Kindred offers eCommerce retailers a solution to assist with rapid order fulfilment from their distribution centres. The solution (SORT) is a combination of a so-called put-wall and a humanoid robot. The robot picks up items from orders, scans them, and puts each item in a cubby of the put-wall according to the scan code. The robot comprises a gripper, a 6-degree-of-freedom arm, and a stereo vision module, as well as other electronics and mechanical housing. The proposed research will explore machine learning techniques based on reinforcement learning to feed data recorded from Kindred’s production robots back into learning algorithms in order to generate new better ways for those robots to pick, scan, and stow those eCommerce customers’ orders.

Faculty Supervisor:

Florian Shkruti


Bryan Chan


Kindred Systems Inc


Computer science



University of Toronto



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