Intelligrated Headquarters, LLC

Dikshya Swain

Abhijit Makhal

Matthew R Wicks

Sai Prasanth Krishnamoorthy

Ramdas M Pai

Dominic George Clucas

Thomas Michael Ferner

Sagar Lakhmani

Various embodiments are directed to unloading system and methods of using the same. In various embodiments, an unloading system comprises a trailer bed configured to receive a container thereon, wherein the trailer bed comprises one or more joints configurable between a plurality of joint positions, wherein the trailer bed is configured to lift, tilt, and oscillate at least a portion of the container based on the joints; and an extendable conveyor configured to receive a box that has been released from the container and convey the box away from the trailer bed. Various embodiments are directed to a method comprising tilting the container such that an open end of the container is lower than a closed end of the container, wherein the open end of the container is proximate to an extendable conveyor; and oscillating the container to facilitate release of a box from the container onto the extendable conveyor.

Unloading system and method of using the same

A method for controlling a robotic item handler is described. The method includes obtaining first point cloud data related to a first three-dimensional (3D) image and second point cloud data related to a second 3D image, captured by a first sensor device and a second sensor device respectively. Further, the method can include transforming the first point cloud data and the second point cloud data to combined point cloud data that is used as an input to a convolutional neural network, to construct a machine learning model. The machine learning model can output a decision classification indicative of a first probability associated with a first operating mode and a second probability associated with a second operating mode. Furthermore, the method can include operating the robotic item handler according to the first operating mode or the second operating mode based on a comparison of the first probability and the second probability.

Machine learning based decision making for robotic item handling

Various embodiments described herein relate to techniques for reinforcement learning based conveyoring control. In this regard, a conveyor system is configured to transport one or more objects via a conveyor belt. Furthermore, a vision system comprises one or more sensors configured to scan the one or more objects associated with the conveyor system. A processing device is configured to employ a machine learning model to determine object pose data associated with the one or more objects. The processing device is further configured to generate speed control data for the conveyor belt of the conveyor system based on a set of control policies associated with the object pose data.

Reinforcement learning based conveyoring control

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St. Louis, MO, United States of America

Dikshya Swain