Amazon Technologies, Inc.

Shantanu Thakar

Pragyana K Mishra

Sisir Babu Karumanchi

Nathalie Majcherczyk

Siyu Dai

Bhavana Mysore Chandrashekhar

Walter Gottlieb Bircher

Garrett Memoli

Ariel Gordon

Ruinian Xu

Monocular depth prediction machine learning models may be trained to receive imaging data of inventory holders having translucent bands that at least partially occlude portions of compartments and items of the inventory holders. The monocular depth prediction models may process the imaging data to generate accurate and reliable depth maps for the inventory holders, while substantially ignoring or disregarding the translucent, partially occlusive bands. Further, the depth maps may be utilized by various occupancy correction models to improve determinations related to available storage space within compartments of the inventory holders, thereby improving the accuracy and efficiency of various material handling processes.

Monocular depth prediction and occupancy correction systems and methods for item stow processes

A physical space may contain sliding doors in various places such as between rooms, as closet doors, on furniture, and so forth. An autonomous mobile device (AMD) capable of non-holonomic motion may selectively contact a portion of the sliding door and apply a force to open or close the door. The AMD moves to an initial pose relative to the door, with a portion of the AMD coming into contact with the door. The AMD then rotates and translates, relative to the door, applying a force to the door. Sensor data, such as linear acceleration, rotation, drive wheel torque, and so forth is used as input to determine the next movement in a series of motions, resulting in continued contact and force application. As the end of travel for this motion is reached, the AMD may separate from the door, reposition, and continue to apply a force to the door.

System to manipulate sliding obstacles with non-holonomic autonomous mobile device

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Mountain View, CA, United States of America

Shantanu Thakar