Amazon Technologies, Inc.

Baoyuan Liu

Hakan Boyraz

Marshall Friend Tappen

Xiaohan Nie

Sheng Chen

Andrew Stubbs

Christopher David Brown

Avinash Aghoram Ravichandran

Aaron Joseph Parness

Kristina Lisa Klinkner

Xin Wang

Parker Owan

Sisir Babu Karumanchi

Joshua Rosenberg Hooks

Rahul Balakrishna Warrier

James Schmidt

Siyu Dai

Andres Camilo Cortes

Techniques and systems for performing a perception analysis for a robotic stowing operation are described. An example technique includes obtaining, via multiple sensors, multiple first images, wherein each first image is an image of a different container of an inventory holder within a robotic workcell. A first machine learning (ML) and image processing pipeline is performed with the first images to determine displacement locations for the containers of the inventory holder. A second ML and image processing pipeline is performed with the first images to determine content signatures for the containers. A plan is generated for stowing a first item into a first container, based at least in part on the plurality of content signatures and the plurality of displacement locations. A robotic apparatus is controlled to stow the first item into the first container, based on the plan.

Visual perception and techniques for placing inventory into pods with a robotic workcell

The present disclosure generally relates to a system of a delivery device for combining sensor data from various types of sensors to generate a map that enables the delivery device to navigate from a first location to a second location to deliver an item to the second location. The system obtains data from RGB, LIDAR, and depth sensors and combines this sensor data according to various algorithms to detect objects in an environment of the delivery device, generate point cloud and pose information associated with the detected objects, and generates object boundary data for the detected objects. The system further identifies object states for the detected object and generates the map for the environment based on the detected object, the generated object proposal data, the labeled point cloud data, and the object states. The generated map may be provided to other systems to navigate the delivery device.

Systems and methods of obstacle detection for automated delivery apparatus

An autonomous delivery robot system to enable delivery of a product to a customer is described. One autonomous ground vehicle (AGV) includes a processing device that receives a delivery request comprising a route divided into multiple navigation segments and computes a navigable space from perception data stored in a perception map. The perception map is a robot-centered local map that stores the perception data indicative of the surroundings of the AGV. The processing device computes a cost inflation from the perception data stored in the perception map, determines a sub-goal that is on the navigable space and reachable by the AGV using the navigable space and the cost inflation, and determines a path to achieve the sub-goal using the using the navigable space and the cost inflation. The processing device controls one or more actuators to move along the path.

Navigation directly from perception data without pre-mapping

Image classification and related imaging tasks performed using machine learning tools may be accelerated by using one or more of such tools to associate an image with a cluster of such labels or categories, and then to select one of the labels or categories of the cluster as associated with the image. The clusters of labels or categories may comprise labels that are mutually confused for one another, e.g., two or more labels or categories that have been identified as associated with a single image. By defining clusters of labels or categories, and configuring a machine learning tool to associate an image with one of the clusters, processes for identifying labels or categories associated with images may be accelerated because computations associated with labels or categories not included in the cluster may be omitted.

Cluster-trained machine learning for image processing

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Bellevue, WA, United States of America

Baoyuan Liu