Osaro: Picking the best path

Description

The founder of Osaro saw the potential of deep reinforcement learning to allow robots to be applied in a wide range of new applications. Osaro targeted warehousing, already a dynamic industry for robotics and automation, for its initial product - a system which allowed robotic arms to "pick and place" individual items into boxes for shipment to consumers. Despite receiving significant attention in the robotics space, the problem of universal "picking" had not been solved. Osaro believed deep reinforcement learning would provide the solution by allowing robots to handle the complexity involved in perceiving and grasping a wide range of objects. The case discussion will focus on the decision to choose the warehouse automation market, provide an overview of key machine learning concepts, and introduce students to some of the contours of competing in the machine learning space during the late 2010s. The case also introduces key concepts for thinking about how the combination of artificial intelligence and robotics will change work in the future.

Related Documents

• Osaro: Picking the best path, Teaching Note

Keywords

Artificial intelligence Automation Commercialization Entrepreneurship Inventory management Logistics Machine-based learning Marketing Organizational learning Robotics Start-ups Startup Technology Warehousing