Robot 2.0: Deep reinforcement learning for industrial robotics
Who is this presentation for?
- Companies interested in automation
- AI-enabled robotics developers, people who work in logistics or manufacturing sectors
There are only 3 million existing industrial robots in the world. Contrast that with 1 billion cars, and you see why self-driving gets more attention. Yet every other robot manufacturer we talk to says they cannot make robots fast enough. The International Federation of Robotics numbers just showed another year of 30% growth. The commercial opportunity for AI-enabled robots is huge, particularly in the logistics, materials-handling, and automotive industry. These market opportunities are compelling because they are large, have already embraced some measure of automation, and have problem characteristics that are well suited to AI solutions.
At a societal level, AI-enabled robots will address the challenges of outsourcing and the balance of trade, per capita productivity, competitiveness, inequality, and safety—some of the most long-standing and important problems facing the United States today. The best market for this opportunity is ecommerce fulfillment. Estimates of the total size of the materials handling and logistics market vary, but most agree that it will approach $50 billion globally by 2022 and experience annual growth rates of 20%. After materials handling and logistics, the best use case is the automotive manufacturing industry. Automotive manufacturing is a $95 billion industry within the US, where approximately 10% of the final cost of goods go toward manual labor.
Bastiane Huang examines how AI-enabled robots are used in warehouse automation, including recent progress in deep reinforcement learning, imitation learning, and real world requirements for various industrial problems, and how you can use warehouse robotics as a crystal ball and example for other industries, including manufacturing, food assembly, and surgical robots.
- Familiarity with basic machine learning technologies, robotics, and warehouse industries
What you'll learn
- Learn how to productize AI in robotics companies and how to work with integrators or legacy industries and help them consider AI in their supply chain
- Discover how to bring current development practices in machine learning teams to companies with little technical expertise
Bastiane Huang is a product manager at OSARO, a San Francisco-based machine learning company building deep reinforcement learning software for industrial robots, backed by Peter Thiel and Jerry Yang’s AME Cloud, where she leads product strategy. Bastiane has close to a decade of experience in the automation and manufacturing industries. She has broad experience in product marketing, business development, and operations at international technology companies across the industrial automation, IoT, AI, and robotics industries. Previously, she worked at e2v; she cofounded a software business at Advantech that offered video analytics solutions to improve traffic congestion and shopping experiences through people counting and facial and heat map analysis; she was an investor and advisor to early stage IoT and AI startups in the US and Greater China; and she worked as a senior product manager at Amazon Alexa. She’s actively involved with Harvard’s Managing the Future of Work initiative on AI and robotics, writing case studies and articles. Bastiane holds a BS in information management from the National Taiwan University and an MBA in technology and entrepreneurship from Harvard Business School.
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