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Applying Machine Learning to Robotics WHITEPAPER WHITEPAPER Credit: Pixabay Applying Machine Learning to Robotics TABLE OF CONTENTS MACHINE LEARNING - TOP MARKS FOR POTENTIAL MACHINE LEARNING SOLVES BUSINESS PROBLEMS CURRENT TECHNOLOGIES AND SUPPLIERS MACHINE LEARNING IN ROBOTICS: EXAMPLE 1 MACHINE LEARNING IN ROBOTICS: EXAMPLE 2 MACHINE LEARNING IN ROBOTICS: EXAMPLE 3 NEXT-GENERATION INDUSTRY WILL RELY ON MACHINE LEARNING roboticsbusinessreview.com 2 APPLYING MACHINE LEARNING TO ROBOTICS Advances in artificial intelligence are making robots smarter at pick-and- place operations, drones more autonomous, and the Industrial Internet of Things more connected. Where else could machine learning help? By Andrew Williams A growing number of businesses worldwide are waking up to the potentially transformative capabilities of machine learning - particularly when applied to robotics systems in the workplace. In recent years, the capacity of machine learning to improve efficiency in fields as diverse as manufacturing assembly, pick-and-place operations, quality control and drone systems has also gathered a great deal of momentum. Knowing that machine learning is improving also heightens awareness of great strides being made in artificial intelligence (AI), to the extent that the two technologies are often viewed interchangeably. Major recent advances in topics such as logic and data analytics, algorithm development, and predictive analytics are also driving AI’s growth. In this report, we’ll review the latest cutting-edge machine learning research and development around the globe, and explore some emerging applications in the field of robotics. MACHINE LEARNING - TOP MARKS FOR POTENTIAL A September 2017 report by Research and Markets predicts the global machine learning market will grow from $1.4 billion in 2017 to $8.81 billion by 2022, with a compound annual growth rate of 44.1%. Driving the growth is “the proliferation of large and multidimensional data sets, rising focus towards solving real-time problems from data,” and rising demand for sophisticated algorithm platforms and tools, the report stated. When applying machine learning to robotics, a recent McKinsey Global Institute study puts the total annual external investment in AI between $8 billion to $12 billion in 2016, with machine learning accounting for almost 60% of that total. The report’s authors state that robotics and speech recognition are among the most popular investment areas, with investors preferring code- based machine learning start-ups because of how agile they can scale up and incorporate new features. roboticsbusinessreview.com 3 MACHINE LEARNING SOLVES BUSINESS PROBLEMS Because machine learning relies on algorithms that can learn from data, many companies find they no longer need to depend on rules-based programming. In recent years, the increasing ability for such systems to learn independently has become more important – particularly as businesses worldwide struggle to keep up with otherwise unmanageable volumes of complex data. This has also led to the introduction of machine learning in several new sectors. For example, the recent Evans Data Corporation Global Development Survey found machine learning applications most frequently created in the following areas: ➤ Internet of Things (11.4% of the total) ➤ Professional, Scientific and Technical Services (10%) ➤ Manufacturing industries (9.4%) ➤ Telecommunications (8.3%) ➤ Utilities / energy (8.1%) ➤ Robotics (7.2%) ➤ Finance / insurance (6.8%) The report stated that almost 6.5 million developers worldwide are now using either AI or machine learning in their projects. Within the robotics sector itself, a recent Techemergence study listed computer vision, imitation learning, self-supervised learning, assistive and medical technologies, and multi-agent learning as the top five current machine learning applications in robotics. CURRENT TECHNOLOGIES AND SUPPLIERS Here’s a quick overview of some companies using machine learning within the software or robotics hardware space: Company Description / Project AIBrain Constructs AI solutions for smartphones and robotics applications. Main tools include the AI agent AICoRE and intelligent robot software platform iRSP. Amazon Among other things, the global behemoth’s Amazon Machine Learning platform is used by companies to predict and find patterns using data. Anki Employs a range of machine learning algorithms dubbed the ‘emotional engine’ in products like its Cozmo consumer robot. Apple Employs machine learning and deep learning technologies across a range of its products and services, perhaps most notably in its flagship voice assistant, Siri. roboticsbusinessreview.com 4 Company Description / Project CloudMinds Connects robots and smart devices to cloud-based systems for artificial intelligence computation or analysis. DataRobot Provides an automated machine learning platform, as well as services and education. Embodied Uses recent advances in deep imitation learning and deep Intelligence reinforcement learning to create AI software that simplifies the process of teaching robots new and complex skills. Facebook The global social media giant runs the Facebook Artificial Intelligence Research (FAIR) team, which is training AI bots in the skills of negotiation. FANUC Created the FANUC Intelligent Edge Link & Drive (FIELD) System, an open platform that ‘enables the execution of various Industrial IoT applications within a factory focusing on interconnecting edge- heavy devices such as machine tools, robots, PLCs and sensors.’ GreenSight Provides drone mapping for golf courses and agriculture markets Agronomics based on machine learning. H2O The machine-learning specialist recently partnered with NVIDIA to help create GPU-enabled deep learning AI in computers, robots, and self-driving cars. Hummingbird Data and imagery analytics company that uses a combination Technologies of drones and machine learning techniques for crop science applications. IBM An early frontrunner in machine learning. Among other projects, its Watson IoT system has been used in commercial robots. One interesting example is the Q.bo One, which ‘has the potential to be used in various commercial settings, including hospitality, financial services, medical assistance, home healthcare, and other service industries.’ Intel Recently launched the Loihi AI-powered ‘self learning chip’, aimed at improving the autonomy and performance efficiency of robots. Kindred Developing intelligent warehouse robots that ‘bring together reinforcement learning, machine learning and remote human guidance’ to ‘solve real-world problems alongside humans in complex, changing environments like today’s supply chain.’ KUKA Working with Huawei on a range of machine learning initiatives, which also established a ‘joint intuitive robot programming team to explore the use of imitative deep learning in advanced manufacturing environments.’ Presenso Creates software tools that use machine learning techniques to support predictive maintenance for machinery and robotic systems. Vicarious Combining insights from generative probabilistic models and systems neuroscience to create artificial general intelligence for robots. roboticsbusinessreview.com 5 MACHINE LEARNING IN ROBOTICS: EXAMPLE 1 The rapidly advancing capabilities of machine learning are helping individual robots learn specific tasks. An interesting example is the SKIL Somatic tool, developed by San Francisco-based start-up Skymind. The company applies deep learning methods to teach a robot to click the correct elevator button when prompted, using a camera mounted to the robot. By applying machine learning, the robot can look at an elevator control panel and understand which button is floor 1, which button is floor 2, etc. The SKIL Somatic tool uses an array of cutting-edge technology, including a convolutional neural network (CNN, or ConvNet)-based vision system, and Long Short-Term Memory (LSTM)-based sensor fusion. The tool also features an RL4J-based guidance system - a reinforcement learning framework integrated with Deeplearning4j and released under an Apache 2.0 open- source license. Chris Nicholson, CEO and Co-founder of Skymind, said the SKIL Somatic tool is like the “operating system” installed in the robots, whereas the open-source Deeplearning4j programming library is used “as the engine behind these predictions.” “The key advantage of SKIL Somatic is the integrations with the Robot Operating System (ROS), a widely used framework for writing robot software, support for reinforcement learning, and a method to manage models baked in,” Nicholson said. “It’s basically an entire package for enabling robots to learn human-like functionality.” Nicholson said the potential market for such tools is certainly expanding, particularly in Asia. He said the company as seen lots of interest in robots that can assist humans. For example, one company contacted Skymind to see if SKIL Somatic could be used for indoor store navigation – having a robot guide customers to the item they are looking for. Another common use case is developing unmanned aerial vehicles (UAVs) that can “navigate terrain themselves and find items of interest without human interference,” Nicholson said. “I’d say there are two promising industries, human assistance - for elderly care, retail and so on - and UAVs, for surveillance, monitoring crops and finding poachers,” Nicholson said. “There could be more, but these two are top of mind.” roboticsbusinessreview.com 6 MACHINE LEARNING IN ROBOTICS: EXAMPLE 2 In a manufacturing environment, an interesting application
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