{"id":24854,"date":"2020-08-10T06:00:00","date_gmt":"2020-08-10T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=24854"},"modified":"2023-05-30T11:34:24","modified_gmt":"2023-05-30T18:34:24","slug":"ai-under-the-hood-flippy-the-robot","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2020\/08\/10\/ai-under-the-hood-flippy-the-robot\/","title":{"rendered":"AI Under the Hood: Flippy the Robot"},"content":{"rendered":"<div class=\"wp-block-image\">\n<figure class=\"alignright size-large is-resized\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/08\/Miso_logo.png\" alt=\"\" class=\"wp-image-24856\" width=\"281\" height=\"77\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/08\/Miso_logo.png 328w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/08\/Miso_logo-300x82.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/08\/Miso_logo-150x41.png 150w\" sizes=\"(max-width: 281px) 100vw, 281px\" \/><\/figure><\/div>\n\n\n<p>In this regular insideBIGDATA feature we highlight our industry\u2019s movers and shakers, companies that are pushing technology forward, and setting trends for innovation. We look at companies with a focus on big data, data science, machine learning, AI and deep learning \u2013 some new, some old, always leading, always dynamic. We also take deep dives into new technology promoted (or hyped) as \u201cAI\u201d or my favorite \u201cAI-powered-XYZ\u201d to provide transparency for what\u2019s really going on under the hood. Watch this column for intimate coverage of some pretty cool firms doing some pretty exciting things. Enjoy the ride!<\/p>\n\n\n\n<p>In this installment of \u201cAI Under the Hood\u201d I introduce &#8220;Flippy&#8221; by <a rel=\"noreferrer noopener\" href=\"https:\/\/misorobotics.com\/\" target=\"_blank\">Miso Robotics<\/a>. Flippy works in fast-food kitchens, operating a frying station for example. The product was decades in the making in terms of research in robotics and machine learning. Flippy is an amalgamation of motors, sensor, chips and processing power that wasn&#8217;t possible until just the past few years. The robotic arm is poised to become a regular fixture in high-volume kitchens nationwide in the coming year. More economical to employ than a minimum-wage worker, Flippy is designed to fit right in alongside human workers on the fast-food prep line. Flippy is available for an estimated $2,000 per month subscription. <\/p>\n\n\n\n<p>Early versions of Flippy have been seen in places like Dodger Stadium and at locations of CaliBurger, a small quick-serve chain. The company has raised more than $13 million in investment capital and is currently seeking another $30 million to gain entry in more fast-food kitchens. Automation in food preparation could be just the solution for COVID-19 limitations with human staffing.   <\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\"  id=\"_ytid_34990\"  width=\"480\" height=\"270\"  data-origwidth=\"480\" data-origheight=\"270\" src=\"https:\/\/www.youtube.com\/embed\/5vjf13h2f6o?enablejsapi=1&#038;autoplay=0&#038;cc_load_policy=0&#038;cc_lang_pref=&#038;iv_load_policy=1&#038;loop=0&#038;modestbranding=0&#038;rel=1&#038;fs=1&#038;playsinline=0&#038;autohide=2&#038;theme=dark&#038;color=red&#038;controls=1&#038;\" class=\"__youtube_prefs__  epyt-is-override  no-lazyload\" title=\"YouTube player\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen data-no-lazy=\"1\" data-skipgform_ajax_framebjll=\"\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>Miso co-founder Ryan Sinnet, Ph.D. has been long entrenched in the robotics space, co-authoring a 2018 research paper &#8220;<a rel=\"noreferrer noopener\" href=\"http:\/\/ames.caltech.edu\/kolathaya2018directtrajectory.pdf\" target=\"_blank\">Direct Collocation for Dynamic Behaviors with Nonprehensile Contacts: Application to Flipping Burgers<\/a>&#8221; which appeared in IEEE Robotics. The research indicates the problem of flipping burgers can be classified as a non-prehensile object manipulation problem. The paper discusses the Fast Robot Optimization and Simulation Toolkit (<a rel=\"noreferrer noopener\" href=\"http:\/\/ames.caltech.edu\/hereid2017frost.pdf\" target=\"_blank\">FROST<\/a>), but the software was not used in production for Flippy. The goal was to obtain joint angle trajectories to achieve two main tasks in the robot: (i) translate burger behavior and (ii) pickup and flip burger behavior. Translate burger behavior involves moving the Cartesian position of the spatula from one point to another while maintaining friction constraints to prevent the burger from slipping. Similarly, for pickup and flip burger behavior, the robot has to pick the burger using its spatula and flip in such a way that the under side of burger faces up after the completion of flip. See the figure below for a representation of the coordinate system involved.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" loading=\"lazy\" width=\"601\" height=\"344\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/08\/Miso_paper.png\" alt=\"\" class=\"wp-image-24855\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/08\/Miso_paper.png 601w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/08\/Miso_paper-300x172.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/08\/Miso_paper-150x86.png 150w\" sizes=\"(max-width: 601px) 100vw, 601px\" \/><figcaption class=\"wp-element-caption\">Spatula holding a freshly cooked burger. Accelerations and orientations along x,y,z directions  are shown.<\/figcaption><\/figure>\n\n\n\n<p>Flippy employs a software platform with a control stack that uses some of the same math implemented in FROST but Flippy also uses a variety of other optimization-based control methods to give the robot a wide range of capabilities. A funny side effect of the control strategies used is that Flippy&#8217;s motions often appear human-like.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p>&#8220;Flippy uses deep learning to locate and identify objects on the grill, as well as equipment and other objects in the kitchen,&#8221; said Miso co-founder Ryan Sinnet, Ph.D. &#8220;We have  developed&nbsp;breakthroughs in pose estimation technology&nbsp;using neural networks which allows Flippy to precisely identify the location of objects in the kitchen and pick them up with high reliability. The theoretical breakthroughs we&#8217;ve made were necessary to take low-reliability cutting-edge machine learning research and use it in a commercial environment.&#8221;<\/p>\n<\/blockquote>\n\n\n\n<p>Sinnet&#8217;s research paper described an exploration of a control strategy for picking up and flipping burger patties. This strategy is something that is part of Miso&#8217;s developer tool kit for adding functionality to the system, but this control strategy is not currently used in practice as the company has developed methods with higher reliability for the task of flipping burger patties.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p>&#8220;For Flippy&#8217;s real-time scheduling, the objective function can be modified to suit the needs of the restaurant,&#8221; continued Sinnet. &#8220;For example, a restaurant might want to ensure customers get their food within 3 minutes. As another example, a restaurant might want to make sure all food in an order comes out at the same time so half the order isn&#8217;t cold. Flippy is a platform that allows restaurant operators to have freedom over operations by shaping Flippy&#8217;s objective function.\u201d<\/p>\n<\/blockquote>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignleft size-large is-resized\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/12\/Daniel_2018_pic.png\" alt=\"\" class=\"wp-image-21778\" width=\"120\" height=\"138\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/12\/Daniel_2018_pic.png 200w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/12\/Daniel_2018_pic-131x150.png 131w\" sizes=\"(max-width: 120px) 100vw, 120px\" \/><\/figure><\/div>\n\n\n<p>C<em>ontributed by Daniel D. Gutierrez, Managing Editor and Resident Data Scientist for insideBIGDATA. In addition to being a tech journalist, Daniel also is a consultant in data scientist, author, educator and sits on a number of advisory boards for various start-up companies.&nbsp;<\/em><\/p>\n\n\n\n<p><em>Sign up for the free insideBIGDATA&nbsp;<a href=\"http:\/\/insidebigdata.com\/newsletter\/\" target=\"_blank\" rel=\"noreferrer noopener\">newsletter<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this installment of \u201cAI Under the Hood\u201d I introduce &#8220;Flippy&#8221; by Miso Robotics. Flippy works in fast-food kitchens, operating a frying station for example. The product was decades in the making in terms of research in robotics and machine learning. Flippy is an amalgamation of motors, sensor, chips and processing power that wasn&#8217;t possible until just the past few years. The robotic arm is poised to become a regular fixture in high-volume kitchens nationwide in the coming year. <\/p>\n","protected":false},"author":37,"featured_media":24856,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,1297,115,87,180,67,56,1],"tags":[437,324,581,264,927,95],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AI Under the Hood: Flippy the Robot - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2020\/08\/10\/ai-under-the-hood-flippy-the-robot\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Under the Hood: Flippy the Robot - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this installment of \u201cAI Under the Hood\u201d I introduce &quot;Flippy&quot; by Miso Robotics. Flippy works in fast-food kitchens, operating a frying station for example. The product was decades in the making in terms of research in robotics and machine learning. Flippy is an amalgamation of motors, sensor, chips and processing power that wasn&#039;t possible until just the past few years. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/08\/Miso_logo.png","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-6sS","jetpack-related-posts":[{"id":24153,"url":"https:\/\/insidebigdata.com\/2020\/03\/24\/ai-under-the-hood-causalens\/","url_meta":{"origin":24854,"position":0},"title":"AI Under the Hood: causaLens","date":"March 24, 2020","format":false,"excerpt":"In this installment of \"AI Under the Hood\" I introduce causaLens, a London-based deep tech company building a machine that predicts the global economy in real-time. The company's Growth Analyst reached out to me on LinkedIn, and I liked what I learned from the materials I received.","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2020\/03\/causaLens_logo.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":24376,"url":"https:\/\/insidebigdata.com\/2020\/05\/11\/ai-under-the-hood-decormatters\/","url_meta":{"origin":24854,"position":1},"title":"AI Under the Hood: DecorMatters","date":"May 11, 2020","format":false,"excerpt":"In this installment of \u201cAI Under the Hood\u201d I introduce Silicon Valley-based DecorMatters, a compelling creativity-sharing ecosystem that brings together interior designers and furniture shoppers to make any home renovation project easier and more affordable. Founded in 2016, DecorMatters is powered by augmented reality and AI technology, and is redefining\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/img.youtube.com\/vi\/yOIedPez1ms\/0.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":24062,"url":"https:\/\/insidebigdata.com\/2020\/03\/19\/ai-under-the-hood-kaskada-inc\/","url_meta":{"origin":24854,"position":2},"title":"AI Under the Hood: Kaskada, Inc.","date":"March 19, 2020","format":false,"excerpt":"In this installment of \"AI Under the Hood\" I introduce Kasakda, Inc., a Seattle-based early stage company founded in January 2018. Kaskada is a machine learning platform for feature engineering using event-based data. Kaskada\u2019s platform allows data scientists to unify the feature engineering process across their organizations with a single\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":24336,"url":"https:\/\/insidebigdata.com\/2020\/04\/29\/ai-under-the-hood-playform\/","url_meta":{"origin":24854,"position":3},"title":"AI Under the Hood: Playform","date":"April 29, 2020","format":false,"excerpt":"In this installment of \u201cAI Under the Hood\u201d I introduce recently launched Playform (Artrendex Inc.), a generative AI collaborative tool for artists. The company\u2019s tech publicist reached out to me around when the global pandemic became serious, so it's taken a while for me to write this review. But I\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":31465,"url":"https:\/\/insidebigdata.com\/2023\/01\/26\/ai-under-the-hood-interactions\/","url_meta":{"origin":24854,"position":4},"title":"AI Under the Hood: Interactions","date":"January 26, 2023","format":false,"excerpt":"We asked our friends over at Interactions to do a deep dive into their technology. Mahnoosh Mehrabani, Ph.D., Interactions' Sr. Principal Scientist shared some fascinating information about how Interactions' Intelligent Virtual Assistants (IVAs) leverage advanced natural language understanding (NLU) models for \"speech recognition\" and \"advanced machine learning.\" The company uses\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/img.youtube.com\/vi\/QbxAXMiwsaQ\/0.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":24721,"url":"https:\/\/insidebigdata.com\/2020\/07\/11\/research-highlights-exbert\/","url_meta":{"origin":24854,"position":5},"title":"Research Highlights: ExBERT","date":"July 11, 2020","format":false,"excerpt":"In the insideBIGDATA Research Highlights column we take a look at new and upcoming results from the research community for data science, machine learning, AI and deep learning. Our readers need to get a glimpse for technology coming down the pipeline that will make their efforts more strategic and competitive.\u2026","rel":"","context":"In &quot;AI Deep Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2020\/07\/exBERT_fig.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/24854"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/37"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=24854"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/24854\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/24856"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=24854"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=24854"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=24854"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}