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Automated with Brian Heater

Association for Advancing Automation

Get a direct line to the biggest names and brightest minds in robotics, Physical AI, and automation. Automated with Brian Heater brings you long-form conversations and unfiltered insights into how we got here, where we’re going, and what’s behind the technologies that are shaping how we live and work. 

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  • #52
    Wednesday · 57 min

    Kate Darling on Why Humans Bond With Robots

    Humans know robots are machines. That does not stop us from naming them, caring about them, or feeling uncomfortable when someone hurts one. In this episode of Automated, Brian Heater speaks with Kate Darling, Research Lead for Robotics, Ethics & Society at RAI Institute, about why social robots may be far more valuable than their current capabilities suggest. Kate believes every social robot we have seen so far is still in the PalmPilot stage of innovation. The breakthrough may come when people stop asking what a robot does and begin recognizing companionship itself as the application. But that emotional connection also creates risks. Kate explains why empathy is not a finite resource, even if attention is, and how companion technologies can be designed to serve a company’s interests instead of the user’s. The problem is not simply that people form relationships with machines. It is what businesses may do with those relationships. Brian and Kate also discuss humanoid robot safety, including what happens when an emergency stop causes a dynamically balanced robot to collapse. Kate explains why the workers who understand factories and warehouses need to be involved in these decisions before robots are deployed. The conversation also challenges one of automation’s most familiar promises: that robots will take over dull, dirty, and dangerous work. Kate argues that these labels are rarely defined and can overlook cultural differences, underreported injuries, and the parts of a job that workers actually enjoy. They also explore the future of work and why robots are not independently coming for anyone’s job. Companies make choices about how automation is used, whether it replaces a worker or improves an entire workflow. Finally, Kate shares the unforgettable Pleo experiment that changed the direction of her career. After participants bonded with a group of baby robot dinosaurs, they refused to damage them, even when threatened with losing every robot in the room. The experiment was not a formal scientific study, but the participants’ emotional response revealed something Kate has spent her career exploring: people can know a robot is a machine and still feel deeply compelled to treat it as if it were alive. Connect with Kate Darling https://www.linkedin.com/in/kate-darling-37a181149 Learn more about Kate’s work https://www.katedarling.org/ Learn more about RAI Institute https://rai-inst.com/ Read The New Breed https://www.amazon.com/New-Breed-History-Animals-Reveals/dp/1250296102 We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, Spotify, and the Automated Newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #51
    August 19 · 46 min

    Nic Radford on Humanoid Robots Beyond the Backflip

    Humanoid robots can walk, fold laundry, and even do backflips. Nic Radford wants to know whether anyone will pay them to work. In this episode of Automated, Brian Heater speaks with Nic Radford, co-founder and CEO of Persona AI and a former NASA roboticist who helped lead the development of Robonaut 2 and Valkyrie, about what it takes to turn a humanoid robot into a durable business. Nic explains why Persona is starting with welding and shipbuilding through its partnership with HD Hyundai, rather than waiting for a single general-purpose robot that can do everything. He breaks down the market questions that matter just as much as the technology: labor scarcity, task complexity, customer purchasing power, tool use, safety, unit economics, and the willingness to adopt. The conversation also gets deeply technical. Nic’s career as a high jumper, and the titanium ankle he now lives with, helped fuel a long-running fascination with biomechanics. He explains why the human ankle is so difficult to recreate, why adding motors at the bottom of a robot’s leg creates an energy problem, and how humanoid designers have to balance capability against overengineering. Brian and Nic also revisit Nic’s years at NASA. Nic explains how radiation can flip bits in a robot’s machine code, why Robonaut used three processors in each actuator, and why building a robot for the deep ocean's pressure can be even harder than building one for space. They also explore the original vision for Robonaut as an astronaut helper and emergency responder, the role DARPA played in advancing modern robotics, and why moonshot programs create lasting value even when the original goal remains out of reach. Finally, Nic shares what humanoid robotics can learn from the nearly 20-year arc of self-driving technology. The biggest lesson may be that impressive hardware is only one part of the journey. The real test is whether the technology solves a valuable problem, reaches customers, and gives them a reason to ask for another robot. Connect with Nic Radford https://www.linkedin.com/in/nicolaus-radford Learn more about Persona AI https://persona.ai/ Learn more about Persona AI’s work with HD Hyundai https://www.prnewswire.com/news-releases/hd-hyundai-and-persona-ai-sign-agreement-to-deploy-humanoid-welding-robots-for-shipbuilding-automation-302449258.html We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, Spotify, and the Automated Newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #50
    August 12 · 1 hr 22 min

    Kathryn Zealand and Jessica Bath on Wearable Robotics, Parkinson’s, and the Future of Mobility

    The ability to move is about far more than getting from one place to another. It can shape independence, confidence, social connection, and quality of life. In this special episode of Automated, Brian Heater speaks with Kathryn Zealand, founder and CEO of Skip, the Google X spinout behind MO/GO, and Jessica Bath, DPT, PhD, assistant professor of physical therapy at UCSF. Kathryn explains how a personal experience with her grandmother helped lead her from theoretical physics and evaluating moonshots at Google X into wearable robotics. She breaks down why Skip thinks about MO/GO as an “e-bike for walking,” how the company is building foundation models of human movement using real-world gait data, and why hiking offered a better starting point than the structured environments where many exoskeleton companies begin. The conversation then turns to Parkinson’s disease, a subject that has become deeply personal for Brian following his father’s diagnosis and recent passing. Kathryn and Jessica explore freezing of gait, fall risk, and what researchers still do not understand about the neurological signals behind movement. They also discuss how wearable robotics, deep brain stimulation, exercise, physical therapy, and better movement data could help people remain active and independent. MO/GO itself is a consumer recreation device and is not FDA-cleared. Skip’s work around Parkinson’s uses a separate prototype and is being studied through clinical research. Kathryn also shares the deeply personal story behind Project Stardust, a research-stage effort working toward better at-home pregnancy monitoring that she helped launch after the loss of her son, Ziggy. It is a conversation about movement, dignity, data, and what it means to build technology around problems that genuinely matter to people. Connect with Kathryn Zealand: https://www.linkedin.com/in/kathryn-zealand/ Learn more about Skip and MO/GO: https://www.skipwithjoy.com/ Learn more about Project Stardust: https://project-stardust.org/ Connect with Jessica Bath: https://www.linkedin.com/in/jessica-bath-pt-dpt-phd-a2331499/ Jessica Bath at UCSF: https://profiles.ucsf.edu/jessica.bath Support Parkinson’s research through The Michael J. Fox Foundation for Parkinson’s Research: https://www.michaeljfox.org/donate We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org. You can find the transcript and more episodes of Automated at automated.fm. Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, Spotify, and the Automated Newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #49
    August 5 · 46 min

    Andrei Danescu on Why the Market Does Not Want More Robots

    The market does not want more robots. It wants real-time answers about what is actually happening inside the warehouse. In this episode of Automated, Brian Heater visits Dexory’s new Nashville facility for a conversation with CEO and co-founder Andrei Danescu about his path from Formula 1 engineering to building one of the most distinctive systems in warehouse automation. Andrei explains why motorsport experience transfers so well to robotics. Both fields require hardware, software, control systems, telemetry, and mechanics to perform reliably under pressure. A robot cannot simply produce an impressive demonstration in a lab. It has to work every day in a complex and unpredictable environment. Brian and Andrei trace Dexory’s evolution from autonomous concierge robots at Heathrow and retail mapping tools to the warehouse intelligence platform it has become today. When logistics companies began approaching the team during the pandemic, Dexory did not immediately jump at the opportunity. The company first tested whether warehouse scanning was a real market need or simply another robotics novelty. That validation led to a hard pivot into logistics and the development of a telescoping autonomous robot capable of scanning warehouse racks with millimeter-level precision. But Andrei says the robot itself is only part of the product. DexoryView combines robotics, perception, autonomy, data processing, and a digital twin that allows customers to understand what is happening across their operations. The conversation also explores why Dexory chose to design, engineer, manufacture, deploy, and support its technology in-house. Andrei argues that robotics is a full-stack discipline. Outsourcing the difficult parts can also mean outsourcing the lessons that help a company improve its product and serve customers more effectively. They also discuss how Andrei’s Formula 1 experience inspired Dexory’s approach to digital twins. Just as racing teams use simulations to test different setups before changing a physical car, warehouse operators can use historical data and scenario analysis to test changes before disrupting an active facility. Finally, Andrei explains why warehouse digital twins could become infrastructure for future autonomous forklifts, humanoid robots, and other AI systems, why Dexory selected Nashville for its US expansion, and why the company deliberately never gave its towering robot a name. Connect with Andrei Danescu https://www.linkedin.com/in/darthvader1/ Learn more about Dexory https://www.dexory.com/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org. You can find the transcript and more episodes of Automated at automated.fm. Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, Spotify, and the Automated Newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #48
    July 29 · 42 min

    Ali Agha on Robot Hallucinations, Physics, and Real-World AI

    Physical AI is moving into the real world. But once a robot leaves the lab, Ali Agha says edge cases become the environment. In this episode of Automated, recorded live at Automate in Chicago, Brian Heater speaks with Ali Agha, founder and CEO of FieldAI, about building autonomous robots that can operate safely in unpredictable, unstructured places. Ali’s path to FieldAI runs through MIT, Qualcomm, NASA JPL, and DARPA. At Qualcomm, he worked to put autonomy on the low-power Snapdragon processor that would later fly aboard NASA’s Mars helicopter. At JPL, he led aerial mobility research and helped develop robotic systems for caves, subterranean networks, and other environments where maps, GPS, and reliable communication are not available. That work eventually led to the DARPA Subterranean Challenge. Ali’s team sent legged, wheeled, and flying robots into unknown environments to map, explore, and coordinate missions with no prior information. The experience changed how he thought about autonomy. In the physical world, the unusual case quickly becomes the normal case. That idea now sits at the center of FieldAI’s approach. Instead of asking a data-only model to learn every physical rule from raw camera and LiDAR input, FieldAI combines data-driven learning with physics and uncertainty quantification. The goal is to help a robot recognize when it is approaching the limits of what it knows, slow down, and make a safer decision. Brian and Ali dig into what robot hallucinations look like when an AI model controls a physical machine. A wrong answer from a chatbot can be corrected. A wrong move from a robot around people, equipment, or an active jobsite carries a much higher cost. They also examine why FieldAI runs its autonomy entirely on the robot without Wi-Fi, 5G, or a cloud connection. Ali argues that building safety into the architecture from day one allows robots to enter real customer workflows, collect useful data, improve, and create the deployment flywheel that robotics has struggled to start. Construction is especially valuable because the environment changes by the hour. FieldAI is also deploying in energy, manufacturing, logistics, and urban operations, giving its models experience across very different conditions and tasks. Finally, Ali explains why FieldAI is staying robot-agnostic while other physical AI companies move toward building full hardware stacks. Its software is already operating across 34 different robot embodiments, from multi-ton vehicles to quadrupeds and humanoids. Connect with Ali Agha https://www.linkedin.com/in/ali-agha-7aa5212a Learn more about FieldAI https://www.fieldai.com/ Learn more about FieldAI’s Field Foundation Models https://www.fieldai.com/news/fieldai-announces-over-400m-in-funds-raised-to-advance-embodied-ai-at-scale We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. Also subscribe to the newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #47
    July 22 · 40 min

    Samantha Johnson on the Robot Giving DeafBlind People a New Way to Connect

    Robotics is often judged by how quickly, powerfully, or autonomously a machine can move. Samantha Johnson built a robot to solve a much more human problem: helping DeafBlind people access information and communicate with the people they love. In this episode of Automated, Brian Heater speaks with Samantha Johnson, co-founder and CEO of Tatum Robotics, about how a chance meeting with a DeafBlind woman named Elaine inspired a tactile signing robot that is now being used in the real world. When Samantha asked Elaine how they could stay in touch, Elaine explained that they could not call each other. Samantha immediately began wondering whether a robot could create a new form of communication. Months later, the pandemic made the need even more urgent as social distancing disrupted the in-person contact and interpreting services on which many DeafBlind people relied. Samantha explains how she went from studying bioengineering and looking up basic servo instructions online to building robotic hand prototypes in her apartment. She also shares how almost every assumption she made during the first user test turned out to be wrong. A silicone covering intended to make the hand feel human instead felt like a monster, and users refused to touch it until the material was removed. That experience shaped the way Tatum Robotics develops its technology. DeafBlind consultants, users, and advisors have been involved throughout the process, guiding decisions about the hand’s size, movement, speed, grammar, personalization, and physical design. Brian and Samantha also discuss the pressure she faced from investors who wanted her to set the DeafBlind application aside and instead build a general-purpose robotic gripper. Samantha explains why she refused to compromise the company’s mission and how grants, startup programs, MassRobotics, and the Perkins School for the Blind helped the team continue developing the product. The conversation explores the communication barriers that many people rarely consider. One DeafBlind user explained that after taking a nap, he could not tell whether he had been asleep for two hours or two days. Others had lost contact with friends for more than a decade or could no longer call their own family members. Samantha shares how the robot is now helping users check the time, read news and weather updates, manage notifications, schedule appointments, and make phone calls. One woman used the system to call her daughter for the first time, repeatedly telling her, “It’s your mom. It’s your mom on the phone.” They also dig into the technical challenges of building a low-cost, compliant, tendon-driven hand capable of the dexterity required for tactile finger spelling. Samantha explains how the company customizes the robot for individual users and why one tester briefly forgot she was touching a machine rather than a human hand. Finally, Samantha discusses Tatum Robotics’ next phase, including a full robotic arm capable of more complex signing and potential applications in healthcare, museums, patient portals, and other public environments. Connect with Samantha Johnson https://www.linkedin.com/in/samantha-johnson-7001b413a Learn more about Tatum Robotics https://tatumrobotics.com/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org. You can find the transcript and more episodes of Automated at automated.fm. Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. Also subscribe to the Automated Newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #46
    July 15 · 46 min

    Dr. Ayanna Howard on Human-Centered Robotics, AI Guardrails, and Physical AI

    Robotics and AI are moving fast. But Dr. Ayanna Howard says the real test is not just whether machines become more capable. It is whether those systems are built with people, safety, accessibility, and real-world impact at the center. In this episode of Automated, Brian Heater speaks with Dr. Ayanna Howard, Dean of The Ohio State University College of Engineering, about human-centered robotics, agentic AI, healthcare robotics, accessibility, and what it really takes to move automation into the real world. Dr. Howard’s career has spanned NASA robotics, field robotics, healthcare robotics, assistive technology, AI ethics, and engineering leadership. Across all of that work, one theme has remained constant: technology should help improve the human condition. Brian and Dr. Howard discuss her early fascination with The Bionic Woman, how she started working at NASA after her freshman year of college, and why her work on glacier robots helped shape the way she thinks about Earth, humanity, and the responsibility of technologists. The conversation also digs into the state of physical AI today. Dr. Howard explains why many of the robotics breakthroughs getting attention now are built on ideas researchers were exploring decades ago. Compute, sensors, and AI models have changed dramatically, but the hardest robotics problems, including manipulation, autonomy, and real-world deployment, are still not solved. Brian and Dr. Howard also discuss humanoid robots and the gap between polished demos and messy real-world environments. A robot handling similar boxes on a flat conveyor belt may be impressive, but warehouses, hospitals, homes, and public spaces are far more complicated. The conversation then turns to AI guardrails. Dr. Howard explains why she is especially concerned about LLMs and agentic AI, and why bias, regulation, and safety become much more urgent as AI systems move into higher-stakes applications. They also explore why accessibility is central to the future of physical AI. Dr. Howard explains that robots encounter many of the same barriers as people with disabilities, and that a more accessible world would make it easier for both people and robots to move through it. Finally, Dr. Howard shares what still makes her optimistic, including low-cost robotics that could support children with cerebral palsy, older adults, injured athletes, hospitals, clinics, nursing homes, and schools. Connect with Dr. Ayanna Howard https://www.linkedin.com/in/ayanna-howard Learn more about Dr. Ayanna Howard https://www.ayannahoward.com/ Learn more about The Ohio State University College of Engineering https://engineering.osu.edu/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. Also subscribe to the Automated Newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #45
    July 8 · 41 min

    Yoel Fink on Education, Invention, and Asking Better Questions

    The best ideas are not always hidden in the future. Sometimes they are sitting right in front of us, waiting for someone to ask a better question. In this episode of Automated, Brian Heater speaks with Yoel Fink, Professor of Materials Science and Engineering at MIT, about education, invention, advanced fibers, and why so much of technology follows the same obvious tracks. This is not a typical conversation about robotics or automation. It is a wider look at how people learn, how researchers discover, and why stepping off a prescribed path can sometimes lead to better outcomes than following the one everyone else expects. Yoel reflects on his own unconventional path, from military service and years of backpacking to studying chemical engineering, physics, and eventually materials science at MIT. He explains why he encourages students to take time, see the world, and collect the kinds of experiences no classroom can fully provide. Brian and Yoel also discuss the pressure students face when they are pushed too quickly from one life milestone to the next. Yoel argues that people are not trains, and that education often works better when students have more room to mature, explore, and understand what they actually want to build. The conversation then moves into research and invention. Yoel shares the story of asking a simple question in a room full of leading optics researchers, a question that helped lead to a new kind of mirror and shaped the direction of his career. For Yoel, that moment reveals something essential about innovation: sometimes the breakthrough is not the answer. It is the courage to ask the question no one else is asking. They also explore Yoel’s work with advanced fibers and functional fabrics. He explains why fibers are one of the oldest and most universal forms of human technology, and why the future of computing and sensing may not look like another screen, headset, watch, or metal device. It may be woven into the clothes we already wear. Finally, Yoel challenges the way major technology companies often move in the same direction, from glasses to headsets to devices that look increasingly similar. His question is simple: are we really out of ideas, or are we just too busy following everyone else? Connect with Yoel Fink https://dmse.mit.edu/people/faculty/yoel-fink/ Learn more about fibers@mit https://pbg-rle.mit.edu/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. Also subscribe to the Automated Newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #44
    July 1 · 47 min

    Russ Tedrake on Robotics, Physical AI, and the Future of Work

    Physical AI is moving fast. But Russ Tedrake says the biggest shift may not just be better robots. It may be the way robotics itself is changing. In this episode of Automated, Brian Heater speaks with Russ Tedrake, Toyota Professor at MIT and founder of a stealth physical AI startup, about why this moment in robotics feels different from past hype cycles. Russ explains how machine learning has moved ahead of our theoretical understanding, and why that changes the role of robotics engineers. Instead of designing everything from first principles, teams are increasingly building systems they do not fully understand yet, then studying their behavior like scientists. Brian and Russ also discuss the long arc of robot locomotion, from passive dynamic walkers to today’s humanoid robots. Russ reflects on why bipedal walking was always the dream, why humanoid hardware has become surprisingly turnkey, and why the next exciting question is what AI can do with a powerful general-purpose body. The conversation also digs into one of the biggest debates in robotics right now: data. Russ argues that the robotics data problem is often framed the wrong way. Robots do not need to learn everything from scratch. Instead, he says the field can build on powerful video and multimodal models that already contain world knowledge, then train those models to output robot actions. Russ also explains the difference between large behavior models and vision-language-action models, why multitask pre-training may help with robustness, and why real-world deployment is the next major milestone for the field. Finally, Russ talks about launching a new physical AI company, why he believes robotics may have escape velocity this time, and why the future of work has to be central to the conversation. His goal is not just more capable robots. It is building systems that amplify people rather than replace them. Connect with Russ Tedrake https://www.linkedin.com/in/russ-tedrake-88648a4a Learn more about Russ Tedrake at MIT https://locomotion.csail.mit.edu/russt.html Learn more about Drake https://drake.mit.edu/ Learn more about Large Behavior Models from Toyota Research Institute https://toyotaresearchinstitute.github.io/lbm1/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. Also subscribe to the Automated Newsletter. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 https://www.automate.org/automation/automated-newsletter You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #43
    June 24 · 44 min

    Rick Faulk on Locus Robotics, Warehouse Automation, and Physical AI

    Warehouse automation is not about building the flashiest robot. It is about solving the right problem at scale. In this episode of Automated, Brian Heater speaks with Rick Faulk, CEO of Locus Robotics, about what it really takes to deploy robots inside working warehouses and why the future of physical AI may look very different from the humanoid hype cycle. Rick explains how Locus grew out of a major logistics problem. Quiet Logistics had been using Kiva robots before Amazon acquired Kiva and took the product off the market. Instead of returning to a manual operation, the team started building its own robotics solution inside the warehouse. That origin story shaped the company’s entire approach. Rick says many robotics companies fail because they start with the robot instead of the customer’s problem. Locus was different because it was built inside the environment it was trying to automate. Brian and Rick also discuss why fixed automation can be limiting in warehouses with seasonal peaks, shifting demand, labor shortages, and changing order volume. Rick explains why flexible systems, Robots-as-a-Service, and scalable deployments matter when operators need to handle holiday surges, back-to-school volume, and unpredictable demand. The conversation digs into one of the biggest topics in robotics right now: humanoids. Rick says humanoids may eventually play a role, but purpose-built warehouse robots have a clearer path to ROI today. In his view, the winning systems are not trying to fold laundry, make burgers, and work in a warehouse. They are designed to do one important job extremely well. They also get into Locus’s real-world data advantage. Rick says Locus has completed more than seven billion picks and is now doing around 150 picks per second. Every pick becomes part of a data flywheel that helps robots move more safely, respond to warehouse conditions, and improve productivity. Rick also breaks down Locus Array, the company’s autonomous Robots-to-Goods system. He explains why mobile manipulation is so difficult, why picking in a warehouse is much harder than it looks, and why Array is designed as a practical physical AI system for fulfillment. Finally, Brian and Rick discuss what automation means for warehouse workers, why robotics can create higher-value roles inside facilities, and how companies can compete in a logistics world shaped by Amazon-level expectations. Connect with Rick Faulk https://www.linkedin.com/in/rickfaulk Learn more about Locus Robotics https://locusrobotics.com/ Learn more about Locus Array https://locusrobotics.com/blog/locus-array-autonomous-warehouse-era We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #42
    June 17 · 46 min

    Aya Durbin on Turning Atlas Into a Real Industrial Robot

    Humanoid robots are everywhere in the headlines. But Aya Durbin says the real test is not whether a robot can impress people in a demo. It is whether that robot can deliver real value, positive ROI, and reliable performance inside industrial environments. In this episode of Automated, Brian Heater speaks with Aya Durbin, Director of Product for Atlas at Boston Dynamics, about what it will actually take to bring humanoid robots out of the lab and into the workforce. Aya explains why she considers herself both a dreamer and a pragmatist. Boston Dynamics has shown what is possible with legged robots, viral demos, and advanced mobility, but productizing Atlas means focusing on customer value, uptime, deployment, serviceability, and hard industrial work. The conversation explores why Atlas has legs, what Boston Dynamics learned from Spot and Stretch, and why the first meaningful humanoid deployments will likely happen in structured industrial environments before anything broader. Brian and Aya also dig into the reality behind Boston Dynamics’ famous robot videos. The backflips, gymnastics, and playful demos may look like fun, but Aya explains how many of those moments are tied to the same core technology used to train robots for real tasks. They also discuss why Atlas is being built around AI-based tools rather than hard-coded applications, how early customers will help shape the roadmap, and why integration, IT, security, downtime, and ROI are just as important as the robot itself. Finally, Aya outlines Boston Dynamics’ current timeline for Atlas, including customer pilots planned for 2028 and Hyundai’s commitment to building 30,000 Atlas robots a year starting in 2030. This is a grounded look at what humanoid robotics looks like beyond the hype, and what has to happen before Atlas becomes a trusted member of the industrial workforce. Connect with Aya Durbin https://www.linkedin.com/in/alexa-durbin Learn more about Boston Dynamics Atlas https://bostondynamics.com/products/atlas/ Thanks for being an Automated fan! Enter our giveaway to win robot-building sets from some of our favorite robotics companies and exclusive Automated swag. We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #41
    June 10 · 44 min

    Andrew Barry on Why Dexterity Is the Next Breakthrough in Physical AI

    Physical AI is moving quickly. But Andrew Barry says one of the biggest unlocks in robotics is not just getting robots to move through the world. It is getting them to touch, grasp, adjust, and manipulate the world with real dexterity. In this episode of Automated, Brian Heater speaks with Andrew Barry, co-founder and CTO of Generalist, about how the company is building general intelligence for the physical world and why dexterous robots may be the starting point for far more capable automation. Andrew explains why Generalist is focused on the tasks that are both difficult and valuable. Robots have made major progress in mobility, but their ability to manipulate objects is still limited. If robots can solve dexterity, they can become useful in a much wider range of real-world environments. The conversation explores how Generalist is collecting massive amounts of real-world manipulation data. Andrew describes the handheld data capture devices the company built, why they chose that approach over teleoperation, and how thousands of devices have helped them scale a much richer data set for robot learning. Brian and Andrew also discuss the commercial side of physical AI. Andrew explains why the company is not just chasing impressive demos, but benchmarking against real tasks people are already paying for today. That distinction matters because a viral robot demo is not the same thing as a deployable robotic system. They also dig into one of the most surprising parts of modern robot learning: improvisation. Andrew shares the moment when a robot picked up a baggie with the opposite hand from the one it had been trained on, completed the task anyway, and left the team realizing something very different was happening inside the model. The episode also covers Generalist’s GEN-1 model, the parallels between robotics and the early GPT era, why flexible objects like cables are so difficult to automate, what data flywheels may actually look like in robotics, and why robots sometimes learn human mistakes from the data they are trained on. Finally, Andrew reflects on his path from Boston Dynamics to the Broad Institute and then to Generalist, explaining how work in molecular biology, machine learning, transformers, and robotics all shaped the way he thinks about building intelligence for the physical world. Connect with Andrew Barry https://www.linkedin.com/in/andy-barry Learn more about Generalist https://generalistai.com/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm. Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. https://www.youtube.com/@automatedpodcast https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Subscribe to the Automated Newsletter: https://www.automate.org/automation/automated-newsletter Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #40
    June 3 · 49 min

    Daniel Rausch on How Alexa Was Rebuilt for the AI Era

    Alexa is entering a very different era. For years, voice assistants were built around rules, scripted responses, and carefully designed commands. But with the rise of large language models and generative AI, Amazon had to rethink what Alexa could be and how people might use it. In this episode of Automated, Brian Heater speaks with Daniel Rausch, Amazon’s Vice President of Alexa and Echo, about Alexa+, the company’s AI-powered evolution of its voice assistant. Daniel explains why the shift from traditional voice assistance to foundational AI assistance required a full rearchitecture of the technology behind Alexa. The conversation explores how Alexa moved from a deterministic system to one powered by more than 70 models, why customers do not care which model is working behind the scenes, and how Amazon thinks about choosing the right AI tool for the job. Brian and Daniel also discuss one of the biggest questions around AI assistants: trust. Daniel explains why Alexa is designed to understand that it is AI, why it should help people prioritize human relationships, and why guardrails matter as assistants become more conversational, personal, and ambient in the home. They also get into the smart home, where Daniel says Alexa+ is changing how people interact with connected devices. Instead of needing to know the right command or app, people can speak naturally, whether they are unlocking a door, checking a Ring camera, controlling lights, or asking for help while cooking. The conversation also covers Echo hardware, privacy controls, personality styles, language and dialect differences, AI’s impact on robotics, and why Daniel sees Amazon as an invention machine at a moment when AI is moving faster than ever. Connect with Daniel Rausch https://www.linkedin.com/in/danielrausch Learn more about Alexa+ https://www.amazon.com/alexaplus/dp/B0CXRRF584 Learn more about Amazon Echo devices https://www.amazon.com/b?ie=UTF8&node=210779651011 We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #39
    May 27 · 50 min

    Daniela Rus on Humanoid Robots, Physical AI, and the Future of Robotics

    Physical AI is moving fast. But Daniela Rus says the future of robotics will not be defined by viral humanoid robot demos alone. The real challenge is building robots that can understand the physical world, make safe decisions in real time, and work reliably outside controlled lab environments. In this episode of Automated, Brian Heater speaks with Daniela Rus, Director of MIT CSAIL, about humanoid robots, self-driving cars, embodied AI, on-device AI, robot learning, and why the next wave of artificial intelligence needs to move beyond the cloud and into the physical world. Daniela explains why humanoid robots are exciting, but not ready for prime time. A robot may look impressive in a short demo, but operating safely and consistently around people requires common sense, physical understanding, and real-world adaptability that robots still do not fully have. The conversation also explores why self-driving cars remain one of the hardest problems in robotics. Daniela breaks down the long tail of autonomous driving, from bad weather and unpredictable human behavior to the messy edge cases that make real-world deployment so difficult. Brian and Daniela also discuss why the future of AI robotics may depend on smaller, more efficient AI models that can run directly on devices. If a car is moving at 60 miles an hour, it cannot wait for the cloud to decide what to do next. For robotics, speed, safety, energy use, and reliability all point toward a hybrid future where AI runs both in the cloud and on the machine itself. Daniela also shares why physical AI needs more than video data. Robots interact with the world through forces, torques, motion, contact, and uncertainty. For many tasks, robot learning requires a deeper understanding of physics, not just visual imitation. The episode also moves into some of the most fascinating frontiers of AI and robotics, including Daniela’s work with Project CETI and the effort to better understand sperm whale communication using machine learning, robotics, and large-scale data collection. Finally, Daniela talks about AI systems that could help design robots from natural language prompts, why engineering constraints can drive creativity, what octopus intelligence can teach us about decentralized robots, and why this moment in robotics feels like the future researchers imagined decades ago is finally arriving. Connect with Daniela Rus https://www.csail.mit.edu/person/daniela-rus Learn more about MIT CSAIL https://www.csail.mit.edu/ Learn more about Liquid AI https://www.liquid.ai/team/daniela-l-rus Learn more about Project CETI https://www.projectceti.org/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org. You can find the transcript and more episodes of Automated at automated.fm. Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. Subscribe to the Automated Newsletter: https://www.automate.org/automation/automated-newsletter You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #38
    May 20 · 57 min

    Matthew Johnson-Roberson on Why Physical AI Still Has a Missing Piece

    Physical AI is moving fast. But Matthew Johnson-Roberson says robotics is still missing something fundamental. The field has data, models, and momentum, but it still does not have the simple learning objective that helped language models scale so quickly. In this episode of Automated, Brian Heater speaks with Matthew Johnson-Roberson, founding dean of Vanderbilt’s College of Connected Computing, about why physical AI may not follow the same playbook as large language models. Matthew explains why robotics still feels stuck between promise and deployment. We still do not live in a world where you can look out your window and see robots everywhere. That gap is not just about hype. It is about the difficulty of building systems that can learn from physical experience in a way that actually scales. Brian and Matthew also discuss what self-driving taught the broader automation world, why last-mile delivery still has not cracked scale, and what Amazon’s long arc with Kiva robots reveals about how real hardware progress actually happens. The conversation also explores healthcare, where Matthew says AI scribes are already making a real impact, even as outdated infrastructure like fax-based record sharing shows how much friction remains. That experience also helped inspire Patients.app, the startup he co-founded after watching how much clinician time gets lost to documentation. They also get into the tension between startups and academia. Matthew argues that startups are powerful vehicles for scaling known solutions, but much worse fits for decade-long research questions that still do not have clear answers. Finally, Matthew reflects on building Vanderbilt’s new College of Connected Computing, why higher ed can take on 30- and 40-year problems in a way few other institutions can, and how AI agents have changed his own workflow so dramatically that he says he has not directly written a line of code in three months. Connect with Matthew Johnson-Roberson https://www.linkedin.com/in/mattkjr Learn more about Vanderbilt’s College of Connected Computing https://computing.vanderbilt.edu/bio/matthew-johnson-roberson/ Learn more about Patients.app https://patients.app/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org. You can find the transcript and more episodes of Automated at automated.fm. Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. Subscribe to the Automated Newsletter: https://www.automate.org/automation/automated-newsletter You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #37
    May 13 · 46 min

    Sergey Levine on Why Real-World Data Will Define Physical AI

    Physical AI looks closer than ever. But the hardest part in robotics is not getting a machine to do one impressive task on camera. It is building systems that can improve from real-world experience, handle edge cases, and scale across different robots and environments. In this episode of Automated, Brian Heater speaks with Sergey Levine of Physical Intelligence about why robotics has reached an inflection point, and why progress now requires more than great models in a lab. Sergey explains why the next phase of robotics will depend on something much less flashy than a viral demo: collecting the right real-world data, learning from it efficiently, and building systems that improve through deployment. The conversation explores what makes a robot experience useful in the first place. Sergey describes a concept borrowed from child psychology called the “zone of proximal development,” where the best learning happens when a system is challenged just beyond what it can already do. For robots, that means creating environments where they can succeed, fail, adapt, and improve. Brian and Sergey also discuss how the bottleneck in robotics is changing. Basic motor skills are improving fast. The harder problem now is judgment. A robot may be able to clean dishes, but if it drops a clean plate on the floor, it still has to understand that the plate needs to be washed again. That kind of common sense remains one of the biggest unsolved challenges in physical AI. They also dig into one of the biggest debates in robotics right now: data. Sergey argues that real-world data collection is not the impossible obstacle many researchers once assumed. In fact, he believes the long-term path to better robots is more practical than people think. Deploy systems, collect experience, improve the model, and repeat. The conversation also covers why Physical Intelligence is focused on a general intelligence layer rather than a single-narrow product, why robots should not just be treated as metal versions of people, and what surprised Sergey most about controlling very different robot platforms with the same model. Finally, Sergey reflects on why Physical Intelligence is structured more like a lab than a traditional startup, why experimentation matters so much in modern AI, and how we may one day look back on this era as the moment AI moved beyond internet data and into the physical world. Connect with Sergey Levine https://www.linkedin.com/in/sergey-levine-5a31a24 Learn more about Physical Intelligence https://www.physicalintelligence.company/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org. You can find the transcript and more episodes of Automated at automated.fm Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. Subscribe to the Automated Newsletter: https://www.automate.org/automation/automated-newsletter You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #36
    May 6 · 50 min

    Colin Angle on Why Home Robots Failed Before and Why AI Changes Everything

    Home robots have been promised for decades. Most of them did not fail because the ambition was too small. They failed because the technology was not yet good enough to understand people, adapt to real homes, or earn a place in daily life. In this episode of Automated, Brian Heater speaks with Colin Angle, founder and CEO of Familiar Machines & Magic and co-founder of iRobot, about why this moment in robotics feels fundamentally different. After helping define consumer robotics with Roomba, Colin is now focused on a new category of robot built not just to perform tasks, but to understand context, respond with intention, and build long-term connections inside the home. The conversation explores why the hardest problem in robotics was never simply movement. For years, robots could hear commands and execute narrow tasks, but they struggled with situational awareness, context, and the complexity of real-world environments. Colin explains why recent advances in AI have changed that, making capabilities that once felt impossible now practical. Brian and Colin also revisit one of Roomba's most important lessons. A robot can technically work and still fail in the home. The real challenge is not just functionality. It is whether the product fits naturally into people’s routines. Colin shares why one of Roomba’s biggest failure modes was not a rare edge case, but something much more common: people turning it off because it was annoying at the wrong time, and never turning it back on. The conversation also digs into what physical presence adds to AI. Colin reflects on early iRobot experiments like My Real Baby and explains why embodied systems can create a deeper and more memorable connection than software on a screen. They also discuss why Colin believes the next major consumer robot will not be a humanoid trying to replicate human labor in the home. Instead, he argues the real opportunity is building machines people trust, enjoy interacting with, and want around over time. Privacy is another major part of that equation. Colin explains why home robots need to run on the edge, not rely on constant cloud streaming, and why trust, latency, and cost all matter just as much as technical capability. This conversation is a deep look at what held home robotics back, what AI has finally unlocked, and why the next breakthrough may come from building robots that feel less like tools and more like a natural part of everyday life. Connect with Colin Angle https://www.linkedin.com/in/colinangle/ Learn more about Familiar Machines & Magic https://www.familiarmachines.com/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org. You can find the transcript and more episodes of Automated at automated.fm. Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. Subscribe to the Automated Newsletter: https://www.automate.org/automation/newsletter-automation-roundup You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #35
    April 29 · 47 min

    Martial Hebert on Why Self-Driving Cars Took So Long and What Everyone Got Wrong About AI

    Self-driving cars were supposed to be everywhere by now. They are not. And the reason is not what most people think. In this episode of Automated, Brian Heater speaks with Martial Hebert, Dean of Carnegie Mellon University’s School of Computer Science, about the reality behind decades of robotics and AI development. Martial has spent more than 40 years at the Robotics Institute and worked on some of the earliest autonomous vehicle systems. From that perspective, the story is not about technology failing. It is about expectations being wrong. The core technology for self-driving cars has existed for years. What slowed everything down is something far less visible: validation, safety, and the challenge of proving these systems can operate reliably in the real world. That gap between “it works” and “it can be trusted” is where most timelines break. The conversation also explores why physical AI is fundamentally different from the AI most people are familiar with. Unlike software, robots have to operate in unpredictable environments, interact with people, and handle edge cases that cannot be fully simulated. Martial explains why simulation alone is not enough, and why real-world experimentation is still essential, even when it is slow, expensive, and difficult to scale. They also discuss the robotics data problem. While large language models benefit from massive amounts of internet data, robotics systems struggle to collect the kind of real-world data they actually need. Brian and Martial also dig into a deeper idea that often gets overlooked: progress in robotics is not just about better algorithms. It is about building long-term ecosystems of talent, culture, and expertise. That is part of what turned places like Carnegie Mellon into leaders in autonomy, and why many of today’s breakthroughs are the result of decades of accumulated work. They also explore the role of DARPA and long-term research funding, not as a way to build products quickly, but as a way to push the limits of what is possible and force entirely new breakthroughs. This conversation offers a grounded perspective on why progress in AI takes longer than expected and what it actually takes to move from impressive demos to systems that work in the real world. Connect with Martial Hebert https://www.linkedin.com/in/martial-hebert-76448756/ Learn more about Carnegie Mellon Robotics https://www.ri.cmu.edu/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org. You can find the transcript and more episodes of Automated at automated.fm. Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. Subscribe to the Automated Newsletter: https://www.automate.org/automation/newsletter-automation-roundup You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #34
    April 22 · 56 min

    Bren Pierce on Why Humanoid Robots Are Overhyped and What Actually Works in Robotics

    Humanoid robots are everywhere right now. From viral demos to bold promises about home automation, it often feels like the future has already arrived. But behind the scenes, the reality is far more complex. In this episode of Automated, Brian Heater speaks with Bren Pierce, founder of Kinisi Robotics and co-founder of Bear Robotics, about what it actually takes to build and deploy robots in the real world. Bren explains why many humanoid robot demonstrations are misleading. While the technology has made major advances in movement and control, real-world deployment is still limited by manipulation, reliability, and the complexity of unstructured environments. The conversation explores why household robotics may be further away than most people think. Despite impressive demos, creating a robot that can operate independently in a dynamic home environment remains an unsolved challenge that could take years to fully unlock. They also discuss the gap between robotics innovation and practical business applications. Many companies are still experimenting, often driven by internal pressure to adopt AI and automation, even when the return on investment is unclear. Bren shares lessons from building multiple robotics companies, including why focusing on real problems matters more than chasing hype. Instead of targeting futuristic home use cases, Kinisi is focused on warehouse and industrial environments where the technology can deliver value today. The episode also dives into the challenges of scaling robotics systems. From deployment complexity to training and usability, the biggest barrier is not just building the technology, but making it reliable and usable without requiring expert engineers. Brian and Bren also explore the parallels between robotics and autonomous vehicles, highlighting how long it can take for breakthrough technologies to transition from demos to real-world impact. This conversation offers a grounded perspective on where robotics actually stands today and what it will take to move from impressive demos to real deployment. Connect with Bren Pierce https://www.linkedin.com/in/brenpierce/ Learn more about Kinisi Robotics https://www.kinisirobotics.com/ We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org. You can find the transcript and more episodes of Automated at automated.fm. Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. Subscribe to the Automated Newsletter: https://www.automate.org/automation/newsletter-automation-roundup You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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  • #33
    April 15 · 45 min

    Ali Kashani on Last Mile Delivery, Robotics at Scale, and the Future of Autonomous Delivery

    Last-mile delivery is one of the most expensive and inefficient parts of the global supply chain. While goods can travel across oceans for just a few dollars, getting them from a local hub to a customer’s door remains disproportionately costly. In this episode of Automated, Brian Heater speaks with Ali Kashani, CEO of Serve Robotics, about the realities of deploying delivery robots in the real world and what it takes to scale autonomous systems beyond early pilots. Ali explains how Serve Robotics evolved from an internal Postmates project into an independent company operating thousands of robots in live environments. This transition reflects a broader shift in robotics from controlled experimentation to real-world deployment at scale. The conversation explores why building in the real world is essential for robotics. Lab environments often miss critical edge cases, while public deployment reveals the unpredictable human behavior, operational challenges, and environmental complexity that define real performance. They also discuss the economic implications of reducing last-mile delivery costs. Lowering delivery from $10 to closer to $1 could unlock new demand, expand local economies, and create new categories of jobs that support and operate these systems. The episode also examines safety, public perception, and the long-term impact of autonomous delivery on cities. From reducing reliance on cars to improving walkability and safety, these systems may reshape how urban environments function. Brian and Ali also explore scaling challenges, lessons from acquisitions, and the operational realities of running thousands of robots in public. From unexpected real-world incidents to long-term infrastructure shifts, this conversation offers a grounded look at what it takes to bring robotics into everyday life. We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org. You can find the transcript and more episodes of Automated at automated.fm. Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. Subscribe to the Automated Newsletter: https://www.automate.org/automation/newsletter-automation-roundup You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/ Hosted on Acast. See acast.com/privacy for more information. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

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