Épisodes

  • Andie Zhang on Why Industrial Robots Still Beat Humanoids
    Oct 7 2026
    Humanoid robots are receiving more attention than almost any other form of physical AI. But Andie Zhang says factory buyers still care more about speed, precision, reliability, safety, and ROI than a human shape.In this episode of Automated, recorded at the inaugural Davos Tech Summit, Brian Heater speaks with Andie, Global Portfolio and Technology Manager for Collaborative Robots at ABB, about why industrial robot arms are not going away, where humanoids still fall short, and how physical AI can unlock factory tasks that traditional automation cannot solve.Andie explains why the form of a robot should follow the job it needs to perform. Humanoids are designed to do many different things, but that versatility does not automatically make them the best option for industrial manufacturing. When a specialized robot can complete the same task faster, more precisely, and at a lower cost, companies have little incentive to pay for additional joints or capabilities that do not improve the application.The conversation also explores how physical AI can expand automation without replacing the systems already working reliably inside factories. Structured and repetitive tasks can still be handled through traditional programming. AI becomes valuable when robots must work in unstructured environments, interpret sensor information, adapt their behavior, or handle flexible objects that cannot be positioned the same way every time.Andie describes how simulation, digital twins, synthetic images, and AI vision are helping manufacturers prepare and test automation systems before deploying them on-site. She also explains how ABB developed an AI-based system that can recognize and pick flexible clothing packages, a task that has traditionally required large numbers of workers because every package looks and behaves differently.Brian and Andie also discuss why labor shortages are accelerating automation around the world. After visiting more than 100 customer plants, Andie says manufacturers consistently turn to robots because they cannot find enough qualified workers. She shares how cobots are helping experienced welders teach robots to perform repetitive welds, allowing companies to increase productivity without removing those workers from the process.They also examine the safety questions surrounding humanoids, the difficulty of developing dexterous five-finger hands, and the importance of making increasingly complex robots usable for factory workers and system integrators.Finally, Andie reflects on becoming the first person responsible for cobots at ABB, building an internal robotics incubator, taking robots directly to small businesses, and creating more opportunities for women and people with transferable skills to enter the robotics industry.Connect with Andie Zhanghttps://www.linkedin.com/in/andiezhang/Learn more about ABB Roboticshttps://www.abb.com/global/en/areas/roboticsExplore ABB collaborative robotshttps://www.abb.com/global/en/areas/robotics/products/robots/collaborative-robotsLearn more about RobotStudio HyperRealityhttps://www.abb.com/global/en/areas/robotics/innovation/robotstudio-hyperrealityWe’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/@automatedpodcasthttps://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6https://www.automate.org/automation/automated-newsletterYou can also find us on:LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/Instagram https://www.instagram.com/automatedpod/LinkedInhttps://www.linkedin.com/showcase/automated-podcast-by-a3/Instagramhttps://www.instagram.com/automatedpod/
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    1 h et 1 min
  • Karen Panetta on Digital Twins, Inclusive AI and Engineering for Impact
    Sep 30 2026

    AI should not be judged only by how advanced it is. It should also be judged by who it helps and who its designers remembered to include.


    In this episode of Automated, Brian Heater speaks with Karen Panetta, Distinguished Professor and Dean for Graduate Education at Tufts University and founder of Nerd Girls, about using engineering, robotics, and artificial intelligence to solve problems with real human impact.


    Karen traces that philosophy back to student projects involving a solar car and Thacher Island, where her team helped power historic lighthouses with solar energy. The goal was never simply to build one car or install one system. It was to show young engineers that the same technical skills could move from one application to another and improve people’s lives.


    That approach now runs through her work in food safety, firefighting, traffic management, marine health, infrastructure, and assistive technology. Some projects have become startups, while others serve populations too small to attract traditional investment. Karen explains why universities remain essential when an important problem does not come with an obvious market.


    The conversation also explores her early work at Digital Equipment Corporation, the simulation tools that helped lay the foundation for digital twins, and the moment at NASA Langley when software she was accustomed to testing in isolation began controlling real jet and propulsion hardware.


    Brian and Karen also discuss bias in physical AI, accessibility, and the business cost of designing for only one kind of user. From early crash-test dummies to modern interfaces, Karen argues that excluding people with different bodies, abilities, or ways of learning is both a design failure and a missed market.


    Her larger message is simple: engineering is not reserved for people who fit one academic mold. Creativity, imagination, determination, and a willingness to work across disciplines are what turn technology into meaningful change.


    Learn more about Karen Panetta at Tufts University:
    https://engineering.tufts.edu/about/undergraduate-and-graduate-deans/dean-karen-panetta


    Learn more about Nerd Girls:
    https://nerdgirls.com/


    Explore Karen Panetta’s research:
    https://www.karenpanetta.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


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    46 min
  • Dr. Ahmad Bahai on the Semiconductor Race Behind AI and Robotics
    Sep 23 2026

    AI and robotics may look like software revolutions.


    But every breakthrough depends on the semiconductors underneath them.


    In this episode of Automated, Brian Heater speaks with Dr. Ahmad Bahai, chief technology officer at Texas Instruments, about the chips, power systems, sensors, and edge-processing technologies enabling the next generation of AI and robotics.


    Ahmad explains why companies cannot predict the next major market a decade in advance. What they can see are the underlying trends, including the growing demand for power density, bandwidth, timing accuracy, and local processing. Those needs now extend from massive AI data centers to mobile devices and robots that must react without waiting for the cloud.


    Brian and Ahmad also trace his path from early Wi-Fi research at Bell Labs to Kilby Labs and Texas Instruments. Ahmad reflects on what made Bell Labs unique, why that model is difficult to reproduce today, and how companies can connect academic research with technology that can be manufactured at scale.


    The conversation also covers the semiconductor supply chain, the search for more efficient AI algorithms, and why technologies like autonomous vehicles and humanoid robots take longer to reach the mainstream than early predictions suggest.


    Finally, Ahmad explains why supposed physical limits rarely end technological progress. Scientists once argued that chips could not scale below 55 nanometers. Engineers found another way, and today the industry has reached two nanometers.


    Learn more about Dr. Ahmad Bahai and semiconductor innovation at Texas Instruments:
    https://www.ti.com/video/6372229233112


    Learn more about Texas Instruments:
    https://www.ti.com/about-ti.html


    Read TI’s insights on scaling humanoid robots: https://www.ti.com/about-ti/behind-chip/articles/what-will-it-take-to-bring-humanoid-robots-into-the-real-world-read-our-experts-insights.html


    We’d love to hear from you. Have thoughts or guest suggestions?
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    You can find the transcript and more episodes of Automated at automated.fm


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    50 min
  • Tony Zhao on Why Home Robots Could Unlock General Intelligence
    Sep 18 2026

    A robot does not need another robot to learn how to work in a home.


    That idea is at the center of Sunday Robotics. The company is building Memo, a general-purpose home robot trained with human data collected through its custom Skill Capture Glove.


    In this episode of Automated, Brian Heater speaks with Sunday Robotics co-founder and CEO Tony Zhao about physical AI, robot learning, and why he left Stanford without keeping an academic fallback. Tony traces the company's foundation to ALOHA and the Universal Manipulation Interface, two research efforts that changed how he thought about scaling robotic intelligence.


    Tony explains why data remains one of robotics' biggest bottlenecks. Sunday's glove closely matches Memo's hand, allowing people to capture high-quality manipulation data while performing ordinary tasks. This approach is cheaper and easier to distribute than collecting every training example through a robot.


    Brian and Tony also discuss how smarter AI could compensate for simpler, lower-cost hardware, why Memo could eventually be priced more like a gaming PC or phone, and why a home robot does not need to work at human speed. If the robot can finish the dishes, laundry, and tidying while its owner is away, reliability and capability matter more than speed.


    Finally, Tony explains why a polished robot video is not the same as repeatable real-world deployment. He argues that the variability of homes creates what Sunday calls research-market fit, pushing robots toward the broad, adaptable intelligence they would need to work around people and eventually move into services and industrial environments.


    Connect with Tony Zhao
    https://www.linkedin.com/in/tony-z-zhao


    Learn more about Sunday Robotics
    https://www.sunday.ai/


    Apply for the Memo beta program
    https://www.sunday.ai/beta-program


    Explore ALOHA
    https://tonyzhaozh.github.io/aloha/


    Explore the Universal Manipulation Interface
    https://umi-gripper.github.io/


    Learn more about ACT-2
    https://www.sunday.ai/blog/act-2-preview




    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 https://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.
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    54 min
  • Carolina Parada on Gemini Robotics, Robot Data and Physical AGI
    Sep 17 2026

    Ten years ago, Google DeepMind’s robotics team wanted to build a single model that could go from pixels to motor control. People thought the idea was crazy. Today, much of general-purpose robotics is moving in that direction.


    In this episode of Automated, Brian Heater speaks with Carolina Parada, VP and head of robotics at Google DeepMind, about the ideas behind Gemini Robotics and the company’s plan to build an AI layer that can power many different kinds of robots.


    Carolina traces her path from speech recognition at Google to self-driving perception at NVIDIA and explains why she has repeatedly pursued technical fields on the verge of transformation. That experience taught her to question established approaches instead of settling for incremental improvements.


    She explains why general-purpose robots face an even harder challenge than autonomous vehicles. Roads have lanes, traffic lights, and established rules. Robots operating around people must navigate unstructured environments, adapt to unfamiliar objects, and understand how to “read the room.”


    Carolina also breaks down the family of models behind Gemini Robotics, including embodied reasoning, vision-language-action models, and reinforcement-learned whole-body control. She explains how these systems work together and why a smaller on-device model may be more useful when reliability matters more than maximum intelligence.


    Brian and Carolina discuss why useful robotics does not have to wait for physical AGI, how Gemini Robotics is expanding what Boston Dynamics’ Spot can do during inspections, and why logistics and manufacturing are likely to see capable systems first. They also explore Google DeepMind’s partnerships with Apptronik, Boston Dynamics, and Agility Robotics, and why Carolina believes one robot will not win it all.


    Finally, Carolina explains why robot data alone cannot scale and why physical AI will require a mix of real-world experience, simulation, human video, and robots learning from their own mistakes.


    Connect with Carolina Parada
    https://www.linkedin.com/in/carolinaparada


    Explore Gemini Robotics 2
    https://deepmind.google/blog/gemini-robotics-2-brings-whole-body-intelligence-to-robots/


    Register for the Automated Happy Hour with Rodney Brooks
    https://luma.com/n688dcl5


    Learn more about the Advanced Vision & AI Conference
    https://www.automate.org/events/advanced-vision-and-ai-conference


    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.
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    52 min
  • Clara Vu on Why General-Purpose Robots Are So Hard to Ship
    Sep 16 2026
    Humanoid robots are attracting enormous investment and dominating the conversation around physical AI.But Clara Vu believes the form factor is a trap. The more a robot is expected to look and work like a person, the more people assume it has human-level capabilities. In reality, forcing spinning motors into a body designed around tendons can add unnecessary constraints before engineers even begin solving the task.In this episode of Automated, Brian Heater speaks with veteran roboticist and Veo Robotics co-founder Clara Vu about what nearly three decades of building autonomous systems has taught her about the gap between a compelling demo and a product that works in the real world.Clara traces her career from joining a 10-person iRobot before she was legally old enough to drink to working on oil-well exploration robots, Hasbro's My Real Baby doll, and the software framework later used for Roomba. She also shares how toy manufacturing taught iRobot to build at consumer scale, why Harvest Automation chose potted plants over fruit picking, and how her work at Rethink Robotics helped lead to Veo.The conversation gets into one of the hardest truths in robotics: a prototype that works 80% of the time may represent only 2% of the work required to ship. Clara explains why edge cases, environmental variability, reliability, and exception handling consume nearly all the effort, and why a task that takes three to five years to commercialize should look like something a robotics graduate student could prototype in three to five weeks.Brian and Clara also examine the current humanoid boom. Clara argues that venture capital rewards companies that can claim enormous markets, which makes a general-purpose humanoid an appealing pitch. The problem is that the more environments and tasks a system must handle, the harder it becomes to make that system reliable enough to deploy.Instead, Clara makes the case for a new version of systems integration that combines existing robot arms, sensors, computer vision, AI, and software with application-specific engineering. That model may not produce one machine that does everything, but it could automate far more of the difficult work happening in factories, farms, warehouses, and other environments today.The technology already exists to automate many of those tasks. The bigger challenge is choosing the right problems, allowing for profitable customization, and building funding models that support focused solutions. As Clara puts it, if the job is moving a pallet, make the pallet jack intelligent instead of building a humanoid to operate it.Connect with Clara Vuhttps://www.linkedin.com/in/clara-vu-244941/Learn more about Veo Robotics and its acquisition by Symbotichttps://www.symbotic.com/news/symbotic-acquires-veo-robotics-to-enhance-efficiency-and-safety-innovation/We'd love to hear from you. Have thoughts or guest suggestions?Reach us at podcast@automate.orgYou can find the transcript and more episodes of Automated at automated.fmUnlock 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/@automatedpodcasthttps://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6https://www.automate.org/automation/automated-newsletterYou can also find us on:LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/Instagram https://www.instagram.com/automatedpod/LinkedInhttps://www.linkedin.com/showcase/automated-podcast-by-a3/Instagramhttps://www.instagram.com/automatedpod/
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    46 min
  • Dhruv Batra on What AI Still Cannot Do in the Physical World
    Sep 15 2026

    AI can speak fluently, reason through problems, and describe the world.


    Getting it to do something in the world is a much harder problem.


    In this episode of Automated, Brian Heater speaks with Dhruv Batra, co-founder and chief scientist at Yutori, about the gap between language models that generate answers and intelligent systems that can take action.


    Dhruv explains why many post-ChatGPT robot demos are still little more than a physical wrapper around a language model. A robot may be able to hold a convincing conversation, but that does not prove it can navigate, manipulate objects, understand physical space, or respond to the movement of people around it.


    The conversation begins with an even more fundamental question: What does it mean for an AI system to have a belief? Dhruv traces the idea back to Bayesian probability and explains why a rational decision-maker should never assign exactly zero probability to an event. Once a possibility reaches zero, no amount of new evidence can change that belief.


    Brian and Dhruv also examine the human language used to describe artificial intelligence. Words like “belief” and “thinking” carry rich meanings in everyday life, but AI researchers often use them in much narrower technical ways. Dhruv argues that this trade-off can create confusion, while also giving researchers something precise enough to measure and improve.


    They trace Dhruv’s path from probabilistic machine learning and computer vision to visual question answering, Grad-CAM, embodied AI at Meta FAIR, and eventually Yutori. Along the way, he explains how his team trained virtual robots to navigate directly from pixels to actions without building a map, using LiDAR, or separating perception from planning.


    The conversation also explores why simulation has earned a complicated reputation in robotics, where sim-to-real transfer works well for locomotion but remains far more difficult for raw camera images and dexterous manipulation. Dhruv recalls the blunt question roboticists asked whenever he presented work in simulation: “Did you touch a robot?”


    At Yutori, Dhruv is now applying many of those embodied AI ideas to web agents. These systems perceive a browser through screenshots and take actions such as clicking buttons, entering information, and completing tasks. He calls them “robots of the web.” Unlike traditional automation, they do not depend on a website remaining perfectly structured or unchanged.


    The result is a wide-ranging conversation about uncertainty, paradigm shifts, robot learning, web agents, and the difference between an AI system that can answer a question and one that can act on your behalf.


    Connect with Dhruv Batra

    https://www.linkedin.com/in/dhruv-batra-dbatra/


    Learn more about Dhruv Batra

    https://dhruvbatra.com/


    Learn more about Yutori

    https://yutori.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.

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    58 min
  • Amanda Prorok on Robot Teams, Physical AI, and Trust
    Sep 14 2026

    The future of robotics may not belong to one machine that can do everything. It may belong to teams of specialized robots that learn how to work together.


    In this special episode of Automated, recorded at the Davos Tech Summit in Switzerland, Brian Heater speaks with Amanda Prorok, professor of collective intelligence and robotics at the University of Cambridge and founder of the Prorok Lab.


    Amanda explains why she believes the world is built on collective intelligence. From ants using pheromone trails to find the shortest path to food to robot teams dividing complex work among specialized members, nature repeatedly shows that cooperation can produce capabilities no individual agent possesses alone.


    That principle produced a surprising result in Amanda’s own research. Her team gave a group of AI agents one simple objective: score a goal. Without being taught human soccer strategy, the agents organized themselves into goalkeepers, defenders, and attackers. They independently discovered the same role specialization humans developed for the game.


    Brian and Amanda also explore the challenges of coordinating robots with different bodies and capabilities. Amanda explains how drones, wheeled robots, and legged robots can share information even when they move through the world differently, and how a “blind” robot can navigate using cameras distributed throughout its environment.


    The conversation then turns to one of the biggest unresolved questions in physical AI: trust. Many systems are being deployed based on empirical performance rather than mathematical guarantees. A robot may work well most of the time while still failing in ways researchers cannot fully explain.


    Amanda shares a personal moment that brought that uncertainty into focus after she stood beside a humanoid robot with her three-week-old baby. She also describes her lab’s work on remotely detectable robot policy watermarks, which could allow someone to record a robot with a phone and verify the origin of the policy controlling its behavior.


    Connect with Amanda Prorok
    https://www.linkedin.com/in/aprorok/


    Learn more about the Prorok Lab
    https://www.proroklab.org/


    View Amanda’s University of Cambridge profile
    https://www.cst.cam.ac.uk/people/asp45


    Explore the robot policy watermarking research
    https://www.proroklab.org/publications/iclr2026-watermarking/

    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.
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    42 min