Automated with Brian Heater

A humanoid robot’s skin can be part of its intelligence.Daniele Pucci is building around that idea at Generative Bionics.The company’s co-founder and CEO joins Brian Heater to explain GENE.01’s tactile skin and proximity sensing, and why a robot’s body and AI have to be designed together.That becomes especially important when a machine is working in direct contact with a person. Pucci describes research using wearables to measure human fatigue, with the goal of having a robot recognize when a worker needs physical assistance.He also explains why a welding humanoid and a hospital robot should share a platform without being identical machines. The job determines the AI, protective covers, and end effectors.His route to those decisions includes iCub, ergoCub, and the jet-powered iRonCub. The conversation closes with why he put the flying project on hold and what it takes to turn robotics research into products customers will use.Connect with Daniele Pucci:https://www.linkedin.com/in/daniele-pucci-78428420/Learn more about Generative Bionics:https://gbionics.ai/Explore GENE.01:https://gbionics.ai/gene01/Explore the iRonCub research project:https://ami.iit.it/aerial-humanoid-roboticsWe’d love to hear from you. Have thoughts or guest suggestions?Reach us at [email protected] 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-newsletterArt Is Not a ThingFrom media art to digital activism: art as critical inquiry and world-building.Listen on: Apple Podcasts   SpotifyLinkedInhttps://www.linkedin.com/showcase/automated-podcast-by-a3/Instagramhttps://www.instagram.com/automatedpod/

Boris Sofman helped bring Cozmo into people’s homes and Waymo’s vehicles onto public roads. At Bedrock Robotics, he is applying those lessons to construction equipment.Update, November 3, 2026: Bedrock Robotics announced a $600 million Series C on October 26 at a $4.1 billion valuation, bringing total funding to nearly $1 billion. The company says its fully autonomous excavators began paid work on commercial construction sites in August 2026. In this episode of Automated, Brian Heater speaks with Boris about why construction offers a different path to commercial autonomy. Bedrock is building a digital operator for machines that contractors already own and know how to use.Boris also reflects on Anki’s rise to more than $100 million in annual revenue at its peak, the pressures of a seasonal hardware business, and why he expects industrial applications to lead the early waves of humanoid adoption.The conversation explores Carnegie Mellon’s influence on robotics, the lasting impact of DARPA’s competitions, and what Cozmo’s expressive personality revealed about human-robot interaction.Learn more about Boris Sofman and the Bedrock team:https://bedrockrobotics.com/about Bedrock RoboticsLearn more about Bedrock Robotics:https://bedrockrobotics.com/We’d love to hear from you. Have thoughts or guest suggestions?Reach us at [email protected] 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-newsletterArt Is Not a ThingFrom media art to digital activism: art as critical inquiry and world-building.Listen on: Apple Podcasts   SpotifyLinkedInhttps://www.linkedin.com/showcase/automated-podcast-by-a3/Instagramhttps://www.instagram.com/automatedpod/

A useful robot has to earn its place in someone’s workflow.For Rodney Brooks, that means safe interactions, measurable results, and customers who want more.Recorded in front of a live audience at Circuit Launch in Mountain View with Silicon Valley Robotics, this episode of Automated brings Brian Heater back together with the show’s first returning guest. Rodney, co-founder and CTO of Robust.AI and co-founder of iRobot, draws on decades of building robots to explain what it takes to turn technical progress into a working business.Rodney discusses scaling beyond 100 deployed robots, handling unreliable warehouse Wi-Fi, and designing machines that people can take control of. He shares what his team learned when workers used emergency stops to reserve their robots, along with safety lessons from Roomba, Baxter, and Sawyer.The conversation also explores physical AI and the limits Rodney sees in learning manipulation from video. He explains why forces and touch matter, how robot eyes can communicate intent, and where specialized machines could help an aging population.Brian and Rodney examine the difference between a pilot and a deployment, the human work surrounding autonomous vehicles, and privacy concerns around teleoperated home robots. Rodney’s test for a robotics business includes a customer-support team that can respond at 3 a.m., measurable ROI that leads to repeat orders, and sustainable economics for each robot shipped.Learn more about Rodney Brooks and read his writing:https://rodneybrooks.com/blog/ Rodney BrooksLearn more about Robust.AI:https://www.robust.ai/ AI-Powered Collaborative Mobile RobotsLearn more about Silicon Valley Robotics:https://www.svrobo.org/ svrobo.orgLearn more and register for IEEE Humanoids 2026, December 6–9 in Santa Clara:https://2026.ieee-humanoids.org/We’d love to hear from you. Have thoughts or guest suggestions?Reach us at [email protected] 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-newsletterArt Is Not a ThingFrom media art to digital activism: art as critical inquiry and world-building.Listen on: Apple Podcasts   SpotifyLinkedInhttps://www.linkedin.com/showcase/automated-podcast-by-a3/Instagramhttps://www.instagram.com/automatedpod/

What would it take for one person to manage an entire warehouse?Brightpick CEO Dr. Jan Zizka sees that future taking shape through AI-powered picking, autonomous night shifts and robots that can ask for help.In this episode of Automated, Brian Heater speaks with Jan about building warehouse automation that customers can deploy, operate and scale. The conversation traces his path from Slovakia and MIT to co-founding Photoneo and Brightpick.Jan shares how childhood friendships helped shape his founding team, how Photoneo’s 3D vision technology gave industrial robots better ways to see and handle objects, and why Zebra Technologies became the company’s next home. He explains why Brightpick took a broader approach, building complete fulfillment systems around the customer’s operation.Brian and Jan explore AI’s ability to pick unfamiliar products, the lights-out night shift at The Feed’s Denver warehouse, and the work that still needs people. Jan explains why human assistance and multiple fallback layers remain essential as automation expands.They also discuss Gridpicker’s approach to throughput and storage density, a simulation reaching 50,000 order lines per hour, and Jan’s account of the Ocado injunction that interrupted its LogiMAT demonstration. Throughout, the conversation returns to deployment speed, ROI and the challenge of turning a capable robot into a reliable warehouse system.Connect with Dr. Jan Zizka:https://www.linkedin.com/in/jzizka LinkedInLearn more about Brightpick:https://brightpick.ai/ Warehouse automation made easyExplore Brightpick Gridpicker:https://brightpick.ai/gridpicker/ BrightpickWe’d love to hear from you. Have thoughts or guest suggestions?Reach us at [email protected] 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-newsletterArt Is Not a ThingFrom media art to digital activism: art as critical inquiry and world-building.Listen on: Apple Podcasts   SpotifyLinkedInhttps://www.linkedin.com/showcase/automated-podcast-by-a3/Instagramhttps://www.instagram.com/automatedpod/

What if the biggest bottleneck in robotics is the way robots are taught?Jagdeep Singh believes the industry needs a much larger and more diverse source of training data to build robots that can handle the unpredictability of real work. At Rhoda AI, that starts with internet video.In this episode of Automated, Brian Heater speaks with Jagdeep, co-founder and CEO of Rhoda AI, about the company’s approach to physical AI, why he sees limits in vision-language-action models, and what it will take to move generalist robots from demonstrations into factories and warehouses.Jagdeep explains how Rhoda pretrains its model on hundreds of millions of video clips to learn how the physical world moves. The goal is to build an understanding of motion, forces, and object behavior before teaching a particular job. He says that foundation has allowed Rhoda to learn specific tasks with 10–20 hours of robot data, rather than the much larger datasets associated with other approaches.The conversation explores why footage of a specific task is not always the best starting point. Jagdeep describes how broader video datasets performed better than more narrowly curated collections of people doing jobs. Task-specific learning comes afterward, followed by additional learning from the unusual situations robots encounter with customers.That distinction became clear when Rhoda took a container-folding task from the lab into a factory. Broken latches and rusted equipment changed the conditions the model had learned. Jagdeep says the team adapted using roughly three hours of additional factory data, illustrating why deployment requires more than repeating a successful demonstration.Brian and Jagdeep also discuss how learning can compound across customers, why access to deployment data is central to Rhoda’s business, and the difference between customers owning their data and allowing the company to use it for model training.The conversation then turns to hardware. Jagdeep outlines four requirements driving Rhoda’s development: payload capacity, reliability, safety and dexterity. He explains why the company is building components it could not buy, how it is weighing the tradeoffs of three-finger hands, and why its mobile manipulators use wheels.Finally, Jagdeep explains why customers ultimately judge robots by success rate and cycle time. He shares Rhoda’s plans to deploy its model on existing hardware while developing its own platform, the role of human demonstrations in teaching new tasks, and why reliable performance in customer environments is the test that matters.Connect with Jagdeep Singhhttps://www.linkedin.com/in/jagdeep-singh-3b2a9222Learn more about Rhoda AIhttps://www.rhoda.ai/Explore Rhoda AI’s researchhttps://www.rhoda.ai/research/direct-video-actionWe’d love to hear from you. Have thoughts or guest suggestions?Reach us at [email protected] 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-newsletterArt Is Not a ThingFrom media art to digital activism: art as critical inquiry and world-building.Listen on: Apple Podcasts   SpotifyLinkedInhttps://www.linkedin.com/showcase/automated-podcast-by-a3/Instagramhttps://www.instagram.com/automatedpod/

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 [email protected] 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/Art Is Not a ThingFrom media art to digital activism: art as critical inquiry and world-building.Listen on: Apple Podcasts   SpotifyLinkedInhttps://www.linkedin.com/showcase/automated-podcast-by-a3/Instagramhttps://www.instagram.com/automatedpod/

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-panettaLearn 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 [email protected] 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-newsletterArt Is Not a ThingFrom media art to digital activism: art as critical inquiry and world-building.Listen on: Apple Podcasts   SpotifyLinkedInhttps://www.linkedin.com/showcase/automated-podcast-by-a3/Instagramhttps://www.instagram.com/automatedpod/

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/6372229233112Learn more about Texas Instruments:https://www.ti.com/about-ti.htmlRead 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.htmlWe’d love to hear from you. Have thoughts or guest suggestions?Reach us at [email protected] 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-newsletterArt Is Not a ThingFrom media art to digital activism: art as critical inquiry and world-building.Listen on: Apple Podcasts   SpotifyLinkedInhttps://www.linkedin.com/showcase/automated-podcast-by-a3/Instagramhttps://www.instagram.com/automatedpod/

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 Zhaohttps://www.linkedin.com/in/tony-z-zhaoLearn more about Sunday Roboticshttps://www.sunday.ai/Apply for the Memo beta programhttps://www.sunday.ai/beta-programExplore ALOHAhttps://tonyzhaozh.github.io/aloha/Explore the Universal Manipulation Interfacehttps://umi-gripper.github.io/Learn more about ACT-2https://www.sunday.ai/blog/act-2-previewWe’d love to hear from you. Have thoughts or guest suggestions?Reach us at [email protected] can find the transcript and more episodes of Automated at https://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-newsletterArt Is Not a ThingFrom media art to digital activism: art as critical inquiry and world-building.Listen on: Apple Podcasts   SpotifyLinkedInhttps://www.linkedin.com/showcase/automated-podcast-by-a3/Instagramhttps://www.instagram.com/automatedpod/

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 Paradahttps://www.linkedin.com/in/carolinaparadaExplore Gemini Robotics 2https://deepmind.google/blog/gemini-robotics-2-brings-whole-body-intelligence-to-robots/Register for the Automated Happy Hour with Rodney Brookshttps://luma.com/n688dcl5Learn more about the Advanced Vision & AI Conferencehttps://www.automate.org/events/advanced-vision-and-ai-conferenceWe’d love to hear from you. Have thoughts or guest suggestions?Reach us at [email protected] 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-newsletterArt Is Not a ThingFrom media art to digital activism: art as critical inquiry and world-building.Listen on: Apple Podcasts   SpotifyLinkedInhttps://www.linkedin.com/showcase/automated-podcast-by-a3/Instagramhttps://www.instagram.com/automatedpod/