Automated with Brian Heater

Karen Panetta on Digital Twins, Inclusive AI and Engineering for Impact

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 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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Dr. Ahmad Bahai on the Semiconductor Race Behind AI and Robotics

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? 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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Tony Zhao on Why Home Robots Could Unlock General Intelligence

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. 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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Carolina Parada on Gemini Robotics, Robot Data and Physical AGI

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. 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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Clara Vu on Why General-Purpose Robots Are So Hard to Ship

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 Vu https://www.linkedin.com/in/clara-vu-244941/ Learn more about Veo Robotics and its acquisition by Symbotic https://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.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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Dhruv Batra on What AI Still Cannot Do in the Physical World

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. 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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Amanda Prorok on Robot Teams, Physical AI, and Trust

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. 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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This is Automated

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 impacting how we live and work. The show features conversations with visionaries from AWS, NVIDIA, Universal Robots, and beyond. From groundbreaking research in humanoid robotics to real-world industrial applications, each weekly episode explores the people and ideas shaping the future of work, industry, and society. Subscribe and be the first to get new episodes on Wednesdays. LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ Instagram https://www.instagram.com/automatedpod/

Jan Liphardt on Building the AI Brain for Humanoid Robots

Humanoid robots can walk, gesture, and look astonishingly capable in a polished demo. Then Jan Liphardt bought one for his house, along with three quadruped robots, and discovered how quickly the illusion breaks. After charging the batteries and getting the humanoid to stand, it simply stood there. It could not become the R2-D2-like helper, companion, teacher, or coworker he had imagined. In this episode of Automated, Brian Heater speaks with Jan Liphardt, founder and CEO of OpenMind and associate professor of bioengineering at Stanford University, about the software, intelligence, and trust systems that robots still need before they can become useful parts of everyday life. Jan explains why the final 3% of humanoid deployment can consume nearly all the work. A robot that performs well in a lab still has to handle traffic, animals, wet pavement, moving leaves, different languages, and countless other edge cases before it can operate safely in an unstructured environment. The conversation explores why OpenMind is deliberately not trying to solve every robotics problem. Rather than building hands, arms, or high-speed control systems, the company combines its own models with technology from other teams. Its OM1 runtime and FABRIC coordination layer are designed to help different robots become more capable, understandable, and useful around people. Brian and Jan also discuss why social intelligence requires much more than speech. A compelling humanoid has to track attention, move its head and shoulders, use its hands, understand body language, remember preferences, and respond in ways that feel natural. Jan even argues that a robot’s imperfections can strengthen the bond between the machine and the person helping it. They also dig into one of OpenMind’s most distinctive ideas: connecting sensors and models through natural language so people can inspect how a robot reaches a decision. That approach trades some speed for intelligibility, giving developers a clearer way to debug behavior, add guardrails, and improve governance. Jan also challenges the idea that robotics has one universal data problem. Teaching math, folding a T-shirt, and completing an assembly task require very different data and system designs. In some cases, he argues, a robot may learn more effectively from physical constraints than from watching millions of videos of humans. The conversation also covers Jan’s path from physics and bioengineering into robotics, why elite degrees do not always predict great engineers, what a 17-year-old calling from a robot field trial taught him about hiring, and why combining academia with the real world can produce better research questions. Connect with Jan Liphardt https://www.linkedin.com/in/jan-liphardt Learn more about OpenMind https://openmind.com/ Learn more about Jan’s work at Stanford https://profiles.stanford.edu/jan-liphardt Register for the Automated Happy Hour in Mountain View on September 22 https://luma.com/n688dcl5 Learn more about the Advanced Vision and AI Conference on September 23 and 24 https://www.automate.org/events/advanced-vision-and-ai-conference/register 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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Rajat Bhageria on Why Chef Robotics Walked Away From Restaurants

Chef Robotics had millions of dollars in contracted revenue from fast-casual restaurant chains. Rajat Bhageria still chose to walk away. The demand was real. The model was not viable. Restaurants only operate at peak production for a few hours a day, while a useful robot may need to handle as many as 90 different ingredients. The economics and technical reality did not support the company Rajat wanted to build. In this episode of Automated, Brian Heater speaks with Rajat, founder and CEO of Chef Robotics, about the difficult decision to leave restaurants behind as the company’s starting market and focus on high-mix food manufacturing instead. Rajat explains how the team assumed food factories were already automated until it visited plants where hundreds of people were still portioning meals by hand. Unlike a restaurant, these facilities can run production for as many as 16 hours a day. A robot on a high-mix assembly line may also need to handle four or five ingredients instead of every ingredient on a restaurant menu. The conversation reveals why so much fresh-food production remains manual. Low-mix products such as soup, cereal, or bagged vegetables can justify dedicated automation. Fresh meals, wraps, salads, and prepared foods require constant changeovers across hundreds or even thousands of SKUs. That flexibility still often comes from people working in refrigerated production rooms. Brian and Rajat also examine the robotics graveyard. Rajat argues that many robotics startups do not fail because their technology is poor. They fail because they choose the wrong customer, solve the wrong problem, or never make the unit economics work. That same tension now hangs over the humanoid market, where capital runway may determine which companies survive long enough to reach commercial scale. They also discuss what it takes to win a manufacturer’s trust when a startup has no track record. Chef structured early deployments around clear acceptance tests, embedded its engineers at customer sites, and spent a year sending team members into plants before sunrise to find bugs, release new software, and test it again the next morning. Finally, Rajat explains why Chef’s lack of food-industry insiders became an advantage only because customers acted as true design partners. The team learned food safety, production planning, and line operations directly in the field. It also discovered that the hardest robotics problems were often the least obvious, including clear trays on white conveyors, sloped floors, changing line speeds, and placing food without spilling it. The result is a candid conversation about product-market fit, customer pull, real-world robotics, and why shipping a useful system requires much more than building impressive technology. Connect with Rajat Bhageria https://www.linkedin.com/in/rajatbhageria Learn more about Chef Robotics https://www.chefrobotics.ai/ 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. 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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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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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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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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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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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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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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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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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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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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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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