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/

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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/

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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/

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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?

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Humanoid robots are everywhere in the headlines.

But Aya Durbin says the real test is not whether a robot can impress people in a demo. It is whether that robot can deliver real value, positive ROI, and reliable performance inside industrial environments.

In this episode of Automated, Brian Heater speaks with Aya Durbin, Director of Product for Atlas at Boston Dynamics, about what it will actually take to bring humanoid robots out of the lab and into the workforce.

Aya explains why she considers herself both a dreamer and a pragmatist. Boston Dynamics has shown what is possible with legged robots, viral demos, and advanced mobility, but productizing Atlas means focusing on customer value, uptime, deployment, serviceability, and hard industrial work.

The conversation explores why Atlas has legs, what Boston Dynamics learned from Spot and Stretch, and why the first meaningful humanoid deployments will likely happen in structured industrial environments before anything broader.

Brian and Aya also dig into the reality behind Boston Dynamics’ famous robot videos. The backflips, gymnastics, and playful demos may look like fun, but Aya explains how many of those moments are tied to the same core technology used to train robots for real tasks.

They also discuss why Atlas is being built around AI-based tools rather than hard-coded applications, how early customers will help shape the roadmap, and why integration, IT, security, downtime, and ROI are just as important as the robot itself.

Finally, Aya outlines Boston Dynamics’ current timeline for Atlas, including customer pilots planned for 2028 and Hyundai’s commitment to building 30,000 Atlas robots a year starting in 2030.

This is a grounded look at what humanoid robotics looks like beyond the hype, and what has to happen before Atlas becomes a trusted member of the industrial workforce.

Connect with Aya Durbin

https://www.linkedin.com/in/alexa-durbin

Learn more about Boston Dynamics Atlas

https://bostondynamics.com/products/atlas/

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Physical AI is moving quickly.

But Andrew Barry says one of the biggest unlocks in robotics is not just getting robots to move through the world. It is getting them to touch, grasp, adjust, and manipulate the world with real dexterity.

In this episode of Automated, Brian Heater speaks with Andrew Barry, co-founder and CTO of Generalist, about how the company is building general intelligence for the physical world and why dexterous robots may be the starting point for far more capable automation.

Andrew explains why Generalist is focused on the tasks that are both difficult and valuable. Robots have made major progress in mobility, but their ability to manipulate objects is still limited. If robots can solve dexterity, they can become useful in a much wider range of real-world environments.

The conversation explores how Generalist is collecting massive amounts of real-world manipulation data. Andrew describes the handheld data capture devices the company built, why they chose that approach over teleoperation, and how thousands of devices have helped them scale a much richer data set for robot learning.

Brian and Andrew also discuss the commercial side of physical AI. Andrew explains why the company is not just chasing impressive demos, but benchmarking against real tasks people are already paying for today. That distinction matters because a viral robot demo is not the same thing as a deployable robotic system.

They also dig into one of the most surprising parts of modern robot learning: improvisation. Andrew shares the moment when a robot picked up a baggie with the opposite hand from the one it had been trained on, completed the task anyway, and left the team realizing something very different was happening inside the model.

The episode also covers Generalist’s GEN-1 model, the parallels between robotics and the early GPT era, why flexible objects like cables are so difficult to automate, what data flywheels may actually look like in robotics, and why robots sometimes learn human mistakes from the data they are trained on.

Finally, Andrew reflects on his path from Boston Dynamics to the Broad Institute and then to Generalist, explaining how work in molecular biology, machine learning, transformers, and robotics all shaped the way he thinks about building intelligence for the physical world.

Connect with Andrew Barry

https://www.linkedin.com/in/andy-barry

Learn more about Generalist

https://generalistai.com/

We’d love to hear from you.

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Alexa is entering a very different era.

For years, voice assistants were built around rules, scripted responses, and carefully designed commands. But with the rise of large language models and generative AI, Amazon had to rethink what Alexa could be and how people might use it.

In this episode of Automated, Brian Heater speaks with Daniel Rausch, Amazon’s Vice President of Alexa and Echo, about Alexa+, the company’s AI-powered evolution of its voice assistant. Daniel explains why the shift from traditional voice assistance to foundational AI assistance required a full rearchitecture of the technology behind Alexa.

The conversation explores how Alexa moved from a deterministic system to one powered by more than 70 models, why customers do not care which model is working behind the scenes, and how Amazon thinks about choosing the right AI tool for the job.

Brian and Daniel also discuss one of the biggest questions around AI assistants: trust. Daniel explains why Alexa is designed to understand that it is AI, why it should help people prioritize human relationships, and why guardrails matter as assistants become more conversational, personal, and ambient in the home.

They also get into the smart home, where Daniel says Alexa+ is changing how people interact with connected devices. Instead of needing to know the right command or app, people can speak naturally, whether they are unlocking a door, checking a Ring camera, controlling lights, or asking for help while cooking.

The conversation also covers Echo hardware, privacy controls, personality styles, language and dialect differences, AI’s impact on robotics, and why Daniel sees Amazon as an invention machine at a moment when AI is moving faster than ever.

Connect with Daniel Rausch

https://www.linkedin.com/in/danielrausch

Learn more about Alexa+

https://www.amazon.com/alexaplus/dp/B0CXRRF584

Learn more about Amazon Echo devices

https://www.amazon.com/b?ie=UTF8&node=210779651011

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Physical AI is moving fast.

But Daniela Rus says the future of robotics will not be defined by viral humanoid robot demos alone. The real challenge is building robots that can understand the physical world, make safe decisions in real time, and work reliably outside controlled lab environments.

In this episode of Automated, Brian Heater speaks with Daniela Rus, Director of MIT CSAIL, about humanoid robots, self-driving cars, embodied AI, on-device AI, robot learning, and why the next wave of artificial intelligence needs to move beyond the cloud and into the physical world.

Daniela explains why humanoid robots are exciting, but not ready for prime time. A robot may look impressive in a short demo, but operating safely and consistently around people requires common sense, physical understanding, and real-world adaptability that robots still do not fully have.

The conversation also explores why self-driving cars remain one of the hardest problems in robotics. Daniela breaks down the long tail of autonomous driving, from bad weather and unpredictable human behavior to the messy edge cases that make real-world deployment so difficult.

Brian and Daniela also discuss why the future of AI robotics may depend on smaller, more efficient AI models that can run directly on devices. If a car is moving at 60 miles an hour, it cannot wait for the cloud to decide what to do next. For robotics, speed, safety, energy use, and reliability all point toward a hybrid future where AI runs both in the cloud and on the machine itself.

Daniela also shares why physical AI needs more than video data. Robots interact with the world through forces, torques, motion, contact, and uncertainty. For many tasks, robot learning requires a deeper understanding of physics, not just visual imitation.

The episode also moves into some of the most fascinating frontiers of AI and robotics, including Daniela’s work with Project CETI and the effort to better understand sperm whale communication using machine learning, robotics, and large-scale data collection.

Finally, Daniela talks about AI systems that could help design robots from natural language prompts, why engineering constraints can drive creativity, what octopus intelligence can teach us about decentralized robots, and why this moment in robotics feels like the future researchers imagined decades ago is finally arriving.

Connect with Daniela Rus

https://www.csail.mit.edu/person/daniela-rus

Learn more about MIT CSAIL

https://www.csail.mit.edu/

Learn more about Liquid AI

https://www.liquid.ai/team/daniela-l-rus

Learn more about Project CETI

https://www.projectceti.org/

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Physical AI is moving fast.

But Matthew Johnson-Roberson says robotics is still missing something fundamental. The field has data, models, and momentum, but it still does not have the simple learning objective that helped language models scale so quickly.

In this episode of Automated, Brian Heater speaks with Matthew Johnson-Roberson, founding dean of Vanderbilt’s College of Connected Computing, about why physical AI may not follow the same playbook as large language models.

Matthew explains why robotics still feels stuck between promise and deployment. We still do not live in a world where you can look out your window and see robots everywhere. That gap is not just about hype. It is about the difficulty of building systems that can learn from physical experience in a way that actually scales.

Brian and Matthew also discuss what self-driving taught the broader automation world, why last-mile delivery still has not cracked scale, and what Amazon’s long arc with Kiva robots reveals about how real hardware progress actually happens.

The conversation also explores healthcare, where Matthew says AI scribes are already making a real impact, even as outdated infrastructure like fax-based record sharing shows how much friction remains. That experience also helped inspire Patients.app, the startup he co-founded after watching how much clinician time gets lost to documentation.

They also get into the tension between startups and academia. Matthew argues that startups are powerful vehicles for scaling known solutions, but much worse fits for decade-long research questions that still do not have clear answers.

Finally, Matthew reflects on building Vanderbilt’s new College of Connected Computing, why higher ed can take on 30- and 40-year problems in a way few other institutions can, and how AI agents have changed his own workflow so dramatically that he says he has not directly written a line of code in three months.

Connect with Matthew Johnson-Roberson

https://www.linkedin.com/in/mattkjr

Learn more about Vanderbilt’s College of Connected Computing

https://computing.vanderbilt.edu/bio/matthew-johnson-roberson/

Learn more about Patients.app

https://patients.app/

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Physical AI looks closer than ever.

But the hardest part in robotics is not getting a machine to do one impressive task on camera. It is building systems that can improve from real-world experience, handle edge cases, and scale across different robots and environments.

In this episode of Automated, Brian Heater speaks with Sergey Levine of Physical Intelligence about why robotics has reached an inflection point, and why progress now requires more than great models in a lab.

Sergey explains why the next phase of robotics will depend on something much less flashy than a viral demo: collecting the right real-world data, learning from it efficiently, and building systems that improve through deployment.

The conversation explores what makes a robot experience useful in the first place. Sergey describes a concept borrowed from child psychology called the “zone of proximal development,” where the best learning happens when a system is challenged just beyond what it can already do. For robots, that means creating environments where they can succeed, fail, adapt, and improve.

Brian and Sergey also discuss how the bottleneck in robotics is changing. Basic motor skills are improving fast. The harder problem now is judgment. A robot may be able to clean dishes, but if it drops a clean plate on the floor, it still has to understand that the plate needs to be washed again. That kind of common sense remains one of the biggest unsolved challenges in physical AI.

They also dig into one of the biggest debates in robotics right now: data. Sergey argues that real-world data collection is not the impossible obstacle many researchers once assumed. In fact, he believes the long-term path to better robots is more practical than people think. Deploy systems, collect experience, improve the model, and repeat.

The conversation also covers why Physical Intelligence is focused on a general intelligence layer rather than a single-narrow product, why robots should not just be treated as metal versions of people, and what surprised Sergey most about controlling very different robot platforms with the same model.

Finally, Sergey reflects on why Physical Intelligence is structured more like a lab than a traditional startup, why experimentation matters so much in modern AI, and how we may one day look back on this era as the moment AI moved beyond internet data and into the physical world.

Connect with Sergey Levine

https://www.linkedin.com/in/sergey-levine-5a31a24

Learn more about Physical Intelligence

https://www.physicalintelligence.company/

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