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

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

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

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

The conversation also explores healthcare, where Matthew says AI scribes are already making a real impact, even as outdated infrastructure like fax-based record sharing shows how much friction remains.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

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

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

Connect with Matthew Johnson-Roberson

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

Learn more about Vanderbilt’s College of Connected Computing

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

Learn more about Patients.app

https://patients.app/

We’d love to hear from you.

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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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You can find the transcript and more episodes of Automated at automated.fm

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Home robots have been promised for decades.

Most of them did not fail because the ambition was too small. They failed because the technology was not yet good enough to understand people, adapt to real homes, or earn a place in daily life.

In this episode of Automated, Brian Heater speaks with Colin Angle, founder and CEO of Familiar Machines & Magic and co-founder of iRobot, about why this moment in robotics feels fundamentally different.

After helping define consumer robotics with Roomba, Colin is now focused on a new category of robot built not just to perform tasks, but to understand context, respond with intention, and build long-term connections inside the home.

The conversation explores why the hardest problem in robotics was never simply movement. For years, robots could hear commands and execute narrow tasks, but they struggled with situational awareness, context, and the complexity of real-world environments. Colin explains why recent advances in AI have changed that, making capabilities that once felt impossible now practical.

Brian and Colin also revisit one of Roomba's most important lessons. A robot can technically work and still fail in the home. The real challenge is not just functionality. It is whether the product fits naturally into people’s routines. Colin shares why one of Roomba’s biggest failure modes was not a rare edge case, but something much more common: people turning it off because it was annoying at the wrong time, and never turning it back on.

The conversation also digs into what physical presence adds to AI. Colin reflects on early iRobot experiments like My Real Baby and explains why embodied systems can create a deeper and more memorable connection than software on a screen.

They also discuss why Colin believes the next major consumer robot will not be a humanoid trying to replicate human labor in the home. Instead, he argues the real opportunity is building machines people trust, enjoy interacting with, and want around over time.

Privacy is another major part of that equation. Colin explains why home robots need to run on the edge, not rely on constant cloud streaming, and why trust, latency, and cost all matter just as much as technical capability.

This conversation is a deep look at what held home robotics back, what AI has finally unlocked, and why the next breakthrough may come from building robots that feel less like tools and more like a natural part of everyday life.

Connect with Colin Angle

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

Learn more about Familiar Machines & Magic

https://www.familiarmachines.com/

We’d love to hear from you.

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Self-driving cars were supposed to be everywhere by now.

They are not.

And the reason is not what most people think.

In this episode of Automated, Brian Heater speaks with Martial Hebert, Dean of Carnegie Mellon University’s School of Computer Science, about the reality behind decades of robotics and AI development.

Martial has spent more than 40 years at the Robotics Institute and worked on some of the earliest autonomous vehicle systems. From that perspective, the story is not about technology failing.

It is about expectations being wrong.

The core technology for self-driving cars has existed for years. What slowed everything down is something far less visible: validation, safety, and the challenge of proving these systems can operate reliably in the real world.

That gap between “it works” and “it can be trusted” is where most timelines break.

The conversation also explores why physical AI is fundamentally different from the AI most people are familiar with. Unlike software, robots have to operate in unpredictable environments, interact with people, and handle edge cases that cannot be fully simulated.

Martial explains why simulation alone is not enough, and why real-world experimentation is still essential, even when it is slow, expensive, and difficult to scale.

They also discuss the robotics data problem. While large language models benefit from massive amounts of internet data, robotics systems struggle to collect the kind of real-world data they actually need.

Brian and Martial also dig into a deeper idea that often gets overlooked: progress in robotics is not just about better algorithms. It is about building long-term ecosystems of talent, culture, and expertise.

That is part of what turned places like Carnegie Mellon into leaders in autonomy, and why many of today’s breakthroughs are the result of decades of accumulated work.

They also explore the role of DARPA and long-term research funding, not as a way to build products quickly, but as a way to push the limits of what is possible and force entirely new breakthroughs.

This conversation offers a grounded perspective on why progress in AI takes longer than expected and what it actually takes to move from impressive demos to systems that work in the real world.

Connect with Martial Hebert

https://www.linkedin.com/in/martial-hebert-76448756/

Learn more about Carnegie Mellon Robotics

https://www.ri.cmu.edu/

We’d love to hear from you.

Have thoughts or guest suggestions?

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You can find the transcript and more episodes of Automated at automated.fm.

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