Avsnitt
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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?
Reach us at [email protected]
You can find the transcript and more episodes of Automated at automated.fm
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https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221
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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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We’d love to hear from you. Have thoughts or guest suggestions?
Reach us at [email protected]
You can find the transcript and more episodes of Automated at automated.fm
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Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify
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https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221
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Saknas det avsnitt?
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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.
Have thoughts or guest suggestions?
Reach us at [email protected]
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
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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
We’d love to hear from you.
Have thoughts or guest suggestions?
Reach us at [email protected]
You can find the transcript and more episodes of Automated at automated.fm
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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/
We’d love to hear from you.
Have thoughts or guest suggestions?
Reach us at [email protected].
You can find the transcript and more episodes of Automated at automated.fm.
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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.
Have thoughts or guest suggestions?
Reach us at [email protected].
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.
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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/
We’d love to hear from you.
Have thoughts or guest suggestions?
Reach us at [email protected].
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.
Have thoughts or guest suggestions?
Reach us at [email protected].
You can find the transcript and more episodes of Automated at automated.fm.
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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?
Reach us at [email protected].
You can find the transcript and more episodes of Automated at automated.fm.
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Humanoid robots are everywhere right now. From viral demos to bold promises about home automation, it often feels like the future has already arrived.
But behind the scenes, the reality is far more complex.
In this episode of Automated, Brian Heater speaks with Bren Pierce, founder of Kinisi Robotics and co-founder of Bear Robotics, about what it actually takes to build and deploy robots in the real world.
Bren explains why many humanoid robot demonstrations are misleading. While the technology has made major advances in movement and control, real-world deployment is still limited by manipulation, reliability, and the complexity of unstructured environments.
The conversation explores why household robotics may be further away than most people think. Despite impressive demos, creating a robot that can operate independently in a dynamic home environment remains an unsolved challenge that could take years to fully unlock.
They also discuss the gap between robotics innovation and practical business applications. Many companies are still experimenting, often driven by internal pressure to adopt AI and automation, even when the return on investment is unclear.
Bren shares lessons from building multiple robotics companies, including why focusing on real problems matters more than chasing hype. Instead of targeting futuristic home use cases, Kinisi is focused on warehouse and industrial environments where the technology can deliver value today.
The episode also dives into the challenges of scaling robotics systems. From deployment complexity to training and usability, the biggest barrier is not just building the technology, but making it reliable and usable without requiring expert engineers.
Brian and Bren also explore the parallels between robotics and autonomous vehicles, highlighting how long it can take for breakthrough technologies to transition from demos to real-world impact.
This conversation offers a grounded perspective on where robotics actually stands today and what it will take to move from impressive demos to real deployment.
Connect with Bren Pierce
https://www.linkedin.com/in/brenpierce/
Learn more about Kinisi Robotics
https://www.kinisirobotics.com/
We’d love to hear from you. Have thoughts or guest suggestions? Reach us at [email protected].
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.
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Last-mile delivery is one of the most expensive and inefficient parts of the global supply chain. While goods can travel across oceans for just a few dollars, getting them from a local hub to a customer’s door remains disproportionately costly.
In this episode of Automated, Brian Heater speaks with Ali Kashani, CEO of Serve Robotics, about the realities of deploying delivery robots in the real world and what it takes to scale autonomous systems beyond early pilots.
Ali explains how Serve Robotics evolved from an internal Postmates project into an independent company operating thousands of robots in live environments. This transition reflects a broader shift in robotics from controlled experimentation to real-world deployment at scale.
The conversation explores why building in the real world is essential for robotics. Lab environments often miss critical edge cases, while public deployment reveals the unpredictable human behavior, operational challenges, and environmental complexity that define real performance.
They also discuss the economic implications of reducing last-mile delivery costs. Lowering delivery from $10 to closer to $1 could unlock new demand, expand local economies, and create new categories of jobs that support and operate these systems.
The episode also examines safety, public perception, and the long-term impact of autonomous delivery on cities. From reducing reliance on cars to improving walkability and safety, these systems may reshape how urban environments function.
Brian and Ali also explore scaling challenges, lessons from acquisitions, and the operational realities of running thousands of robots in public. From unexpected real-world incidents to long-term infrastructure shifts, this conversation offers a grounded look at what it takes to bring robotics into everyday life.
We’d love to hear from you. Have thoughts or guest suggestions? Reach us at [email protected].
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.
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Robotics is advancing quickly, but building systems that can operate reliably in the real world remains one of the most complex challenges in technology.
In this episode of Automated, Brian Heater speaks with Zachary Jackowski of Boston Dynamics about the shift from research to commercialization and why generalization is emerging as the defining problem in modern robotics.
Zachary explains how Boston Dynamics approaches robot design, from early research platforms like Atlas R1 to more refined production systems. Early versions prioritize exploration and performance, while newer iterations focus on reliability, repairability, and deployment in real environments. This evolution reflects a broader shift across the industry toward building systems that can move beyond controlled demos and operate consistently in the field.
The conversation explores why generalization is critical for robotics. Training robots on a single task does not prepare them for real-world variability. Instead, diverse data, multiple environments, and exposure to different behaviors are required to build systems that can adapt and perform across use cases.
They also discuss the challenge of data collection and deployment, including the chicken-and-egg problem of needing real-world data to improve systems that are not yet ready for large-scale deployment. Incremental rollout, focused applications, and controlled environments are key steps in bridging that gap.
The episode also examines why industrial environments are the starting point for humanoid robots. Factories provide structure, repeatability, and trained operators, while home environments introduce unpredictability that current systems are not yet equipped to handle at scale.
Brian and Zachary also explore how different robot platforms, including humanoids, quadrupeds, and wheeled systems, each serve distinct roles. Rather than a single dominant design, the future of robotics will likely involve multiple systems working together and benefiting from shared data and learning.
From actuator design and system simplification to deployment strategy and data diversity, this conversation offers a grounded look at what it takes to bring robotics into real-world applications.
Key Moments:
(00:00) Boston Dynamics and the shift to commercialization
(02:11) Zachary’s path into robotics and Boston Dynamics
(04:16) From research to product development
(07:19) Research versus commercialization in robotics
(08:53) Why early robots are built differently
(11:16) Designing better systems through iteration
(13:22) Advances in actuator performance
(14:36) Safety and robot design decisions
(16:11) Why humanoid robots are just the starting point
(17:21) Why generalization is the real breakthrough
(20:10) The data collection challenge in robotics
(21:31) Why data diversity matters more than volume
(23:24) Why robots are going to factories first
25:52 Why robots are not ready for homes
31:34 Why complexity increases in real-world robotics
Sponsors: maxon designs and manufactures precision drive systems that enable reliable, high‑duty‑cycle performance in industrial automation, robotics, and smart manufacturing. https://www.maxongroup.com/
We’d love to hear from you. Have thoughts or guest suggestions? Reach us at [email protected].
You can find the transcript and more episodes of Automated at automated.fm.
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Robotics is advancing quickly, but real-world deployment is still far more difficult than most people expect.
In this episode of Automated, Brian Heater speaks with Ranjay Krishna, a professor at the University of Washington and former researcher at Ai2, about the fundamental challenges preventing robots from working reliably outside controlled environments, and why solving the data problem is key to unlocking the next wave of robotics.
Much of the work discussed in this episode was developed during his time at Ai2.
Ranjay explains why today’s robots struggle with tasks that humans find intuitive, from learning by observation to understanding perspective and adapting to new environments. While AI models have made massive progress in language and vision, robotics introduces a new layer of complexity where actions change the world in real time and small errors compound over time.
The conversation explores the limitations of current approaches, including why training robots in simulation often fails to translate to the real world, and how the lack of diverse environments creates major gaps in performance. Ranjay shares how his team at the Allen Institute is addressing this by building large-scale simulated environments designed to better reflect the variability of real-world spaces.
They also discuss the concept of an ImageNet moment for robotics, and what it would take to create the kind of large, diverse datasets that transformed AI. By generating hundreds of thousands of simulated environments and scaling data collection, his team is exploring whether robots can learn more effectively in simulation and generalize those skills into the physical world.
The conversation also covers why robotics requires more than just better models, including challenges in hardware, sensing, and real-world interaction. From embodiment and perception to reasoning and adaptation, it is a grounded look at why robotics remains one of the hardest problems in AI and what needs to happen next for the industry to move forward.
We’d love to hear from you. Have thoughts or guest suggestions? Reach us at [email protected].
You can find the transcript and more episodes of Automated at automated.fm.
Also, join us at MassRobotics for a happy hour with Brian Heater from A3. Wednesday, April 8 - 4:30 PM - 6:00 PM EDT
Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify.
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Billions are flowing into humanoid robots. But on the factory floor, nobody cares what the robot looks like.
In this episode of Automated, Brian Heater speaks with Erik Nieves, CEO and co-founder of Plus One Robotics, about the gap between robotics investment and real-world deployment. Recorded live at the A3 Business Forum in Miami, Nieves explains why enterprise customers have one question and one question only: does it hit 2,200 picks per hour with three nines of uptime?
From the Cambrian explosion happening across warehouse automation to why dexterity remains the biggest unsolved problem in robotics, Nieves gives one of the most grounded, honest assessments of where the industry actually stands. He also explains why human-in-the-loop systems are not a limitation but a competitive advantage, and why robots are about to end the era of labor arbitrage entirely.
Key Moments:
(00:00) Why Enterprise Customers Don't Care What the Robot Looks Like
(00:38) From Astronomy to Robotics: Erik Nieves' Origin Story
(04:44) CES Humanoid Demos vs Real-World Deployment
(06:52) Has the Capital Outpaced the Technology
(09:32) Plus One Robotics at Year Ten
(11:15) What Has Changed and What Has Stayed the Same
(12:25) KPIs Matter More Than Form Factor
(13:28) Why Humanoids Cannot Meet Industrial Rates Yet
(15:59) Dexterity Is the Real Problem Nobody Is Solving
(19:33) Legs vs Wheels: The Debate That Won't Die
(21:43) Why Robots Are Still Behind a Fence
(23:06) The Fence Is Not There to Keep the Robot In
(24:59) The Cambrian Explosion in Robotics
(26:57) How Plus One Robotics Was Founded
(29:42) Why Human in the Loop Was the Right Bet
(34:27) Robots Will End the Era of Labor Arbitrage
(36:01) Nearshoring vs Reshoring
(37:53) Why San Antonio Is a Hidden Advantage
(39:08) Talent, Universities, and the AI Pipeline
(41:04) Why Hardware Companies Cannot Go Fully Remote
(42:17) Mentorship Only Works in Person
(46:34) The One KPI That Runs the Entire Company
Sponsored by SANYO DENKI America: SANMOTION delivers precise, reliable multi-axis control for advanced robotics systems.
Learn more at https://www.sanyodenki.com/america/
We'd love to hear from you! Have thoughts or guest suggestions? Reach us at [email protected]
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.
Subscribe to the Automated Newsletter:
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Robot vacuums have been on the market for over 20 years and are still in fewer than 20 percent of US homes. In this episode of Automated, Brian Heater speaks with Gary Cohen, CEO of iRobot and the brand behind Roomba, about what it actually takes to rebuild one of the most iconic names in consumer robotics.
Gary breaks down the shift from feature-led to consumer-led product development, explaining why iRobot missed key market opportunities and how competitors used that window to take significant market share. He shares the full story behind the failed Amazon acquisition, the Chapter 11 restructuring, and how he rebuilt the company's entire product line in under 12 months to win Prime Day 2025.
They also discuss why the robot vacuum market is far from saturated, why simplifying the setup and onboarding experience matters more than any new feature, and what the Gillette razor wars teach us about the robot vacuum arms race happening right now. Gary also addresses data privacy under the new ownership structure and previews what iRobot's roadmap looks like over the next two to three years, including a Japan-first product launch and the long-awaited iRobot lawnmower.
We'd love to hear from you! Have thoughts or guest suggestions? Reach us at [email protected]
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.
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Home robotics has been promised for decades, but most products still struggle to meet everyday expectations. In this episode of Automated, Brian Heater speaks with Mehul Nariyawala of Matic Robots about why the robot vacuum category became the beachhead for home robots, and what it actually takes to ship a product people trust.
Mehul breaks down the shift from “default trust” to “default skepticism” in consumer hardware, and why robotics lives in the “march of nines,” where demos look impressive at 90%, but real products require relentless work to reach the reliability customers demand. He explains why people will collaborate with AI software, but they want robots to delegate tasks completely, which raises the bar dramatically for home robotics.
They also talk through what makes Matic’s approach different, including why the company believes vision-only autonomy is the only economically viable path for indoor robots at scale, and how mapping, localization, and on-device intelligence lay the foundation for future home capabilities beyond vacuuming. The conversation closes with Mehul’s view of an “iPhone moment” in home robotics, and how Matic plans to keep improving through software updates while building toward what comes next.
We’d love to hear from you! Have thoughts or guest suggestions? Reach us at [email protected].
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.
Subscribe to the Automated Newsletter:
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“We wanted to ship before we talked.”
That’s how Rob Cochran, co-founder of Fauna Robotics, explains the company’s decision to stay in stealth until its humanoid robot was already in customers’ hands. In this episode of Automated, Brian Heater speaks with Cochran about launching a humanoid startup in one of the most competitive and uncertain moments in robotics.
Instead of targeting factories or chasing headline-grabbing demonstrations, Fauna built Sprout, a lightweight, three-and-a-half-foot-tall humanoid designed for developers and real-world experimentation. The robot is soft to the touch, expressive, and modular by design, supporting teleoperation, mapping and navigation, voice interaction, and AI model development out of the box. The goal is not to claim that humanoids are solved, but to create a platform where researchers, startups, and enterprises can begin solving them.
They discuss why shipping matters more than announcements, the realities of pricing and scaling hardware, how developer ecosystems accelerate the adoption of emerging technologies, and why modular AI stacks may be more practical than a single end-to-end model. The conversation also covers data ownership, teleoperation versus autonomy, early commercial deployments, and the long-term vision for consumer and home robotics. It is a pragmatic look at what it takes to move humanoids from concept videos to working systems in the world.
Sponsored by SANYO DENKI America: SANMOTION delivers precise, reliable multi-axis control for advanced robotics systems.
Learn more at https://www.sanyodenki.com/america/.
We’d love to hear from you! Have thoughts or guest suggestions? Reach us at [email protected].
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.
Subscribe to the Automated Newsletter:
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“Nobody wants a robot.” That’s how Péter Fankhauser, CEO of ANYbotics, reframes industrial automation. Customers are not buying quadrupeds for spectacle. They are investing in solutions that solve real operational problems. In this episode of Automated, Brian Heater speaks with the ETH Zurich spinout founder about turning cutting-edge robotics research into a commercially deployed inspection platform used in offshore wind, oil and gas, and other hazardous environments.
They discuss why ANYbotics is not chasing humanoid hype, how the company built traction through real-world deployments, what industrial facilities actually look like behind the scenes, and how reinforcement learning reshaped their control systems. The conversation also covers transparency in robotics marketing, the role of teleoperation in autonomy, the shift from collecting data to delivering insights, and the ethical line the company drew around weaponization. It is a grounded look at where industrial AI is delivering value today and what it takes to scale autonomous robots in the real world.
Sponsored by SANYO DENKI America: High-performance SANMOTION C S300 delivers precise, reliable multi-axis control. Learn more at sanyodenki.com/america.
We’d love to hear from you! Have thoughts or guest suggestions? Reach us at [email protected].
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.
Subscribe to the Automated Newsletter:
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Talk about humanoid robots is everywhere, but how useful are they in industrial settings?
In this live episode of Automated, Brian Heater talks with Mikell Taylor, former Amazon Robotics leader and current head of General Motors’ Autonomous Robotics Center. Recorded in front of an audience at A3’s Business Forum, the conversation dives into safety, collaboration, automation at scale, and why the best robots don’t necessarily look like us.
From Amazon’s Proteus AMR to GM’s next generation of manufacturing automation, this episode cuts through the hype and focuses on what actually works in the real world.
We’d love to hear from you! Have thoughts or guest suggestions? Reach us at [email protected].
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.
Subscribe to the Automated Newsletter:
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Brian Gerkey believes deeply in the importance of open-sourcing robotics technology. His career, with time spent at Willow Garage, Open Robotics, and now as CTO at Intrinsic, has been guided by this philosophy.
In this episode of Automated, Gerkey explains why “simple” tasks like picking and placing remain some of the toughest problems to solve, especially in high-variability environments. We explore Intrinsic’s software-first approach to making automation economically viable, the idea of artificial functional intelligence (AFI), and how technology only succeeds when workers trust and understand how to use it. Bryan reflects on the tight-knit spirit of the industry, and why community, relationships, and impact, not just perfect tech, drive it forward and keep him dedicated to robotics after all of these years.
We’d love to hear from you! Have thoughts or guest suggestions? Reach us at [email protected].
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.
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- Visa fler
