Automated with Brian Heater

What if the biggest bottleneck in robotics is the way robots are taught?

Jagdeep Singh believes the industry needs a much larger and more diverse source of training data to build robots that can handle the unpredictability of real work. At Rhoda AI, that starts with internet video.

In this episode of Automated, Brian Heater speaks with Jagdeep, co-founder and CEO of Rhoda AI, about the company’s approach to physical AI, why he sees limits in vision-language-action models, and what it will take to move generalist robots from demonstrations into factories and warehouses.

Jagdeep explains how Rhoda pretrains its model on hundreds of millions of video clips to learn how the physical world moves. The goal is to build an understanding of motion, forces, and object behavior before teaching a particular job. He says that foundation has allowed Rhoda to learn specific tasks with 10–20 hours of robot data, rather than the much larger datasets associated with other approaches.

The conversation explores why footage of a specific task is not always the best starting point. Jagdeep describes how broader video datasets performed better than more narrowly curated collections of people doing jobs. Task-specific learning comes afterward, followed by additional learning from the unusual situations robots encounter with customers.

That distinction became clear when Rhoda took a container-folding task from the lab into a factory. Broken latches and rusted equipment changed the conditions the model had learned. Jagdeep says the team adapted using roughly three hours of additional factory data, illustrating why deployment requires more than repeating a successful demonstration.
Brian and Jagdeep also discuss how learning can compound across customers, why access to deployment data is central to Rhoda’s business, and the difference between customers owning their data and allowing the company to use it for model training.

The conversation then turns to hardware. Jagdeep outlines four requirements driving Rhoda’s development: payload capacity, reliability, safety and dexterity. He explains why the company is building components it could not buy, how it is weighing the tradeoffs of three-finger hands, and why its mobile manipulators use wheels.

Finally, Jagdeep explains why customers ultimately judge robots by success rate and cycle time. He shares Rhoda’s plans to deploy its model on existing hardware while developing its own platform, the role of human demonstrations in teaching new tasks, and why reliable performance in customer environments is the test that matters.

Connect with Jagdeep Singh
https://www.linkedin.com/in/jagdeep-singh-3b2a9222

Learn more about Rhoda AI
https://www.rhoda.ai/

Explore Rhoda AI’s research
https://www.rhoda.ai/research/direct-video-action

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.

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Humanoid robots are receiving more attention than almost any other form of physical AI. But Andie Zhang says factory buyers still care more about speed, precision, reliability, safety, and ROI than a human shape.

In this episode of Automated, recorded at the inaugural Davos Tech Summit, Brian Heater speaks with Andie, Global Portfolio and Technology Manager for Collaborative Robots at ABB, about why industrial robot arms are not going away, where humanoids still fall short, and how physical AI can unlock factory tasks that traditional automation cannot solve.

Andie explains why the form of a robot should follow the job it needs to perform. Humanoids are designed to do many different things, but that versatility does not automatically make them the best option for industrial manufacturing. When a specialized robot can complete the same task faster, more precisely, and at a lower cost, companies have little incentive to pay for additional joints or capabilities that do not improve the application.

The conversation also explores how physical AI can expand automation without replacing the systems already working reliably inside factories. Structured and repetitive tasks can still be handled through traditional programming. AI becomes valuable when robots must work in unstructured environments, interpret sensor information, adapt their behavior, or handle flexible objects that cannot be positioned the same way every time.

Andie describes how simulation, digital twins, synthetic images, and AI vision are helping manufacturers prepare and test automation systems before deploying them on-site. She also explains how ABB developed an AI-based system that can recognize and pick flexible clothing packages, a task that has traditionally required large numbers of workers because every package looks and behaves differently.

Brian and Andie also discuss why labor shortages are accelerating automation around the world. After visiting more than 100 customer plants, Andie says manufacturers consistently turn to robots because they cannot find enough qualified workers. She shares how cobots are helping experienced welders teach robots to perform repetitive welds, allowing companies to increase productivity without removing those workers from the process.

They also examine the safety questions surrounding humanoids, the difficulty of developing dexterous five-finger hands, and the importance of making increasingly complex robots usable for factory workers and system integrators.
Finally, Andie reflects on becoming the first person responsible for cobots at ABB, building an internal robotics incubator, taking robots directly to small businesses, and creating more opportunities for women and people with transferable skills to enter the robotics industry.

Connect with Andie Zhang
https://www.linkedin.com/in/andiezhang/

Learn more about ABB Robotics
https://www.abb.com/global/en/areas/robotics

Explore ABB collaborative robots
https://www.abb.com/global/en/areas/robotics/products/robots/collaborative-robots

Learn more about RobotStudio HyperReality
https://www.abb.com/global/en/areas/robotics/innovation/robotstudio-hyperreality
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.

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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.
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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.
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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.
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