Rick Faulk on Locus Robotics, Warehouse Automation, and Physical AI

Rick Faulk on Locus Robotics, Warehouse Automation, and Physical AI

Warehouse automation is not about building the flashiest robot. It is about solving the right problem at scale. In this episode of Automated, Brian Heater speaks with Rick Faulk, CEO of Locus Robotics, about what it really takes to deploy robots inside working warehouses and why the future of physical AI may look very different from the humanoid hype cycle. Rick explains how Locus grew out of a major logistics problem. Quiet Logistics had been using Kiva robots before Amazon acquired Kiva and took the product off the market. Instead of returning to a manual operation, the team started building its own robotics solution inside the warehouse. That origin story shaped the company’s entire approach. Rick says many robotics companies fail because they start with the robot instead of the customer’s problem. Locus was different because it was built inside the environment it was trying to automate. Brian and Rick also discuss why fixed automation can be limiting in warehouses with seasonal peaks, shifting demand, labor shortages, and changing order volume. Rick explains why flexible systems, Robots-as-a-Service, and scalable deployments matter when operators need to handle holiday surges, back-to-school volume, and unpredictable demand. The conversation digs into one of the biggest topics in robotics right now: humanoids. Rick says humanoids may eventually play a role, but purpose-built warehouse robots have a clearer path to ROI today. In his view, the winning systems are not trying to fold laundry, make burgers, and work in a warehouse. They are designed to do one important job extremely well. They also get into Locus’s real-world data advantage. Rick says Locus has completed more than seven billion picks and is now doing around 150 picks per second. Every pick becomes part of a data flywheel that helps robots move more safely, respond to warehouse conditions, and improve productivity. Rick also breaks down Locus Array, the company’s autonomous Robots-to-Goods system. He explains why mobile manipulation is so difficult, why picking in a warehouse is much harder than it looks, and why Array is designed as a practical physical AI system for fulfillment. Finally, Brian and Rick discuss what automation means for warehouse workers, why robotics can create higher-value roles inside facilities, and how companies can compete in a logistics world shaped by Amazon-level expectations. Connect with Rick Faulk https://www.linkedin.com/in/rickfaulk [https://www.linkedin.com/in/rickfaulk?utm_source=chatgpt.com] Learn more about Locus Robotics https://locusrobotics.com/ [https://locusrobotics.com/?utm_source=chatgpt.com] Learn more about Locus Array https://locusrobotics.com/blog/locus-array-autonomous-warehouse-era [https://locusrobotics.com/blog/locus-array-autonomous-warehouse-era?utm_source=chatgpt.com] We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org [podcast@automate.org] You can find the transcript and more episodes of Automated at automated.fm [http://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://www.youtube.com/@automatedpodcast] https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 [https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221] https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 [https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6] You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ [https://www.linkedin.com/showcase/automated-podcast-by-a3/] Instagram https://www.instagram.com/automatedpod/ [https://www.instagram.com/automatedpod/] ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.

Aya Durbin on Turning Atlas Into a Real Industrial Robot

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 [https://www.linkedin.com/in/alexa-durbin] Learn more about Boston Dynamics Atlas https://bostondynamics.com/products/atlas/ [https://bostondynamics.com/products/atlas/] Thanks for being an Automated fan! Enter our giveaway to win robot-building sets from some of our favorite robotics companies and exclusive Automated swag. We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org [podcast@automate.org] You can find the transcript and more episodes of Automated at automated.fm [http://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://www.youtube.com/@automatedpodcast] https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 [https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221] https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 [https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6] You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ [https://www.linkedin.com/showcase/automated-podcast-by-a3/] Instagram https://www.instagram.com/automatedpod/ [https://www.instagram.com/automatedpod/] ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.

Andrew Barry on Why Dexterity Is the Next Breakthrough in Physical AI

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 [https://www.linkedin.com/in/andy-barry?utm_source=chatgpt.com] Learn more about Generalist https://generalistai.com/ [https://generalistai.com/?utm_source=chatgpt.com] We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org [podcast@automate.org] You can find the transcript and more episodes of Automated at automated.fm [http://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://www.youtube.com/@automatedpodcast] https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221 [https://podcasts.apple.com/us/podcast/automated-with-brian-heater/id1837762221] https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6 [https://open.spotify.com/show/60olq6brlBEIJWggx2fMR6] You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ [https://www.linkedin.com/showcase/automated-podcast-by-a3/] Instagram https://www.instagram.com/automatedpod/ [https://www.instagram.com/automatedpod/] Subscribe to the Automated Newsletter: https://www.automate.org/automation/automated-newsletter [https://www.automate.org/automation/automated-newsletter] ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.

Daniel Rausch on How Alexa Was Rebuilt for the AI Era

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 [https://www.linkedin.com/in/danielrausch?utm_source=chatgpt.com] Learn more about Alexa+ https://www.amazon.com/alexaplus/dp/B0CXRRF584 [https://www.amazon.com/alexaplus/dp/B0CXRRF584?utm_source=chatgpt.com] Learn more about Amazon Echo devices https://www.amazon.com/b?ie=UTF8&node=210779651011 [https://www.amazon.com/b?ie=UTF8&node=210779651011&utm_source=chatgpt.com] We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org [podcast@automate.org] You can find the transcript and more episodes of Automated at automated.fm [automated.fm] Unlock full access to Automated and explore everything automation. Subscribe today and leave a review on YouTube, Apple Podcasts, and Spotify. You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ [https://www.linkedin.com/showcase/automated-podcast-by-a3/?utm_source=chatgpt.com] Instagram https://www.instagram.com/automatedpod/ [https://www.instagram.com/automatedpod/] ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.

Daniela Rus on Humanoid Robots, Physical AI, and the Future of Robotics

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 [https://www.csail.mit.edu/person/daniela-rus?utm_source=chatgpt.com] Learn more about MIT CSAIL https://www.csail.mit.edu/ [https://www.csail.mit.edu/] Learn more about Liquid AI https://www.liquid.ai/team/daniela-l-rus [https://www.liquid.ai/team/daniela-l-rus?utm_source=chatgpt.com] Learn more about Project CETI https://www.projectceti.org/ [https://www.projectceti.org/?utm_source=chatgpt.com] We’d love to hear from you. Have thoughts or guest suggestions? Reach us at podcast@automate.org [podcast@automate.org]. You can find the transcript and more episodes of Automated at automated.fm [http://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: https://www.automate.org/automation/automated-newsletter [https://www.automate.org/automation/automated-newsletter] You can also find us on: LinkedIn https://www.linkedin.com/showcase/automated-podcast-by-a3/ [https://www.linkedin.com/showcase/automated-podcast-by-a3/?utm_source=chatgpt.com] Instagram https://www.instagram.com/automatedpod/ [https://www.instagram.com/automatedpod/] ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.