Audrow Nash Podcast

Audrow Nash

If you're building a robotics company, backing one, or deciding whether to join one, this is a way to hear how they actually work from the people running them. Founders, CTOs, investors, and researchers on how the robots work, how the business makes money, and what they've gotten wrong. Unscripted, usually over an hour, with chapters so you can skip to the part you care about. Hosted by Audrow Nash, a robotics engineer who's been interviewing roboticists since 2014. Questions and guest suggestions: audrow.com/contact

  1. Oct 13

    Why Defense Gets Humanoids Before Your Home Does: Skylark Labs' Amarjot Singh

    Episode 27 of the Audrow Nash Podcast. A four-and-a-half-foot humanoid kicks in a door so a person doesn't have to go in first. Amarjot's case is that work like this is where humanoids pay off now: the tasks vary less than in a home, and working 70 or 80% of the time is still worth it when a person isn't the one getting shot. I talk with Amarjot Singh, founder and CEO of Skylark Labs, about a model that flags, labels, and learns new things on the device after it's deployed, how they teach a humanoid a skill from a few demonstrations, and why he thinks humanoid hardware becomes a commodity while the value moves to the brain. You'll like this interview if you work on robot learning or perception at the edge, you follow humanoids, or you're curious how robots get sold into defense. The model architecture stretch (8:51–34:41) is the most technical. EPISODE LINKS - Skylark Labs website: https://www.skylarklabs.ai/ PODCAST LINKS - Podcast Website: https://audrownashpodcast.com/ - YouTube: https://www.youtube.com/@audrow - Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786 - Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162 TIMECODES 0:00 Cold open 0:33 Amarjot Singh and Skylark Labs 3:30 Humanoids that sweep for bombs and breach doors 7:23 A brain-inspired model with memory 8:51 Wavelets and Gabor filters as the first layer 13:40 Unsupervised features, supervised on top 17:26 1,000x less data and flagging outliers 21:58 Detecting a camouflaged tank 25:53 Explainability for kinetic systems 28:12 Self-labeling and on-device memory 32:06 From perception to motion 34:41 The robotics stack and sensor language model 36:45 Skills stored as graphs 39:10 Mapping human motion onto a humanoid 40:33 Adapting to a new door handle 43:18 Teaching with Apple Vision Pro and egocentric video 46:58 Simulation and sim-to-real 50:14 What's easy, hard, and not yet possible 52:44 When 70% reliable is enough 55:01 The humanoid Turing test 56:51 Generalization is the bottleneck 1:00:05 From runway debris to robot dogs 1:03:46 Why a humanoid, on two legs or four 1:07:00 Humanoid hardware as a commodity 1:10:56 Why defense comes before homes 1:14:32 Trying markets until someone paid 1:18:40 Building their own humanoid 1:19:48 Manufacturing humanoids in the US 1:22:20 India for data, testing, and talent 1:25:40 Skylark's next three to five years 1:28:21 Advice for robotics founders 1:30:02 Where robotics is going 1:31:57 Final takeaway

    Why Defense Gets Humanoids Before Your Home Does: Skylark Labs' Amarjot Singh

  2. Oct 6

    Robots That Learn Surgery From Surgeons: Inner Logic's Axel Krieger

    Episode 26 of the Audrow Nash Podcast. Johns Hopkins surgeons showed Axel's team the clip-and-cut phase of gallbladder surgery. The team recorded demonstrations, trained a robot on them, and it did that phase eight times in a row with no human help. The organs were from a butcher. Now they're testing it in live animals. I talk with Axel Krieger, Chief Robotics Officer at Inner Logic and associate professor at Johns Hopkins, about why his lab moved from hand-built models to imitation learning from surgeons, how they break a procedure into small steps a robot can learn, and how simulated patients built from tens of thousands of CT scans help test surgical devices. You'll like this interview if you're a roboticist curious about surgery, you work on imitation learning or VLAs, or you want a grounded take on when autonomy reaches the operating room. The high- and low-level policy stretch (23:46–42:28) is the most technical. EPISODE LINKS - Axel's LinkedIn: https://www.linkedin.com/in/axel-krieger-b14a0075/ - IMERSE Lab (Johns Hopkins): https://imerse.lcsr.jhu.edu/ - Inner Logic website: https://innerlogic.io/ PODCAST LINKS - Podcast Website: https://audrownashpodcast.com/ - YouTube: https://www.youtube.com/@audrow - Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786 - Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162 TIMECODES 0:00 Cold open 0:39 Axel Krieger and Inner Logic 4:05 How surgical robots work today 9:41 AI infrastructure for device makers 11:15 From stitching to imitation learning 18:23 Autonomous gallbladder surgery steps 21:37 Action chunking transformers (ACT) 23:46 Breaking surgery into small steps 29:42 Language between high and low level policies 35:44 Adapting to surgical robot hardware 40:01 How much training data it takes 42:28 VLAs and large surgical datasets 47:30 Mixing training data 51:44 From dead tissue to live animals 54:47 Self-driving analogy and FDA approval 58:35 Inner Logic's data platforms 1:02:42 Simulated patients from CT scans 1:05:20 Simulating bones, vessels, and soft tissue 1:13:07 Where surgical robotics is heading 1:17:28 One surgeon, several surgeries 1:18:57 Training the next surgeons 1:22:19 Funding climate 1:24:52 Next milestones, from suction to suturing 1:26:58 Tumor resection and full procedures 1:29:49 Robot surgery in 20 years? 1:31:15 Advice for getting into surgical robotics 1:35:02 Final takeaway

    Robots That Learn Surgery From Surgeons: Inner Logic's Axel Krieger

  3. 10/07/2025

    Robots That Copy Scientists for Cell Therapy: Multiply Labs' Fred Parietti

    Episode 25 of the Audrow Nash Podcast. A bag of gene-edited cells can be worth about half a million dollars (Fred's range for a dose is roughly $400K to $2M), and today that work is still done by hand in clean rooms, over thousands of steps. I talk with Fred Parietti, Co-founder and CEO of Multiply Labs, about robots that manufacture cell and gene therapies using the same instruments, reagents, and consumables the scientists use, why they train from video of scientists instead of teleoperation, and where a ~$30K humanoid fits in their plant (loading and unloading carts, not the science). You'll like this interview if you're a roboticist curious about pharma, a founder picking high-value problems over pizza robots, or anyone who wants Fred's take on "infinite market" pitches and a robotics winter. The biology explainer (16:08–34:00) is the most technical stretch. EPISODE LINKS - Multiply Labs: https://www.multiplylabs.com PODCAST LINKS - Podcast Website: https://audrownashpodcast.com/ - YouTube: https://www.youtube.com/@audrow - Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786 - Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162 TIMECODES 0:00 Cold open 0:53 Multiply Labs overview 7:17 Pharma robotics gap 16:08 Next-gen pharma 34:00 Multiply Labs automation 43:54 Deployment size 1:00:04 Thoughts on humanoids 1:10:01 Training process 1:21:39 High-value problems 1:24:37 Key takeaways

    Robots That Copy Scientists for Cell Therapy: Multiply Labs' Fred Parietti

  4. 08/21/2025

    Why "Shut It Off" Fails for Bipeds: Agility Robotics' Pras Velagapudi

    Episode 24 of the Audrow Nash Podcast. For a wheeled robot, "shut it off" can be a safety move. For a biped that has to stay upright, killing power is the hazard. Pras walks through why Digit needs different rules. I talk with Pras Velagapudi, CTO of Agility Robotics, about Digit (two arms, two legs) doing paid material handling on full shifts, why a dynamically stable biped beats a heavy wheeled base for narrow aisles, the tradeoffs of wheels on feet, and how they're working with others (including Boston Dynamics) on safety standards for robots that can't just lock their wheels and call it safe. You'll like this interview if you're into humanoids, warehouse automation, or the control and safety problems that show up once a robot has to balance. The legs-and-wheels stretch (about 5:26–23:15) is the densest design argument; safety and standards (36:42 on) is where the shut-it-off problem shows up. EPISODE LINKS - Agility Robotics: https://www.agilityrobotics.com PODCAST LINKS - Podcast Website: https://audrownashpodcast.com/ - YouTube: https://www.youtube.com/@audrow - Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786 - Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162 TIMECODES 0:00 Cold open 0:42 Agility overview 5:26 Why humanoid design 13:26 Why legs, not wheels 23:15 Digit's application 36:42 Safety in humanoids 49:11 Standardizing safety 1:06:50 Digit in other industries 1:14:41 How AI fits into robotics 1:29:23 Is data still gold? 1:35:54 The future of Agility 1:38:13 Key takeaways

    Why "Shut It Off" Fails for Bipeds: Agility Robotics' Pras Velagapudi

  5. 07/08/2025

    The Best Robot Today Is Worth Less Than a 1960 Computer: Polymath

    Episode 23 of the Audrow Nash Podcast. Stefan's cold open: the best robot in the world today is less valuable to its owner than an average computer was in 1960. They argue robotics still makes teams rebuild the same navigation stack instead of buying commodity software. I talk with Stefan Seltz-Axmacher (CEO) and Ilia Baranov (CTO), co-founders of Polymath Robotics, about packaging point-to-point navigation for off-highway machines (tractors, bulldozers, mines), why SaaS feels easy because you don't rebuild payments and backends, labor shortages and vehicle SKU sprawl, and why they think humanoid-as-general-purpose is often the wrong bet next to Roomba-scale appliances. You'll like this if you build autonomy for industrial vehicles, sell into mining/ag, or want a blunt take on demos vs products. The ROI/history stretch (about 12:14–24:15) and the humanoid/minimally-viable-humanoid bit (52:02 on) are the densest arguments. EPISODE LINKS - Polymath Robotics: https://www.polymathrobotics.com PODCAST LINKS - Podcast Website: https://audrownashpodcast.com/ - YouTube: https://www.youtube.com/@audrow - Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786 - Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162 TIMECODES 0:00 Cold open 0:12 Polymath overview 6:04 Robotics standards gap 12:14 Market reality 16:43 Labor shortages 24:15 Cutting mining costs 33:55 AgTech needs action 43:16 Practical autonomy 52:02 Humanoid future 1:12:31 LLMs and coding 1:21:10 Work-kids balance 1:36:04 Key takeaways

    The Best Robot Today Is Worth Less Than a 1960 Computer: Polymath

  6. 05/01/2025

    Why Deep Tech Investing Still Has No Rulebook: AlleyCorp's Brannon Jones

    Episode 22 of the Audrow Nash Podcast. Twenty years ago, SaaS had no rule of 40. Brannon Jones's take is that deep tech is in that same stretch now: the rules are still unwritten. I talk with Brannon Jones, investor on AlleyCorp's deep tech team, about why they started the vertical in robotics, how they underwrite hardware with a value-creation multiple instead of SaaS metrics, and what that looks like in the portfolio: EyeBot's 90-second eye exam kiosk, Portal Space's satellite thrusters, and Aescape's robotic massage table (which he says raised $83M). We also get into why AlleyCorp hasn't backed a humanoid yet, and why he thinks a lot of embodied-AI demos are still pick and place. You'll like this if you raise for a robotics company, invest in hardware, or want an operator-turned-investor view of New York deep tech. The portfolio walk (about 15:43–56:00) is concrete; the investing framework (56:08 on) is the densest stretch. EPISODE LINKS - Brannon Jones X: https://x.com/robotjonez - Brannon Jones LinkedIn: https://www.linkedin.com/in/jones-brannon - AlleyCorp website: https://alleycorp.com - AlleyCorp LinkedIn: https://www.linkedin.com/company/alleycorp PODCAST LINKS - Podcast Website: https://audrownashpodcast.com/ - YouTube: https://www.youtube.com/@audrow - Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786 - Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162 TIMECODES 0:00 Cold open 0:23 AlleyCorp and Brannon Jones 3:26 What deep tech means at AlleyCorp 10:14 New York vs West Coast portfolio 15:43 Deep tech portfolio overview 17:11 EyeBot eye exam kiosk 22:25 Why deep tech vs SaaS 28:46 Portal Space satellite thrusters 32:35 Aescape massage robot 44:39 Valar Atomics 49:24 Koop Technologies 50:58 Glacier recycling robots 54:46 Civ Robotics solar surveying 56:08 Funding robotics without SaaS playbooks 1:09:50 Market-focused startups 1:14:17 Early robotics investments 1:23:40 Humanoids and value creation 1:28:32 Deglobalization and China 1:32:48 Hype vs pick and place 1:38:19 Key takeaways

    Why Deep Tech Investing Still Has No Rulebook: AlleyCorp's Brannon Jones

  7. 04/08/2025

    From Napkin Sketch to Truck: BotBuilt's Barrett Ames

    Episode 21 of the Audrow Nash Podcast. Somebody draws a house on a napkin. BotBuilt turns that into a 3D nail schedule, cuts twisted lumber in a warehouse, and ships panels so on-site framing drops from weeks to about four hours. I talk with Barrett Ames, co-founder of BotBuilt, about why they automate framing (the bones of the house) instead of the whole build, how they plan and grip imperfect lumber, and the business of warehouse construction that still has to win on jobsites. You'll like this if you care about construction robotics, founder-market fit in hardtech, or how to pick an automation wedge that isn't a race to the bottom. The lumber-handling stretch (about 6:59–17:41) is the densest engineering bit; time and cost (56:52 on) is the densest business bit. EPISODE LINKS - BotBuilt: https://www.botbuilt.com - Barrett Ames: https://www.cbames.com PODCAST LINKS - Podcast Website: https://audrownashpodcast.com/ - YouTube: https://www.youtube.com/@audrow - Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786 - Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162 TIMECODES 0:00 Cold open 2:22 Overview 6:59 Challenges 17:41 Sketch to structure 27:20 Generative solutions 30:36 Structuring build 35:49 When things go wrong 42:31 Traditional vs automated 56:52 Time and cost 1:07:36 Long-term vision 1:14:35 Construction opportunities 1:23:51 Lessons learned 1:33:32 Final thoughts

    From Napkin Sketch to Truck: BotBuilt's Barrett Ames

  8. 03/13/2025

    Eyes and Brain on the Camera: Luxonis' Bradley Dillon

    Episode 20 of the Audrow Nash Podcast. Tesla gets far on vision alone, but shipping that stack means hardware, bandwidth, AI, and sync. Luxonis's bet is cameras that are eyes, ears, and a chunk of brain at the edge. I talk with Bradley Dillon, CEO of Luxonis, about AI-enabled cameras for robots (he cites more than 150,000 sold), fusing sensors on-device, multi-device linking, models and labeling, and where they aim the platform next (including harder industrial and security screening angles). You'll like this if you build robots and don't want to become a camera company, or if you care about edge vs cloud for vision. The edge-compute stretch (about 25:01–31:41) and applications (20:26 on) are the densest product bits. EPISODE LINKS - Luxonis: https://www.luxonis.com - Bradley Dillon LinkedIn: https://www.linkedin.com/in/bradley-dillon/ PODCAST LINKS - Podcast Website: https://audrownashpodcast.com/ - YouTube: https://www.youtube.com/@audrow - Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786 - Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162 TIMECODES 0:00 Cold open 1:54 Bradley Dillon and Luxonis overview 8:22 Luxonis equipment 13:01 Integrating multiple sensors 20:26 Applications of Luxonis vision 25:01 Luxonis and computing power 28:21 Linking multiple devices 31:41 Luxonis AI models 34:53 Data generation and labeling 44:53 Future predictions 49:01 AI for scene understanding 59:31 Targeting industries 1:04:54 Smart security screening 1:09:43 Growth story 1:18:58 Chip supply challenges

    Eyes and Brain on the Camera: Luxonis' Bradley Dillon
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Ratings & Reviews

5
out of 5
4 Ratings

About

If you're building a robotics company, backing one, or deciding whether to join one, this is a way to hear how they actually work from the people running them. Founders, CTOs, investors, and researchers on how the robots work, how the business makes money, and what they've gotten wrong. Unscripted, usually over an hour, with chapters so you can skip to the part you care about. Hosted by Audrow Nash, a robotics engineer who's been interviewing roboticists since 2014. Questions and guest suggestions: audrow.com/contact

Information

  • Creator
    Audrow Nash
  • Years Active
    2023 - 2026
  • Episodes
    26
  • Rating
    Clean
  • Copyright
    © 2026 Audrow Nash
  • Show Website
    Audrow Nash Podcast

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