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 (http://audrow.com/contact)
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/ (https://www.skylarklabs.ai/)
PODCAST LINKS
- Podcast Website: https://audrownashpodcast.com/ (https://audrownashpodcast.com/)
- YouTube: https://www.youtube.com/@audrow (https://www.youtube.com/@audrow)
- Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786 (https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786)
- Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162 (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
6 Oct 2026
Axel Krieger (Inner Logic): Robots That Learn Surgery From Surgeons
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/ (https://www.linkedin.com/in/axel-krieger-b14a0075/)
- IMERSE Lab (Johns Hopkins): https://imerse.lcsr.jhu.edu/ (https://imerse.lcsr.jhu.edu/)
- Inner Logic website: https://innerlogic.io/ (https://innerlogic.io/)
PODCAST LINKS
- Podcast Website: https://audrownashpodcast.com/ (https://audrownashpodcast.com/)
- YouTube: https://www.youtube.com/@audrow (https://www.youtube.com/@audrow)
- Apple Podcasts: https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786 (https://podcasts.apple.com/us/podcast/audrow-nash-podcast/id1716486786)
- Spotify: https://open.spotify.com/show/74jWpWiLwsasY2QHtDcl8I?si=6c92796bf9554162 (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
7 Oct 2025
What If Robots Actually Did the Most Important Jobs in the World?
What if robots stopped doing just the simple, repetitive work and instead took on the most important, life-changing jobs in the world?
I talk with Fred Parietti, Co-founder and CEO of Multiply Labs, about how advanced robotics can transform pharmaceutical manufacturing by automating the complex and high-value tasks that save lives.
You'll like this interview if you're interested in robotics, automation in healthcare, and how technology can tackle some of the toughest challenges in next-generation medicine.
TRANSCRIPT
- https://audrownashpodcast.com/episodes/25-multiplylabs
EPISODE LINKS
- Fred Parietti's website: http://federicoparietti.com
- Fred Parietti's X: https://x.com/FredParietti
- Fred Parietti's LinkedIn: https://www.linkedin.com/in/fred-parietti
- Multiply Labs website: https://multiplylabs.com
- Multiply Labs X: https://x.com/multiplylabs
- Multiply Labs LinkedIn: https://www.linkedin.com/company/multiply-labs
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) Start
(0:53) Multiply Labs Overview
(7:17) Pharma Robotics Gap
(16:18) Next-Gen Pharma
(34:04) Multiply Labs Automation
(43:58) Deployment Size
(1:00:06) Thoughts on Humanoids
(1:10:02) Training Process
(1:21:45) High-Value Problems
(1:24:58) Key Takeaways
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Apple Podcasts (US)
5.0 / 5
4 ratings
Spotify
5.0 / 5
7 ratings
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