Audrow Nash Podcast

Episode 18 of the Audrow Nash Podcast.

Vision and audio have digital sensors. Smell mostly doesn't. Kordel's bet is to make olfaction as usable for robots as cameras are today.

I talk with Kordel France, founder of Scentience, about how smell sensing works, what goes wrong when you try to identify complex odors, near-term use cases (from quality control to security-style detection), datasets and standardization, and what an API for smell would look like for robot builders.

You'll like this if you're curious about a new sensing modality, or you're hunting for application wedges that aren't another gripper demo. The mechanisms stretch (about 8:14–15:08) is the densest science bit; API / integration (38:38 on) is the densest product bit.

EPISODE LINKS

- Scentience: https://scentience.ai/

- Kordel France: https://kordelfrance.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

1:22 Kordel France and Scentience overview

2:39 Smell's role in robotics

5:19 Smell sensor problem solving

8:14 Smell identification mechanisms

15:08 Use cases

16:42 Near-future innovations

25:09 Application development

27:53 Scent technology standardization

33:02 Scent recognition dataset

33:33 Industry vs academia

36:56 Startup landscape in smell robotics

38:38 Smell sensing API

42:40 Sensor integration

49:58 Weather data's impact on smell sensors

Episode 17 of the Audrow Nash Podcast.

ARIA is the UK's DARPA-like funder without the defense mission. Jenny Read's program bets on robot hardware, especially dexterity, with a budget she puts around £50 million.

I talk with Jenny Read, Programme Director at ARIA, about how the agency works, why she thinks hardware lagged while software ate the attention, gear ratios and touch/state awareness, integrating body design with neural control, and how they pick and measure high-risk projects.

You'll like this if you raise or apply for ambitious robotics hardware research, or you want a funder's view of the dexterity bottleneck. The hardware/dexterity stretch (about 8:12–33:19) is the densest technical bit; funding logistics (21:52 on) is the densest process bit.

EPISODE LINKS

- ARIA: https://www.aria.org.uk

- Jenny Read: https://www.jennyreadresearch.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 Introduction to Jenny Read and ARIA

3:49 Jenny's role in ARIA

6:26 Jenny's robotics focus

8:12 Areas of focus within hardware

9:40 High versus low gear ratios

11:56 Touch sensing and fragility

13:40 Integrating body design and neural control

18:47 Jenny's background and funding approach

21:52 Selecting focus areas, funding, logistics

27:56 Dexterity challenge and investment focus

33:19 ARIA's allocation strategy

42:59 Bottlenecks: manufacturing and motors

45:38 Measuring success

48:28 Key takeaways

51:49 Outro

Episode 16 of the Audrow Nash Podcast.

Three interviews from ROSCon 2024 in Denmark: Dexory scaling warehouse inventory robots and treating testing as how you ship fast, Asimovo building a Kubernetes-based RoboDevOps platform for team setups and sim compute, and ZettaScale on Zenoh as a protocol aimed at DDS's rough edges for ROS 2.

I talk with Marcus Scheunemann (head of autonomy, Dexory) about tall scanning robots for lost inventory and advocating for testing when the org wants features; with Christine Fraser (CEO & founder, Asimovo) about collaborative robotics development environments across laptop, on-prem, and cloud; and with Angelo Corsaro (CEO & CTO, ZettaScale) about Zenoh end-to-end from microcontroller to data center, discovery, and ROS 2 middleware.

You'll like this if you were at ROSCon, ship ROS 2 fleets, or care about warehouse robotics, developer platforms, or middleware. Segments: Dexory ~2:12, Asimovo ~19:22, ZettaScale / Zenoh ~44:32.

EPISODE LINKS

- ROSCon 2024: https://roscon.ros.org/2024/

- Dexory: https://www.dexory.com/

- Asimovo: https://asimovo.com/

- ZettaScale: https://www.zettascale.tech/

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

2:12 Dexory: Marcus intro

3:24 Series B and scaling

6:52 Lost inventory problem

11:10 Software behind Dexory

13:58 Advocating for testing

17:18 Dexory next 2–5 years

19:22 Asimovo: Christine intro

21:17 Solutions for every setup

23:56 Diverse workflows

29:23 Clusters across platforms

34:07 Progress and adoption

40:04 One-year goals

43:09 Where to learn more

44:32 ZettaScale: Angelo intro

49:56 Distributed queries

53:07 Routing and sync

1:01:17 DDS challenges and ROS 2

1:05:29 Zenoh name story

1:07:48 ROS integration

1:13:11 Next 1–3 years

Episode 15 of the Audrow Nash Podcast.

Dave's split: open-source MoveIt was built to compare algorithms. Shipping needs containers, recovery, sequencing, and fault handling. That's the gap MoveIt Pro is aimed at, for "unstructured" arms outside teach-pendant factories.

I talk with Dave Coleman, founder and CPO of PickNik Robotics, about unstructured robotics (strawberry fields, subsea, space), why incumbents optimized for warehouses leave those problems underserved, MoveIt Pro as runtime plus developer tools, and PickNik's NASA / Space ROS work.

You'll like this if you build manipulation stacks, evaluate MoveIt for production, or care about space and outdoor arms. The MoveIt Pro stretch (about 13:45–27:00) is the densest product bit; NASA / Space ROS from about 45:22.

EPISODE LINKS

- PickNik: https://picknik.ai

- Dave Coleman LinkedIn: https://www.linkedin.com/in/davetcoleman

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

1:04 Guest intro

1:28 PickNik overview

3:16 Why manipulation is hard

4:54 Unstructured robotics

13:45 MoveIt Pro

44:11 High-level commands

45:22 NASA collaborations

48:33 Space ROS

52:06 Space robotics challenges

53:32 Industry applications

1:02:06 Hivebotics case study

1:10:57 Mobile manipulation and humanoids

1:19:20 Mobile manip opportunities

1:25:32 PickNik vision

1:27:58 Close

Episode 14 of the Audrow Nash Podcast.

Saman's frame: a lot of US metal-fab shops run one shift, so machines sit idle most of the available hours. Formic sells drop-in robots customers pay for by the hour, and tracks usage hours, not units shipped.

I talk with Saman Farid, founder and CEO of Formic, about cutting typical robot deployment from roughly four to six months to about two weeks, why most of the cost is site-specific engineering (concrete, EOAT, safety) rather than making the arm "smarter," and how they run post-deploy service across about a hundred US manufacturing facilities.

You'll like this if you sell or buy factory automation, care about SMB manufacturers, or want a blunt take on uptime and maintenance as the product. The utilization / machine-tending stretch (about 14:30–20:00) and the "what's robotics doing wrong" bit (1:28:04 on) are the densest arguments.

EPISODE LINKS

- Formic: https://formic.co/

- Saman Farid LinkedIn: https://www.linkedin.com/in/samanfarid/

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 Guest intro

1:39 Formic mission and scale

4:01 Deployment challenges

9:46 Case studies

14:30 Utilization and machine tending

28:35 Collecting site data

35:08 Configuring a workcell

40:30 Programming with generative AI

50:09 Simulation and AI in the loop

52:54 Software stack

54:30 Sim to reality

59:07 LLMs for data ingestion

1:03:43 Support and uptime

1:12:26 Scaling and in-house service

1:19:38 Serving SMBs

1:28:04 What robotics companies get wrong

1:31:04 US manufacturing

Episode 12 of the Audrow Nash Podcast.

Pipedream Labs lays storm-drain-class pipe, adds a rail, and runs tote robots underground. Canon Reeves argues the hard product isn't the tunnel robot. It's the portals people actually grab food from.

I talk with Canon Reeves, CTO and co-founder of Pipedream Labs, about underground last-mile delivery, Instant Pickup as a drive-thru-style parking-lot wedge, and how they think about scaling from QSR portals toward a wider network without pretending Manhattan is day one.

You'll like this if you care about logistics robotics, urban infrastructure, or shipping physical systems that have to survive permits and maintenance. The Instant Pickup stretch (about 0:06:58–0:12:26) is the densest product bit; install/permits and Peachtree Corners (0:20:15 on) is the densest infra bit.

EPISODE LINKS

- Pipedream Labs: https://www.pipedreamlabs.co/

- Pipedream Labs X: https://x.com/pipedream_labs

- Canon Reeves X: https://x.com/ReevesCanon

- Pipedream Labs LinkedIn: https://www.linkedin.com/company/pipedream-labs/

- Canon Reeves LinkedIn: https://www.linkedin.com/in/canonreeves/

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 Episode intro

0:37 Meet Canon Reeves

1:05 Underground delivery vision

1:49 Storm drains and rails

3:17 Last-mile cost problem

6:58 Instant Pickup drive-thru

12:26 Parking lot to city

20:15 Pipe install and permits

48:22 Modular pipe network

50:14 Sell before you build

52:39 Small team, long runway

54:45 Hiring killer engineers

56:43 Remote vs in-person

1:00:43 Modular future-proofing

1:07:17 Seed round scaling

1:28:00 Founder advice

Episode 11 of the Audrow Nash Podcast.

Most property-casualty books pay out most of every premium dollar. In Koop's early data, Sergey Litvinenko says their robotics book is paying out far less so far, and the plan is to push rates down as the models improve.

I talk with Sergey Litvinenko, co-founder and CEO of Koop, about underwriting robots, autonomous vehicles, and drones, how they use utilization and troubleshooting signals when public benchmarks don't exist, and why he thinks robot bugs that get patched fleet-wide change the insurance math versus human drivers.

You'll like this if you deploy robots commercially, work in AV or industrial automation, or care about the finance layer that sits under adoption. The humans-vs-robots stretch (about 0:50:44–0:54:26) is the densest conceptual bit; novel domains (0:18:00 on) is the densest "how do you underwrite that?" bit.

EPISODE LINKS

- Koop: https://www.koop.ai/

- Sergey Litvinenko LinkedIn: https://www.linkedin.com/in/sergey-from-koop

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

1:46 Intro and background

2:37 What is underwriting?

4:07 Insurance for robotics

6:41 Journey to Koop

11:38 Risk assessment and data

18:00 Novel robotics domains

32:56 Training robotics underwriters

47:12 Performance guarantees

48:34 Erroneous data handling

50:44 Humans vs robot behavior

52:20 Payout metrics and losses

54:26 AI edge cases

1:04:22 Data collection API

1:29:41 Future vision for Koop

Episode 10 of the Audrow Nash Podcast.

The obvious kitchen robot market is fast casual. Chef Robotics pivoted to high-volume food factories instead: fewer ingredients per station, hair-on-fire labor gaps, and a path to production data you can't download from the internet.

I talk with Rajat Bhageria, founder and CEO of Chef Robotics, about AI-enabled food assembly robots, why simulation fails on deformable food, and how they sell robotics-as-a-service against site-acceptance tests before customers pay recurring fees.

You'll like this if you care about manipulation in messy environments, food or manufacturing robotics, or founder GTM for hardware. The food-manipulation stretch (about 0:48:46–1:02:06) is the densest engineering bit; production-vs-prototype (1:36:58 on) is the densest ops bit.

EPISODE LINKS

- Chef Robotics: https://www.chefrobotics.ai/

- Rajat Bhageria LinkedIn: https://www.linkedin.com/in/rajatbhageria/

- Chef Robotics LinkedIn: https://www.linkedin.com/company/chef-robotics/

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 Episode intro

1:36 Intro to Rajat

3:03 Early ventures, AI

5:51 Picking food market

8:15 Food labor pain

13:13 High mix pivot

42:33 Bootstrapping real-world AI

45:23 Expanding markets

48:46 Food manipulation challenges

1:02:06 Learning from demonstration

1:23:12 Field replaceable units

1:24:05 Scaling global deployment

1:25:25 Hardware upgrades

1:29:45 AI robotics trends

1:36:58 Production is harder

1:53:45 Hiring best people

Episode 9 of the Audrow Nash Podcast.

Robotics logs aren't web metrics. Dense multimodal data, bad edge bandwidth, and "S3 buckets full of bags" break the Datadog/Grafana playbook Adrian Macneil saw work in software.

I talk with Adrian Macneil, co-founder and CEO of Foxglove, about building visualization and observability for robots, why they open-sourced the MCAP logging format, and why Foxglove 2.0 closed further development of the open-source visualizer.

You'll like this if you ship robot software, care about logging and replay tooling, or want a founder-level account of open-source strategy in robotics infrastructure. The open-to-closed stretch (about 1:24:55–1:41:59) is the densest product-strategy bit; MCAP (0:07:20 on) is the densest format bit.

EPISODE LINKS

- Foxglove: https://foxglove.dev/

- Actuate: https://actuate.foxglove.dev/

- Adrian Macneil LinkedIn: https://www.linkedin.com/in/adrianmacneil/

- Foxglove LinkedIn: https://www.linkedin.com/company/foxglovedev/

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 Episode intro

1:28 Meet Adrian Macneil

1:43 Genesis of Foxglove

4:55 Robotics data logging pain

7:20 MCAP logging standard

18:35 Upload and ingestion

30:46 Cloud indexing

35:53 Visualization workflows

39:05 Performance and platforms

51:54 Extensibility and customization

56:50 Visualizer evolution

58:17 Plugins and extensions

1:00:23 Advanced visualization

1:06:57 WebAssembly performance

1:08:36 Simulation visualization

1:12:30 Future directions

1:18:48 Robotics tooling gaps

1:24:55 Open to closed, 2.0

1:41:59 Actuate conference

Episode 8 of the Audrow Nash Podcast.

Cuby's product is a mobile microfactory: roughly four dozen shipping containers that land, stand up in weeks, and make home components for nearby assembly. The point is to ship the plant, not only the house.

I talk with Oleg Kondrashov and Aleks Gampel, co-founders of Cuby Technologies, about why containerized "factory in a box" setup aims for about a month to start manufacturing versus many months fitting out a warehouse, how they think about housing labor and cost pressure, and why the business is built around productizing and distributing factories (including white-label / license paths).

You'll like this interview if you care about construction automation, prefab/factory homebuilding, or hardtech business models that sell CapEx plants. The container lead-time stretch (about 38:02–39:54) is the densest ops bit; productizing factories and cashflow talk (55:15–1:04:05) is the densest business bit. Treat utilization and $/ft² figures as their model, not audited results.

EPISODE LINKS

- Cuby Technologies: https://www.cubytechnologies.com/

- Cuby LinkedIn: https://www.linkedin.com/company/buildwithcuby/

- Oleg Kondrashov LinkedIn: https://www.linkedin.com/in/olegkondrashov/

- Aleks Gampel LinkedIn: https://www.linkedin.com/in/iamaleksandrgampel/

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 Introducing the episode

1:27 Meet the founders

2:23 What is Cuby? Mobile microfactories

7:07 Aleks' journey: real estate to PropTech to Cuby

13:37 Inside the R&D center

19:28 The vector theory

23:42 Addressing the housing crisis

31:30 Construction process challenges

34:30 Foundation to finish with Cuby's tech

38:02 Why shipping containers

39:54 Challenges and competitors

40:44 Customizing shipping containers

41:11 Engineering and production innovations

45:42 Cost efficiency and global talent

47:47 Comparing engineering schools

55:15 Productizing factories

1:00:56 Expected cashflow from a mobile microfactory

1:04:05 Assembling houses with humanoids

1:09:13 Cuby's software setup

1:13:48 Scaling and future plans