NEURA Robotics

Reporting directly to the CGO, I serve as the Growth Strategy Manager at NEURA Robotics. In this key position, my main goal is to identify and implement strategies to drive business expansion and market penetration.

I support the Chief Growth Officer by offering strategic advice, overseeing cross-functional initiatives, and empowering sub-departments such as Sales, Marketing, Service and Support, and People and Culture to achieve their objectives, and optimize operational efficiency.

NEURA Robotics (founded in 2019 near Stuttgart), is a German deep-tech company with a diverse team working on revolutionary robotics technologies to bring robots and humans closer together under the guiding principle "we serve humanity."

Growth Strategy Manager

Micropsi Industries

As a Deployment Strategist, we collaborate with a team of Engineers, Product Designers, and Sales Reps to deploy our software, MIRAI. We analyze major client challenges, interpret data, and identify product needs and user motivations for optimal impact.

We engage deeply with customer workflows, working with our customer success teams to understand data and product intricacies, aiding scalability.

Head of Deployment Strategy

As the inaugural Customer Success Manager, the unique opportunity to build and shape the Customer Success Department from scratch, focusing on creating effective strategies and fostering strong client relationships was something I really enjoyed. My role also involved productive collaborations with the engineering and product teams.

Artificial intelligence, software, software agents, robotics, robot control software, process prediction, predictive maintenance, and IoT.

CS Manager

GlassDollar

In my role as a Corporate Innovation Manager, I had the privilege of driving forward-thinking initiatives and fostering innovation across various projects with industry leaders like Daimler, Porsche, and BSH. 

My focus was on bridging the gap between traditional practices and innovative solutions, ensuring a seamless integration of cutting-edge ideas into established corporate frameworks.

Corporate Innovation Manager

Siemens Energy

Gas Turbines - Project Management: Innovation Lab

Collaborativ Robot (UR5) Implementation in Combustion Manufacturing.
In charge of planning, coordination and internal financing. Programming of the Cobot in the CAD / CAM environment (NX18), as well as process adaptation in the real working environment.

Digitalization/Paperless Manufacturing - Test reports as a cloud-based app
User-centred prototyping: record user specifications, describing user stories / jouneys, furthermore design, test and validate digital mock-ups (UX/UI with Adobe XD) with users.

Innovation Lab Project Driver

Siemens

Gas Turbines - Project Management: Innovation Lab
Within a Start-up enviroment, the manufacturing team creates new processes and products by indepth knowledge and out-of-the-box-thinking, agile working and fast prototyping (3D printing).

Responsible for the Innovation Lab - PCS / ULN Burner Support Housing.
Leading a team (>10 team members) by planning and monitoring the implementation of the technologies developed by the project. Contributing with the conceptualization, coordinating multi-disciplinary communication and business evaluation.

Innovation Lab Manager

Gas Turbines - Project Lead Factory Planning: Transformation
Transforming the manufacturing operations into modern working cultures, i.e. self-organizing teams, agile working modes and decentralized autonomy. 

Assistance in drawing up business plans, forecasts and budgets. Together with technical experts the development of business presentations for internal investments: cool bearings, housing and rotating components.

Project Management Assistant

Gas Turbines - Combustion Manufacturing: Apparatuses
Cataloguing and prioritisation of assembly fixtures, creating an maintenance and repair catalog and associated with it, developing and implementing an homogeneous concept for apparatuses.

Co-Project Manager Manufacturing / Production Maintenance

Garden Center Rudow - Tree Nursery

E-Commerce. Sales and customer service, coordinating supply logistics, work and process planning, assigning and scheduling of temporary employees.

Nurseryman

GermanyConcierge Incoming & Events GmbH

Event management, booking and sale, reservations, recommendations and arranging for services. Customer care in an international, high-end environment.

Concierge

Wilking & Tarrach Metallbau

Design/Concept and planning, manufacturing of construction elements, supporting structures and general metal processing. Schedule planning and client communication.

Intern

Der Holländer Pflanzencenter GmbH

Sales and customer service, coordinating supply logistics, work and process planning.
Consultant and Salesman

Robotics & AI: simplified | 44.000+ Followers | @micropsi | Ex-Siemens

I’m deeply interested in deep tech startups: 
building, bringing them to market, and scaling. Follow along as I share my journey and what I’m learning along the way.

You need $100 Billion to solve self-driving? Wrong!
A small team in San Diego is doing it with a smartphone chip and a few cameras... for $999. 🧵

Waymo spent $10 Billion. comma.ai spent $18 Million. Who won?

If you want to understand capital efficiency, look at self-driving cars.

The industry is famous for burning cash. Cruise, Zoox, and Waymo have spent a combined $20 Billion+ trying to solve autonomy.

They built "Robotaxis"... $150,000 vehicles packed with LIDAR, geo-fenced to tiny areas, and terrified of rain.

Then there is George Hotz and comma.ai.

Hotz (the hacker who jailbroke the iPhone) took a different bet: Don't build the car. Fix the car people already own.

He built a $999 aftermarket device. You stick it on your windshield. Plug it in. And suddenly, your 2016 Honda Civic has better autonomy than a 2024 Mercedes.

The industry laughed. Then the regulators sent cease-and-desist letters. So Hotz did the most "hacker" thing possible: He open-sourced the code.

Fast forward to today:
- 20,000+ active users.
- 300 Million miles driven.
- #1 Rank from Consumer Reports (beating Tesla and Ford).
- Profitable.

While Big Auto builds "walled gardens," Comma is building the "Android of self-driving." They prove that you don't need billions of dollars to solve hard tech problems. 

You need better code, a community of 1,000+ contributors, and the courage to ignore the "rules" of the industry.

We are watching the Linux moment for robotics happen in real-time.

The "Stakes" are massive. This is a battle for the soul of AI.

Option A: AI is owned by 3 mega-corps who rent it to you for $15k. 
Option B: AI is open-source, runs on consumer hardware, and you own it.

comma.ai  is the only one fighting for Option B.

If you are tired of monthly subscriptions for heated seats, check them out.


Listen to my full interview with CTO Harald Schäfer here:
https://lnkd.in/dsdmM2rC

Ilir Aliu

AI & Robotics | 100k+ | Scaling Deep Tech

Robotics has an unwritten rule: Hardware first. Deployment later. A tiny team in Brooklyn ignored that rule & shipped a robot in months. 🧵 

While everyone’s chasing $100k+ humanoids, this YC S24 team is quietly proving that the real bottleneck isn't the hardware... it's the software.

Here's the devastating truth of warehouse automation: it's built for giants.
It’s rigid, costs millions, and takes weeks or months to integrate. If a task changes, the robot is a paperweight.

This has left thousands of 3PLs stuck with a 1.3 million-worker labor shortage.

That’s the problem Ultra Robotics set out to solve.
Founded by Jon Miller Schwartz, Max Friefeld, Oliver Ortlieb, and Chetan Parthiban (YC S24), this isn't a team of academics.

It’s the same core team that built Voodoo Manufacturing, one of the world's largest 3D printing factories. They don't just know code; they know scaling real-world hardware.

So they did the opposite of everyone else.
They realized the hardware is mostly solved.
The real problem (the one holding everyone back) is the "brain."

Here's the magic:
Ultra uses reliable, off-the-shelf robot arms. Their innovation is a neural network control system.

Instead of hiring robotics PhDs to write code for every single task... they use remote human operators to train the AI by example.

This is the key.
The robot is deployed on Day 1, providing value (and collecting data) via teleoperation. It's a human-in-the-loop, learning on the job.

It creates a data flywheel:
1. More deployments
2. More real-world data
3. Smarter AI
4. Faster, more autonomous robots

They're building a data moat from actual warehouse chaos.

The Ultra mission says it all:
"When physical work becomes as flexible as software, innovation in the real world becomes frictionless."

They aren't just selling robots; they're selling flexibility.

This isn’t some mega-corp R&D project.
Ultra was founded in 2024.
They are already deployed in 3PL warehouses in Brooklyn, Chicago, and Austin, picking and packing real e-commerce orders.

They are deploying robots in HOURS, not weeks.
That’s the startup velocity we need.

Built by a small Brooklyn team, not a slow incumbent.

This is one of the most compelling robotics stories I've seen. Ultra was part of the YC S24 batch, which was a record-setter for robotics.

Listen to my full interview with co-founder and CEO Jon Miller Schwartz: https://lnkd.in/d-jeRT8r




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Talks about #startups, #technology, and #enterprenuership

Ultra

Co-founder of Ultra. Building multi-purpose, performance robots.

Engineer: Manufacturing, Ops, Robotics

Co-founder & CTO of Ultra. building robots to automate American warehouses.

Co-Founder @ Ultra. Building intelligent robots to automate the dull, dirty, and dangerous!

AI & Robotics | 100k+ Follow

Josué Charles

Robots are starting to handle throws and hits at speeds most people would struggle with. Researchers at the RAI Institute built a low-impedance platform to study dynamic manipulation, and the first demos already look wild. Two robots throw, catch, and bat a ball, and they can even rally with humans at short distance.

A few notes that stood out:
	•	Throwing speeds reach 70 kph
	•	Catching works at up to 66 kph
	•	Batting works at up to 48 kph
	•	All at only 7 meters of distance, which puts serious pressure on reaction times

The system is designed to study how robots handle fast contacts and changing trajectories

This is not a product. It is a research platform to explore how robots can coordinate timing, force, and control in situations where milliseconds matter.

Dynamic manipulation is one of the hardest open problems in robotics. Seeing robots throw and hit a moving target at these speeds is a small but meaningful step toward machines that can handle real physical skills, not just slow, scripted motions.


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RAI Institute

Benedict F.

If you work with robotics, AV, or 3D vision, this update will save you months of engineering.

Most models need complex engineering to get reliable 3D geometry.
This one does it with a plain transformer.

Depth Anything 3 is the new model from ByteDance that predicts stable, spatially consistent geometry from almost any visual input. No special architecture. No multi-task setup. Just a single transformer trained on depth rays.

✅ Single vanilla transformer as backbone
✅ One depth ray target instead of multi-task learning
✅ Outperforms DA2 in monocular depth
✅ Beats VGGT across camera pose, any-view geometry, and visual rendering
✅ Trained only on public academic datasets

It shows that simple designs can scale further than expected.
Cleaner pipelines. Fewer moving parts. Stronger results.

Project page: depth-anything-3.github.io



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🎙️ I talked with Stephan van den Brink, founder and CEO of MANUS™, the company behind some of the most advanced data gloves used in robotics:

Not only robotics: teleoperation, motion capture, and embodied AI. Manus started as a small student project and grew into a deep tech company trusted across the robotics world.

We talk about Stephan's path from studying law and economics to discovering he was meant to build things, not file documents. He explains how the first Manus glove was built in evenings and weekends, how an early Kickstarter failure opened better doors, and how an accelerator program became the turning point for the company.

Stephan shares how Manus shifted from VR gaming to B2B simulation, then to motion capture, and now to robotics. He explains why EMF tracking became their core technology and why precise hand data is suddenly in huge demand as humanoids and AI driven robots take off.

We also talk about building a company for ten years, staying alive through hype cycles, making hard calls, and focusing on what real customers need.


Building Deep Tech #92
"New Opportunities Grow From Every Failure"
with Stephan van den Brink, founder and CEO of MANUS™.


🎬 YouTube: https://lnkd.in/ePpkCdda

🎧 Spotify: https://lnkd.in/eDvwC7mx

🎧 Apple: https://lnkd.in/eyVDd_4V

CEO, Founder & Managing Director

MANUS™

Robot models get better only when humans feed them more demos. This one improves by learning from its own mistakes.

pi*0.6 is a new VLA from Physical Intelligence, that can refine its skills through real-world RL, not just teleop data. 

The team calls the method Recap, and from what I can see, the gains are not small.

A quick summary:

✅ Learns from its own rollouts using a value function trained across all data
✅ Humans only step in when the robot is about to drift too far
✅ Every correction updates the model and improves future rollouts
✅ Works across real tasks like espresso prep, laundry, and box assembly
✅ Throughput more than doubles on hard tasks, with far fewer failure cases

What stands out is the structure: 

a general policy, a shared value function, and a loop where the robot collects data, improves the critic, then improves itself again. 

No huge fleets of teleoperators. 
No massive manual resets.

If VLAs can reliably self-improve in the real world, the bottleneck shifts.

Data becomes cheaper. Deployment becomes the real test bench.

Full paper, videos, and method details here:
https://lnkd.in/dgCeZdjT

Most humanoid projects talk about real work.
Very few last an hour on a real line.
This week I saw a case that matters for anyone building robots, perception, or physical AI.
Kinisi deployed its first mobile manipulation system into a live recycling facility. Not a demo. Not a staged test. A real production line with real output pressure. 

Why this matters if you want robotics to deliver real value on your floor:

• Handles mixed glass with random poses and no fixed fixtures.
• Runs real grasp selection under noise, vibration and production variability.
• Maintains throughput while avoiding breakage on a delicate material.
• Shows mobile manipulation doing actual shift work instead of controlled lab runs.

Kinisi published a video that shows what the robot sees and how sensor data turns into action. This is the part most teams struggle to explain to customers, so the educational angle is useful for anyone working on adoption. 

On top of this, the team signed a pilot with a global automotive manufacturer to explore humanoid use cases in production. 

The direction is clear. 

Wheeled mobility (not legs!) plus strong perception seems to be shaping a large part of industrial humanoids right now. 

I know Brennand from earlier conversations and from our podcast session, and I am always glad to see European teams push the category forward. Wishing the Kinisi team continued success.




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Robot tiles that let you walk forever in VR

Researchers at the University of Tsukuba built a tile that moves under your feet as you walk.

It uses sensors to read your gait and predict where your next step will land.

✅ Each tile slides into place before your foot touches down.
✅ To you, it feels like you keep walking forward.
✅ In reality, the system keeps you in the same small area.

This could remove one of the biggest limits in VR.

You get natural walking without a big treadmill setup.

And no risk of drifting into a wall.

Simple idea. Clever execution.
Robotics keeps finding strange ways to solve motion problems.

Credit: Unilad Tech



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Robots that can learn real-world skills in minutes instead of months are not science fiction anymore.

We are watching the shift happen in front of us.

The team at Toyota Research Institute  has been pushing this space for years. Now their work is leaving the lab and moving into real industrial environments at a pace that is very rare in robotics.

The video says more than any technical breakdown, but here is the simple idea behind it:

✅ Robots that learn directly from human behaviors
✅ Skills that transfer from research platforms to real hardware
✅ A system that keeps improving the more it is used
✅ Real customers already testing it in the field

No hype. Just clear progress toward robots that can handle useful work in real places.

If you want to follow where this goes next, TRI is also looking for a Head of Customer Success to help bring these systems into enterprise environments. It is a founding role for someone who likes building from zero. 

More info is in the video and the link from the team: https://lnkd.in/dnJjn7sG

Seen at Dave Johnson  



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A simple CNC machine. Wood. A rotating tool. And yet the pattern shifts from circles to perfect hexagons with a level of accuracy most factories still struggle to hit.

I worked five years in manufacturing at Siemens, and this is the part people often overlook.

The real magic is not new or loud.

It is stability. 

Repeatability. 

Tool paths that were tuned forever. Precision that just keeps working.

Robotics is in a hype cycle right now, but manufacturing has delivered quiet perfection long before anyone talked about foundation models.

Sometimes the most interesting engineering is the part that does not ask for attention.



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Humanoids learn dexterity from videos? Wrong!
Make a fist, cameras go blind. A tiny Dutch startup fixed what billion-dollar labs couldn’t. 🧵

In 2014, Stephan van den Brink was a tax consultant at Ernst & Young. He had a stable career in fiscal law. But he couldn't stop thinking about Virtual Reality.

He saw a gap: "We have headsets to see, but no way to touch."

So he quit. He founded MANUS™ to build the ultimate VR gaming glove. They flew to E3 in Los Angeles. They slept in cheap motels, and snuck their booth equipment past security because they couldn't afford the union fees.

They built a sleek, futuristic glove for gamers. And nobody bought it.

Gamers didn't want to wear gloves. It was too much friction. Most startups would have died there. Manus didn't. They pivoted.

Stephan and his team realized that while gamers didn't care about finger precision, engineers did. 

• NASA needed it for simulation. 
• Hollywood needed it for motion capture. 
• And eventually... Robotics needed it for AI.

Today, the Humanoid Robot revolution is built on data. 

To teach a robot how to fold a shirt or crack an egg, a human has to do it first (Teleoperation). Cameras (Computer Vision) often fail because fingers block each other ("occlusion").

Manus had spent 10 years perfecting Electro Magnetic Field (EMF) tracking. It doesn't use cameras. It uses magnetic fields to know exactly where a finger is, with millimeter precision, even in the dark.

When Tesla and other giants started building humanoids, they didn't build their own gloves. They called Manus.

I had Stephan on the podcast to discuss this incredible 10-year journey. We talked about:

The hustle, how to launch a hardware startup with no money, on how to survive when your target market (gamers) rejects you, why magnetic fields beat computer vision for ground-truth data, and last but not least:

How human dexterity is being "uploaded" into robots.

This is a masterclass in resilience and finding "Product-Market Fit" in the most unexpected places.

Listen to the episode here:
https://lnkd.in/dD68NUqX

And thanks to Dove Feng for making this happen!

Most robotics startups never make it out of the lab.
This one raised $20M and deployed in 100+ factories.
[If you build robotics, save this 🧵]

This is one of the clearest lessons I’ve seen on execution vs technology.
[All lessons summarised at the end 👇)

Three founders (Rudy Cohen, Louis DUMAS, and Albane Dersy) built a factory-first robotics company now deployed across Europe, the US, and Japan.

They had:

• No hardware background
• No manufacturing network
• No industrial reputation

But they raised $20M and deployed in 100+ factories by doing what most founders avoid:

They treated execution, not tech, as the product.

Most deep-tech teams make the same mistakes:

• Talk to 5 customers, not 50
• Polish the product for years
• Expand only when a country “is ready”
• Treat deployment as a support function

Inbolt did the opposite.

Here’s where it started.

Not in a lab.
Not in a research paper.

But in a bus maintenance shop, watching workers struggle with daily tasks.
Real pain.
Real chaos.
Real constraints.

Since then, the three founders have spent more time on factory floors than most executives will in an entire career.

And the magic was the mix.

• Rudy with deep engineering roots
• Louis with aerospace and robotics expertise
• Albane with operations, sales, and commercial execution

That combination is what made the company work.

The execution lessons:

1. Talk to dozens of customers.
Not five.
Factories are fragmented.
You need operators, maintenance, quality, integrators… all of them.

2. Sell early.
They closed deals before the company was even fully set up.

3. Pivot based on customer pull.
Their early idea guided human workers.
Customers said, “Put this on robots.”
So they did.

4. Pair technical excellence with operational excellence.
The tech handles unstructured environments.
Albane built the systems to deploy it fast and reliably.

5. Deployment is a strategic weapon.
Most teams outsource it.
They didn’t.

6. Go global early.
Not because it’s cool.
Because automotive OEMs operate everywhere.

7. ROI > AI.
Factories don’t care about “intelligence.”
They care about uptime, cycle time, and payback.

This is how a small team with no manufacturing roots outpaced better-funded competitors.

If you want the deeper story, listen to my full conversation with
co-founder and COO Albane Dersy.

❗ It’s one of the best masterclasses I’ve recorded
on building and scaling robotics in the real world.

🎥 Full episode: https://lnkd.in/dHS5WwNa

Robots that can learn new skills without robot data. Sunday Robotics shows a different path. 

That is the idea behind ACT-1.

Instead of collecting thousands of teleoperated robot demos, they train a robot foundation model from human motion alone. 

No robot in the loop, just people wearing a glove that mirrors Memo’s hand.

Why this matters:
If you can learn from human motion at scale, you remove the biggest bottleneck in home robotics. You do not need fleets of teleop robots in real kitchens or bedrooms. You only need people showing skills with the glove.

What ACT-1 can already do:
	•	Clean a full dinner table
	•	Fold laundry for hours
	•	Assemble boxes
	•	Make espresso with long, uninterrupted runs
	•	Improve through RL (their π*0.6 model more than doubled throughput)

The interesting part is the data pipeline.

More than 500 people collected human motion demonstrations with the glove. The system builds a general understanding of objects and simple physics from that data, then transfers it to Memo.

If you want to dig into the technical write-up:
https://lnkd.in/dfWwST7M

Applications for the 2026 home beta (50 households) are open:
https://lnkd.in/dciP_gSp



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Bioinspired robot:
Fly - Roll - Walk - Crawl [Paper ⬇️]

Multi-Modal Mobility Morphobot (M4), a revolutionary robot inspired by nature's most adaptable creatures.

✅ Capable of multiple forms of movement: flying, rolling, crawling, and more.
✅ Features adaptive appendages that function as wheels, thrusters, and legs.
✅ Equipped with advanced sensors and autonomous capabilities for navigating complex terrains.

M4 shows the future of robotic locomotion, seamlessly adapting to diverse environments.

📍Paper: https://lnkd.in/ev9p9HDT

Caltech: https://lnkd.in/dgH7kKZH

Funded by 
NASA Jet Propulsion Laboratory






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Hardware Production 🤝 Robotics

But these motors... are CRAZY synced:

CNC motor synchronization helps make sure that different motors in a machine work together smoothly so that the machine can cut and shape materials with perfect accuracy.

This is really important for making sure the machine does its job correctly.

✅ Helps the machine's parts move together perfectly, reducing mistakes and making things more accurate.

✅ Prevents the machine from getting damaged by keeping everything aligned.

✅ Allows the machine to work faster by making sure the motors are in sync.

Synchronized CNC motors make sure machines work better, last longer, and create more precise products.





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Robot policies fail on the hard parts of manipulation. The moment contact, friction, or force uncertainty shows up, the success rate drops fast.

CR DAgger shows a very different path. 

You take a pre trained policy. 
You let a human correct it in the real world for a short time. 

And the system uses those corrections to learn a force aware residual policy that fixes the failure cases.

The surprising part is the sample count❗️

They report improvements to near perfect performance on contact heavy tasks with only 50 to 100 episodes of correction.

Key ideas in simple terms:

✓ A compliant interface so humans can give tiny corrections without stopping the robot
✓ A residual policy that adds the missing force reasoning to a position only base policy
✓ A fast update loop that makes the policy better with every bit of feedback

They tested tasks like book flipping and belt assembly. The gains are large. It beats finetuning and it beats retraining from scratch.

Thanks for sharing, Yifan Hou!!

 
📍Paper:
https://lnkd.in/dAza-p_c

Extended version with more experiments:
https://lnkd.in/dmD_5FTz

Code:
https://lnkd.in/d4uB4wYj



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PostDoc at Stanford University

Tapan P.

Most robots still need markers, checkerboards, or long calibration rituals just to know where their arms are.

Now it works from raw images in seconds.

roboreg is a markerless multi arm localization toolkit that plugs into ROS 2 and RViz. 

No special hardware. 
No custom setup. 

You toggle between robot descriptions and the system figures out the rest.

The idea is simple:

✅ Hand eye calibration from plain RGB or RGB D images
✅ Only three robot poses needed for millimeter accuracy
✅ Works with any ROS 2 compatible robot and camera
✅ Fully open source under Apache 2.0

It is powered by Hydra, a new marker free ICP variant that converges far more reliably than classical baselines and runs in under a second.

Thanks for sharing, Martin Huber ! 

If you want to try it:

roboreg: https://lnkd.in/d2H3GgX6
ROS 2 roboreg: https://lnkd.in/dcy8wXU7
Hydra paper: https://lnkd.in/dzGWWeer
pip install roboreg

More details and discussion on Open Robotics Discourse:
https://lnkd.in/dTm_hwEs

Music tools usually live on flat screens.
This is an Apple Vision Pro app that turns music production into a modular, spatial system.

Sounds are blocks. 

Arrangements become structures. 

The composition exists as a physical object in the room, not a timeline on a screen.

OPERATOR treats music as something you build in space.

What stands out is not complexity, but restraint. Simple blocks, a snake-like sequencer, and interactions that feel closer to assembling than editing. 

No DAW metaphors. 

No menus overload.

It feels less like software and more like a spatial instrument. 

A good example of how XR can rethink creative tools instead of porting old ones into 3D.

Built by FUN AND AWE  
Saying hi to founder Maxim Zhestkov ! 




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