TLDR - title

I honestly think if humanoid robots are going to be safely deployed and become a part of daily life they need a lot more to go off than vision language action (VLA) models, which if i’m not wrong, is the dominant type of foundation model for majority of robots that helps them understand the world around them.

My argument is simple, robots will have far more strength and speed which will cause accidents and general harm if they lack complex sensory inputs, which will in turn cause backlash against adoption.

There are so many behaviors we do unconsciously to be safe and keep others safe, which just wouldn’t be imitated by robots trained on videos, at least not by the early iterations. Like the difference in force when holding a child’s arm or opening different kinds of doors, or moving a delicate object like a cake, or shutting a window. All of these need different applications of force, sometimes even adapting the level of force mid-task.These things are impossible to learn from videos.

AI has maybe solved language and that’s great but physical interaction is just so much more complex.

These are fairly simple arguments I know, but come on, we humans have 5 senses for input since we’re born and we mess up regularly. A robot that learns the world through only 1 sense is a safety hazard. Change my view

If we map out the number of motors needed in each part of a robot’s body the same way the homunculus maps the brain space used for each body part, the two aren’t too far off.

What’s shockingly similar: fingers and hands take up so much room. On a Unitree G1 with dexterous hands, the fingers alone get 14 of its 43 motors, about a third. In the human motor cortex, hands get roughly a quarter to a third.

The biggest difference: human brains need a lot of space for eating and speech. Lips, tongue, jaw and throat take about a third of the map. Robots bypass that altogether. The G1 has zero face motors and talks through a speaker.

Both images show one side of the body. On the robot side, each dot is one motor.

Sources: G1 joint counts come from Unitree’s published specs. The human side is Penfield’s motor homunculus, whose proportions are approximate.

so as i usually do, i spent about an hour producing an analysis of interpretation of bodily movement using my language-based analytical technique only to receive a take down notice from the moderators stating that it was low-effort work.

admittedly, the post consisted of an image and a hyperlink. the actual analysis at the link is indeed short, but the actual theory and research behind took many hours and thousands of analyses.

its a bit like looking at a robotic arm and saying, 'meh! nothing to see here! - delete.'

i was hoping the comments would allow me to elaborate further

it is what it is.

for anyone who would like to see the analysis that the moderators removed, let me know and you can decide for yourself.

rrobot companies love telling you what there robots can do but its weirdly hard to find out what’s actually inside them. Like which AI model, which chip, what cameras and sensors, how it was trained, what software it runs. The info is out there but its scattered all over spec sheets, github repos, press releases and supplier announcments.

So I started putting it all in one place. Its called Teardex, teardex.com. Every robot gets a page and every fact links to where it came from, with a saved copy incase the page dissapears. Each fact is marked confirmed if the robot maker or the part maker said it, likely if it came from a distributor or the press, and rumoured otherwise. If something changes the old version stays visible so you can see what got corrected.

Its tiny right now, just the Unitree G1 and Go2, Spot, 1X NEO and Figure 02 plus about 30 parts. The robot pages will always be free.

Im trying to figure out what to build next and if anyone would ever actually pay for it. Some ideas I had are comparing a few robots side by side, getting an alert when something changes on a robot you follow, downloading the data as a spreadsheet, or ordering a custom write up like every humanoid on sale and the chip it uses.

Would any of that be useful to you or the company you work at? would you pay for one of them and what price would feel fair? And honestly if the answer is you would never pay for this I want to hear that too lol

Also curious where you guys look today when you need this kind of info, and what robots I should add next.

One of the more interesting things I've learned from working with egocentric robotics data is that we tend to underestimate the importance of the camera itself.

A conventional video pipeline treats the camera as a source of images. Once the footage is recorded, the camera largely disappears from the dataset. That approach makes sense for many computer vision applications, but it becomes limiting when the goal is to understand physical interaction.

If someone reaches towards an object, the RGB frames tell you what the camera observed. Without information about the camera's position and orientation, however, you have a much harder time establishing where that observation exists in three-dimensional space.

Camera intrinsics and distortion parameters describe how a scene projects onto the image. Stereo extrinsics describe the cameras’ position and orientation relative to each other. With a known distance between the cameras, a matching algorithm can turn a synchronised pair of frames into metric depth. What we store is the calibrated stereo video, not a depth channel. The geometry is what makes depth recoverable in real units later. Tracking the whole rig's changing position and orientation in the room requires an additional localization or motion-tracking process.

We've been building this into an egocentric setup and combining it with motion tracking. Companies such as Kognic are tackling a related problem from the sensor-fusion and annotation side, and Unidata is working on multimodal data collection for robotics. The result is a recording that contains synchronised stereo video alongside calibration, camera pose, a body skeleton and hand coordinates. Depth comes out of that stereo footage downstream, at whatever quality the matching algorithm and the scene allow.

That additional information changes what you can do with the dataset later. You can work on reconstruction, align different sensors, reason about relative positions and potentially transfer observations into other environments or robotic embodiments.

The difficult part is making all of these signals agree with one another. Calibration and synchronization errors can turn a technically impressive capture into a surprisingly difficult dataset to use.

That’s why I think sensor and dataset design should happen together. We should decide which spatial information the intended tasks need, how accurately it must be recovered, and what must be recorded to make that possible. Sometimes that justifies additional sensors; sometimes a simpler setup with well-preserved calibration and metadata is sufficient. 

The cheapest dataset to collect is not necessarily the cheapest dataset to make useful.

This group is seeking humanoid AI robot demos for an AI Robotics meeting coming up in November in Colorado.  I think you could reach them via their website: https://rmaiig.org/contact/

Applications are now open to exhibit at the AI Robotics Showcase, hosted by the Rocky Mountain AI Interest Group (RMAIIG) on Tuesday, November 17, at CU Boulder’s East Campus. There is no cost to exhibit.

We’re bringing together companies, researchers, students, and community members for an engaging evening of AI robotics, featuring live demos, presentations, networking, and free pizza.

The event has two parts: an AI robot showcase and demos, followed by pre-selected presentations in the lecture hall.

During the showcase, attendees can explore projects, see live demos, ask questions, and meet the people building them. We’re expecting 300+ in-person attendees, and the event will also be promoted to our RMAIIG community of 4,500+ members.

Event Details
University of Colorado Boulder
Ann and H.J. Smead Aerospace Engineering Sciences
3775 Discovery Drive
Boulder, CO 80303

Tuesday, November 17, 2026
Showcase & Robot Demos: 5:45–7:15 PM
Presentations: 7:15–8:45 PM

If you have an AI robotics project you’d like to showcase, we’d love to hear from you. Exhibiting is free, and your project doesn’t need to be 100% complete when you apply. If you have a great idea or something still in development, tell us what you’re working on!

Application Deadline: October 31

Humanoid robots are moving beyond vehicle inspection and into factory logistics.

UBTECH Robotics and FAW-Volkswagen are expanding their collaboration to test humanoid robots in automotive manufacturing, including material handling and logistics applications. The agreement represents another step toward evaluating humanoids in real industrial environments.

But the bigger question remains: Can humanoid robots deliver the reliability, safety and economics needed to compete with established automation systems?

The next milestone isn't another factory demonstration. It's proving these robots can perform useful work consistently and economically.

Most advanced humanoids use massive digital neural networks (Nvidia chips, etc.) for control, but we still see robots dropping cups or crushing eggs due to latency. Given that biological reflex loops are analog and asynchronous, is digital brute force fundamentally limited here? How many companies are actually working on mixed-signal or neuromorphic hardware for hands vs. just scaling software?

This race for humanoid robotics is the precursor to automated labor right? And unitree is currently leading that space or no?

Hey everyone, curious to get your take on the future of robot hardware sales.

Right now, most physical bots still feel pretty gimmicky and limited, but software/embodied AI seems to be advancing crazy fast (look at what Unitree is putting out lately).

For anyone in hardware, deep tech, or automation: is jumping into commercial robotics sales right now getting in on the ground floor, or are we still years away from real enterprise adoption? Would love to hear your thoughts.

Hello hello!

I'm running into a couple of hardware challenges with our robot setups and wanted to see if anyone here has experienced similar issues or found reliable workarounds.

We are trying to add extra tools and sensing capabilities to the robot hands, but we are hitting a bottleneck with the internal data buses. There simply aren't enough internal cables available to transmit the additional data back to the controller.

Have you experienced the same problem or something similar? I was looking to add, in the future, a vision-based tactile sensor; it's just crazy the 4K image data of each finger, and I would really like to know what you have done to fix this problem.

Maybe we can all think of an alternative to just running some cables outside of the robot; we are afraid that they will get caught on something, and we really do not want to risk breaking it.