AI software for builders and developers. Understand the site before you commit.
Joined March 2026
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We’re all-in on this.
We're all-in on a new product. @HapticLabsAI
is now AI for construction and development, a land intelligence engine with an agent on every parcel.
Land diligence that used to take 3 months of legal, zoning, permits, earthworks, economics we now compress into 2 days.
Paying customer #1 is live with us. If diligence is still the bottleneck, come talk to us.
"Network jitter became a dominant failure mode" is becoming more relevant in today's edge-or-cloud debate in robotics. We believe it doesn't stay edge-or-cloud for long. Instead it's several models per robot: safety running on the edge, heavy reasoning in the cloud, or a hybrid depending on the environment your robot is operating in. Running both and switching between them live is one of the things we've built for at Haptic.
Kudos to @chichengcc @ArpitKalla and the @sundayrobotics team! Excited to watch them continue to build
We just hit a weird milestone: our model became more reliable than your average home WiFi.
Just like everybody else, we thought cloud inference was the obvious choice. Yet 2 days into the ACT-2 eval, our mind completely changed.
If our hero @ArpitKalla didn’t cook, this video wouldn’t exist 🧵
In March, we mapped the pain across 64 RoboPapers episodes ... this time, we mapped the surprises that most changed our thinking. Here are eight of the surprises after scanning those same episodes.
TL;DR: better systems around a model often beat better models.
hapticlabs.ai/blog/2026/07/1…
What problems and bottlenecks keep coming up in physical AI research?
We analyzed 64 episode transcripts of the brilliant @RoboPapers podcast to see which bottlenecks robotics researchers mention the most.
hapticlabs.ai/blog/2026/03/0…
hapticlabs.ai/blog/2026/07/1…
Credit for the source material to @chris_j_paxton and @micoolcho and their RoboPapers podcast. We just did the packaging!
Haptic AI retweeted
When running robot policies, we established that cloud had to be at least twice as fast as edge before we felt it worthwhile (including all the network, etc. latency). Where is that crossover for your product?
Haptic AI retweeted
MolmoAct2 (so101 ft) does well in some basic pick and place tasks and runs smoothly on @HapticLabsAI's inf. infra. A bit surprised that it's not been able to distinguish colors or shapes (e.g. doesn't distinguish between a yellow or a blue cube, or between a cube and a cylinder)
Robot training pipelines do not always fail loudly
We harvested 267 public datasets tagged 16-DOF OpenArm. Only 45 merged cleanly: 652 episodes and 583k frames
Read about how we mapped what broke and had an agent curate the corpus and fine-tune Pi0.5 ↓
hapticlabs.ai/blog/2026/07/1…
Three open robot models dropped in three days. @LeRobotHF 0.6.0 on Tuesday, with six benchmarks to evaluate a policy before it touches hardware. LingBot-VLA 2.0 the same day: a 6B open checkpoint trained on 60,000 hours of real robot data. @Nvidia GR00T 1.7 this morning, on a 2B backbone.
Where @HapticLabsAI steps in: we do everything after the download. Shrinking a policy to one robot's compute budget, serving it at control-loop rates, and managing a fleet where some inference runs on the robot and some in the cloud.
Robot policy deployment should feel more like shipping software.
Today it doesn't. A policy that works in a demo still has to run on the robot, hit latency targets, survive real networks, and be managed across a fleet.
That's what we've built at Haptic: the deployment and inference layer for robot policies.
Haptic is the deployment and inference layer for robot policies. We help robotics teams:
- optimize policies for edge
- serve inference from the cloud
- route between edge + cloud
- manage policies across robot fleets
Bring your model, robot, and target runtime. Haptic figures out where it should run: on-device, cloud, or hybrid.
We're onboarding robotics teams now. If you're deploying VLAs, robot foundation models, or custom policies onto real robots, secure access to our beta here: hapticlabs.ai/waitlist
This week @pham_blnh got a robot policy running inside a browser tab. It works because that policy (ACT) is about 80M parameters, small enough to live in a tab.
It's a great on-ramp. It also quietly shows the deployment problem we work on every day. The policies that run in robots are larger: generalist VLAs are around 50 times bigger and still have to hold 20Hz on an 8GB computer bolted to the arm.
you don’t need a robot to see physical ai in action, you just need a browser.
this week I finally tried something I've wanted to do for a while: design an SO-101 scene in three.js, connect it to a real leader arm, collect teleoperation data, train an ACT model, then port it to run in the browser.
the result is the first ever demo of ACT that runs locally on the web.
try it yourself now at lerobot.binhph.am
The 10TB to 2TB re-encode is genuinely nice engineering. Credit to LeRobot for the unglamorous part. A lot of the companies we have worked with have cited this as a big pain.
The largest open-source humanoid teleop dataset EVER just dropped on @LeRobotHF @huggingface
huggingface.co/datasets/BitR…
We re-encoded the full dataset into LeRobot format: ~10TB → ~2TB, no loss of fidelity. Same trajectories, a fraction of the footprint, far easier to stream and train on.
Explore the dataset in LeRobot Visualizer: live 3D render of the robot, synced camera feeds, subtask annotations and language instructions, all in your browser.
Most importantly: Unitree G1 is supported in LeRobot, meaning you can load HIW-500 and start training imitation / VLA policies on 20,000 real-home humanoid episodes on your G1.
Huge thanks to @BitRobotNetwork for sharing the data. Go build. 🤖
Haptic AI retweeted
“There’s no version of the world, I think, where in 5 years there are not good, dexterous, capable hands for a few thousand dollars price, ready for integration into different robots and products.
You should probably plan accordingly.”
This is another reason why I am bullish on costs dropping and Cambrian explosion of robotics startups.
Not surprising coming from @chris_j_paxton , but this is really a great primer and overview on the subject matter.
Haptic AI retweeted
I learned so much about physical AI from a short call with one of my favorite cracked builders @0xChrisM and now I’m hooked. 🔥
Will follow along as the @HapticLabsAI team builds Festivus, which makes it easy for anyone to understand how to give AI a physical body!
At #FestivusAI, we’re building a catalog for robotics.
Our first milestone was simple but important: gather scattered robotics data from #HuggingFace, #GitHub, vendor sites, tinkerer blogs, and all the odd little corners of the internet where useful knowledge tends to live.
We credit sources clearly. No mystery meat data.
Then we brought everything into one searchable platform, so people can actually find what they need without opening 47 tabs and questioning their life choices.
Now we’ve added an AI agent that makes pages easier to edit, improve, and keep current.
Think Wikipedia for #robotics, but faster to contribute to, easier to maintain, and built for the people actually doing the work.
And yes, our code and data are #opensource too.
Why? Because that’s how innovation leapfrogs. People build faster when the map is shared.
Haptic AI retweeted
IMO when it comes to physical AI, SV is a bit too frontier-pilled.
Don't get me wrong, frontier labs are VERY important to the ecosystem, but I think there are many problems in agriculture, construction, even consumer that can be solved with off-the-shelf hardware and don't need 99.999% success rates, and I suspect they are not as interesting to discuss so they are (accidentally) under-rated.
What we really need are entrepreneurs who UNDERSTAND the business case and pain point, and are willing to assemble the product, get costs down, and pound the pavement for distribution.
I meet many robotics companies that fit the bill, and I'd argue this is an underrated real bottleneck for robotics: people fighting the good fight.
Haptic AI retweeted
Got a chance to see an early preview of this before launch and @mexitlan and the @HapticLabsAI team has done an amazing job!
Great to see more people contributing to open source robotics like this. The ecosystem needs a lot more efforts in this direction.
Excited to see where this goes.
We bring the ML expertise, GPUs, and infra. We’ll help do the work. We’ll give you up to $5,000 in GPU credits. You keep all the code and models.
Robotics teams: if you’re trying to get models out of the lab and onto real robots, we want to work with you. We help teams doing hard deployment work: compression, fine-tuning, distillation/quantization, and evals on real edge silicon. We’ve helped teams move to cheaper Jetsons and cut latency in half.
All we ask for is blunt feedback. Send a DM, email hello@, or book via hapticlabs.ai/deploy if interested!