Hebbian Robotics (YC S26)

Developing robots that extend what humanity can do, for the benefit of the communities they serve.

[Brandon Ong @bdono_]

We’re joining the S26 batch at @ycombinator!

@kstonekuan and I started @hbr_pbc to develop humanlike robots that work alongside expert field teams to build, maintain, and repair the future infrastructure humanity will increasingly depend on.

YC will help us accelerate toward achieving that mission. Thanks @sdianahu for taking a chance on us.

There is still much to build, learn, and prove, but we’re excited for what’s next.

We’re grateful to be working with partners who share our mission to develop robots that extend what humanity can do, for the benefit of the communities they serve.

[kingston kuan @kstonekuan]

Hebbian Robotics has joined @ycombinator's S26 batch!

@bdono_ and I started @hbr_pbc because we believe the next generation of infrastructure will be built and maintained in places that are increasingly difficult for people to access safely and consistently: remote sites, subsea environments, orbit, and other demanding field conditions.

Our goal is to build human-like robots that can work alongside expert field teams to help construct, inspect, maintain, and repair the systems that communities and industries rely on.

YC will help us accelerate as we continue building toward that mission. Thanks @sdianahu for taking a chance on us!

We are grateful to the early believers who supported us when there was not much to show beyond conviction, early prototypes, and a lot of work ahead. Your trust, feedback, and encouragement have meant a lot.

There is still plenty to build, learn, and prove, but we are excited for what is ahead. We are grateful to be working with partners who share our mission in developing robotics to extend what humanity can do for the benefit of the communities they serve.

[kingston kuan @kstonekuan]

We’re open sourcing the agent skills we built to plan this demo.

Datacenter robotics tasks are full of hidden constraints: rack geometry, cable density, live equipment, etc.

These skills are an attempt to make those constraints explicit so AI agents can reason about them more reliably when producing robot task plans.

[Brandon Ong @bdono_]

hey @sama, we'd love to walk the floors of OpenAI's GW data centers and see if our robots can address the bottlenecks behind compute delays.

just emailed you more details on what we're working on at @hebbyrobotics for YC S26

[Brandon Ong @bdono_]

The scarce thing in a data center is not manpower, but instinct that only comes from years on the floor.

@kstonekuan and I spent the past month with data center operators and industrial robotics startups. Most robotics companies are focused on robots as a productivity amplifiers: 24/7 uptime, five days of work done in two. Few are focused on the potential of robots to change how people work altogether.

We want to show what it looks like to rethink human-robot collaboration, using AI so a shrinking pool of experts can meet the increasing demands of future infrastructure.

[kingston kuan @kstonekuan]

Seeing a fair bit of interest in the Newton RJ45 example so wanted to share another video, directly from the @NVIDIARobotics blog post.

"Video 5. A RB-Y1 robot is performing a cable insertion task for refrigerator assembly, simulated with two-way coupled MuJoCo Warp and a VBD cable solver"

What's interesting here is the use of two coupled solvers for one task. This kind of solver composition is a first-class capability in a lower level framework like Newton.

[kingston kuan @kstonekuan]

NVIDIA showcased Newton at GTC again earlier this year.

If you are familiar with MuJoCo or Isaac Sim, Newton is a new open-source, GPU-accelerated physics engine aimed at robotics and contact-rich manipulation, built with DeepMind and Disney Research. It also runs as a physics backend inside the Isaac ecosystem.

I finally had time to dig through the examples, and the RJ45 plug simulation (example_contacts_rj45_plug.py) caught my attention. The latch deflects and clicks, and the cable behaves like a real 1D deformable. We've been working on cables, connectors, and other contact-heavy interactions, so seeing this as a first-class example was a pleasant surprise.

I extended it to test insertion and removal, with and without pressing the latch, and visualized the result in Rerun. Pull without pressing the tab, and the latch holds the plug in place; press it, and the plug comes free.

It also interested me how code-first the examples feel. We've spent real time trying to drive both MuJoCo and Isaac Sim with coding agents, and the recurring friction is being able to reason spatially to modify the simulation setup.

It feels like there's real potential here. I'm curious if Newton might be better suited for agent-generated robotics simulations.

If you're looking for something like this too, Newton is definitely worth a try.