satvik retweeted
We found a way to scale humanoid control that reduces GPU-hours by more than 1000×.
Across 129,514 motions, SuperMimic surpasses the state of the art (128 GPUs, 7 days) with 1 GPU in 3 hours.
Project: supermimic.github.io
Demo: supermimic.github.io/demo/
this is so cool, i bet gemini is better than all others in visual intelligence
In robotics, Gemini 4 Argon is speeding up real-world physical learning.
We let Argon watch a video of a human playing cornhole. It mapped the environment, calculated whole-body joint movements, and autonomously built the simulation code to practice.
When transferred to Apptronik’s Apollo humanoid, the robot sank seven straight shots in a real-world trial.
kaggle but for robotics. i love this!
The Microduck Arena is live!
Train your duck with RL, publish it on @huggingface, and race it in the 2 m Sprint, Hill Climb, Roller Sprint and MicroDuck Got a Talent. No robot required.
Join the live 👉 huggingface.co/spaces/pollen…
#Microduck #robotics
youtu.be/9Lnh0Bjr1ds
super bullish, any company would be lucky to have this guy
looking back, I’ve worked on sim2real RL, VLA training and deployment, VR teleoperation, human-in-the-loop recovery, online RL and building low-cost robot hardware for data collection.
I truly love robots and everything that goes into making them work in the real world.
If you’re looking for someone who’s high agency and work entire robotics stack, I’m on the market for internship from Jan.
satvik retweeted
We see entertainment as the best category to solve robotics while keeping the business sustainable and scalable.
We'd start shipping soon.
1) We already have one of the most advanced human-robot-interaction module.
2) generalist rl policies.
3) slam running on $10 chips with loop closures.
4) rapid hardware dev cycles for new characters.
The algorithms stay the same. It's just that we are solving robotics on the desk first.
satvik retweeted
today we’re launching eyecandy robotics!
we’re bringing animated characters to life through robotics and ai.
satvik retweeted
Your next robotics project could start with these steps.
Today, we're open-sourcing Asimov 1's locomotion policy and the training code behind it, giving developers a foundation to build on.
Alongside the trained policy checkpoint, we're sharing our Isaac Lab-based training code, including reward definitions, hardware-tuned actuator configurations, and domain randomization settings.
That foundation gives you a starting point for your own experiments. Adapt the training setup to hardware changes. Explore different walking styles. Build toward new capabilities.
We've taken the first steps.
Build what comes next.
Asimov 1 locomotion: github.com/menloresearch/isa…