Applied Intuition is the physical AI company bringing intelligence to every moving machine on the planet.
Sunnyvale, CA
Joined August 2017
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Applied Intuition retweeted
Reinforcement Learning (RL) in simulation is a scalable strategy to learn dexterous skills, but naive exploration from scratch often fails to discover manipulation strategies for diverse objects.
At @AppliedInt, we propose X-Reset, a straightforward method to resolve the exploration problem for generalist RL policies by randomly sampling resets from human hand-object states.
- We train a single dexterous policy to manipulate a variety of objects to any goal pose, ranging from small markers to heavy powerdrills with zero teleoperation data
- The learned policy zero-shot generalizes well beyond the training set (DexYCB), and transfers from sim-to-real using object pose-tracking models
We are continuously expanding robotics research at @AppliedInt beyond X-Reset, with the bounds of simulation and synthetic data for dexterous manipulation pushed. Stay tuned and join us!
🌐 Project Page: xreset-applied.github.io
📄 Paper: arxiv.org/abs/2609.35715
Dexterous manipulation in simulation has an exploration problem.
Here is how our team is fixing this problem.
Applied Intuition retweeted
“I don’t believe you can be very successful and not read a lot.”
4 books @qasar recommends, the ones that helped him build @AppliedInt, a $15B physical AI company:
📍I Am That, Sri Nisargadatta Maharaj
📍Sam Walton: Made in America, Sam Walton (with John Huey)
📍The Story of My Experiments with Truth, Mahatma Gandhi
📍Long Walk to Freedom, Nelson Mandela
You join. You ship. You find what’s broken and fix it.
That’s the bar.
review.firstround.com/firsth…
Come build alongside some of the best engineers in the industry, solving the hardest problems in physical AI.
appliedintuition.com/careers…
Every startup says they have the best team. We looked at the data.
@paraformtalent’s Talent Density Index ranks which companies attract, develop and retain the people the market values most.
The latest edition is live.
New Top 50: paraform.com/talent-density-…
Applied Intuition retweeted
Not 1 but 2 mega announcements to share!
#1: We’re partnering with Honda to build the software platform powering their next-generation of AI-defined vehicles.
applied.co/honda
Applied Intuition retweeted
AI開発の米アプライド、SDVでホンダ・日産と協業 ソフト開発を支援
nikkei.com/article/DGXZQOUC0…
Silicon Valley software expertise x Japanese engineering excellence.
Applied Intuition and @HondaJP are partnering to speed up Honda’s in-house development of a centralized, upgradeable SDV platform.
Learn more about how we are working together to transform the automotive industry.
appliedintuition.com/blog/ho…
AI-defined vehicles require transforming more than the software in the car, they require transforming how that software gets built.
That’s why we’re excited to partner with @Nissan to support their next-gen SDV and AI-defined vehicle efforts, built on our Vehicle OS.
This is how AI-defined vehicles get built from here.
appliedintuition.com/blog/ni…
Our team is headed to @Stanford this Wednesday, October 7th, for the Stanford Computer Forum Career Fair!
Drop by McCaw Hall at the Arrillaga Alumni Center between 11am and 4pm to meet our team and learn about how our team is solving the most complex challenges in physical AI.
appliedintuition.com/careers…
Applied Intuition retweeted
How can we exploit the large-scale in-the-wild dashcam data towards a driving foundation model? Our CVPR 2026 paper shows the power of such “free-gift” unposed and unlabeled data, learning driving representation with strong performance but significantly less labeled data!
Training AVs is complex and expensive to scale in new cities and terrains. From calibrated multi-sensor rigs to millions of labeled miles and painstaking annotation, we need to remove the blockers to autonomy development. Meanwhile, there are millions of hours of unlabeled dashcam footage.
And that’s where LFG comes in with a solution to exploit all this data.
🧵
On NAVSIM, LFG with only 10% of labeled data (81.4 PDMS) already matches other pretrained encoders trained on the full dataset (PPGeo 74.6 PDMS, DINOv3 81.4 PDMS, Pi3 82.8 PDMS). With the full training set LFG reaches 85.2 PDMS, beating multi-camera and lidar systems like UniAD (83.4 PDMS) and Hydra-MDP (84.7 PDMS), using a single front camera.
arxiv.org/abs/2602.22091
The result:
→ On semantic segmentation, LFG surpasses its own teacher on future frames (0.751 vs 0.680 mIoU), despite predicting blind while the teacher saw the real images.
→ Depth predictions on future frames closely match the teacher (0.31 vs 0.26 AbsRel on KITTI-360, 0.22 vs 0.19 on Waymo Open Dataset).
→ It correctly separates moving objects from static background even when the automated labeling pipeline gets it wrong.
Architecturally, the model consists of a frozen image encoder backbone paired with trainable causal autoregressive transformers. To reconstruct scenes and generate future predictions, these transformer layers are trained under the guidance of a suite of teacher models for depth, poses, semantic and motion.
Training AVs is complex and expensive to scale in new cities and terrains. From calibrated multi-sensor rigs to millions of labeled miles and painstaking annotation, we need to remove the blockers to autonomy development. Meanwhile, there are millions of hours of unlabeled dashcam footage.
And that’s where LFG comes in with a solution to exploit all this data.
🧵
When you're building a top tier team aimed at solving the most complex problems in physical AI, mediocrity is not an option.
Over the years, @malharhar has interviewed and hired many across Applied. As a high growth company, one of the biggest challenges has been maintaining and evolving the culture at scale.
review.firstround.com/firsth…
Our work with @dstlmod is shaping how the @BritishArmy can effectively deploy swarms of autonomous systems to deliver operational effect.
We led the Disrupter Group, bringing together Rowden, Evolve Dynamics and SAIF Autonomy to deliver this capability Test Bed in just a matter of months.
Learn more about our work: appliedintuition.com/defense…
Working with the @BritishArmy @Bluebearsystems and @AppliedInt we’ve successfully experimented with a collective control drone swarm in a realistic military environment
🔗 gov.uk/government/news/dstl-…
#Dstl #Autonomy #AI
Hundreds of drones, built by different vendors, tasked with one mission. Before they deploy, someone has to check the pieces work together, and today that usually happens at a live event, often the first time these systems actually talk to each other.
Applied Intuition's Digital Proving Grounds fix that — an always-on, vendor-neutral sim environment spanning the full sim-to-live continuum:
🔷 SIL: software tested continuously in a shared DevOps environment
🔷 HIL: real hardware verified against simulated environments
🔷 LVC: live platforms stress-test C2 at scale, no full fleet required
🔷 Live exercises: validate readiness, don't discover gaps
Applied Intuition partnered with the @DoWCTO on Virtual Readiness and Experimentation, integrating air, surface, and subsurface vehicles from different vendors on one mission, compressing the find-fix-fly loop from weeks to under an hour. Now scaling across the @CDAODoW's Autonomy Factory.
appliedintuition.com/blog/di…
