Deepak Pathak

Co-Founder & CEO @SkildAI, Faculty @CarnegieMellon.
PhD @UCBerkeley; BTech @IITKanpur
I study topics in AI (robotics, machine learning & computer vision).

[Deepak Pathak @deepakpathak]

We hosted Prof. Alyosha Efros (UC Berkeley) at @SkildAI! He didn't believe that robots could actually cook eggs reliably. :)
Tested back-to-back 5times without fail! One batch of scrambled eggs every ~2.5mins nonstop. The same model assembles a GPU on a server rack too.

[Deepak Pathak @deepakpathak]

@ShuoYangAIR Great work @Shuo — love to see our ROA algorithm in action. This robot is so cool!

[Deepak Pathak @deepakpathak]

@cloneminchankim thanks Minchan! glad you enjoyed it

[Deepak Pathak @deepakpathak]

@hardmaru Congrats on the amazing release, David!

[Carnegie Mellon University @CarnegieMellon]

Deepak Pathak (@deepakpathak) received a 2026 Pattern Analysis and Machine Intelligence Young Researcher Award at @IEEEorg's Conference on Computer Vision and Pattern Recognition. 👏

A @CMU_Robotics professor, Pathak is also co-founder and CEO of @SkildAI. https://t.co/I8SfrjxbdY

[Deepak Pathak @deepakpathak]

@Luckyballa @SkildAI Welcome @Luckyballa !!

[Berkeley AI Research @berkeley_ai]

Congratulations to @berkeley_ai alumnus @deepakpathak who has been awarded the PAMI Young Researcher in Computer Vision Award!

This top award for young researchers in computer vision is given to two recipients yearly.

https://t.co/PuVbUpp6oo

[Deepak Pathak @deepakpathak]

@bilawalsidhu @CVPR Thanks @bilawalsidhu !! :)

[Deepak Pathak @deepakpathak]

I’m deeply grateful to receive the PAMI Young Researcher Award. This recognition is a reflection of the exceptional students, colleagues, collaborators, and mentors I’ve had the privilege to learn from and work alongside. 🙏😇

Thanks, TC PAMI and @CVPR, for this honor.

[Skild AI @SkildAI]

Exciting news! 🎉 Our CEO & Co-founder, Deepak Pathak (@deepakpathak), received the PAMI Young Researcher Award at #CVPR2026 this week.

Among the highest honors in computer vision for early-career researchers, the award recognizes groundbreaking contributions that have a lasting impact on the field of AI.

Congratulations, Deepak!

[Deepak Pathak @deepakpathak]

Force is arguably the most overlooked ingredient in modern robot learning.

Introducing FACTR 2: it turns *any* commodity robot into a force-aware system with no force sensors required.

Train a tiny force network in <1min with <10mins of data and drop it into any existing teleop pipelines:

✅ Free force sensing for both the robot and the operator arm
✅ Makes demos higher-quality → fewer of them needed.
✅ A new force-aware learning algorithm (FIRST) uses those recovered forces to figure out which parts of a demo actually matter, making learning data-efficient.
✅ Strong performance on complex tasks with fewer demos and even no pretraining!

More details below.

[Jason Liu @JasonJZLiu]

💥Introducing FACTR 2, learning external force sensing on commodity robot arms without needing dedicated sensors.

We show that learned force signals enable force-feedback teleop on low-cost arms and improve BC policies.

FACTR 2 consists of:
1. Neural External Torque (NEXT): learns external forces without needing dedicated force sensors.
2. Force-Informed Re-Sampling Training (FIRST): uses the learned force signal to identify task-critical regions and upsample them during training.

w/ @StevenOh_ @_tonytao_

🧵(1/N)

[Deepak Pathak @deepakpathak]

@Scobleizer @CVPR Thanks, Robert! :)

[Mihir Prabhudesai @mihirp98]

Update: This work has been accepted at ICML 🎉

We’ve also released our code, model weights, dataset and all resources here:
https://t.co/aKGlclhfBX

[Sandeep Routray @SandeepRoutra11]

🚀 Excited to share ViPRA: Video Prediction for Robot Actions

📍 Accepted to #ICLR2026 @iclr_conf
🏆 Best Paper — #NeurIPS2025 Embodied World Models Workshop

Robot learning today still needs millions of action labeled videos.

Yet videos are abundant — from humans and the web — but lack action labels. Meanwhile, pretrained video models already learn rich dynamics.

ViPRA is a recipe for turning pretrained video models into robot policies while enabling robot learning to scale with actionless videos.
🧵 Thread ↓

[NVIDIA Robotics @NVIDIARobotics]

What if one AI brain could run every robot on the planet—from a humanoid to a warehouse arm—all at once? 🧠

@deepakpathak, CEO and Co-Founder, and Abhinav Gupta, President and Co-Founder of @SkildAI, explain how they are building "OmniBrain," a universal foundation model designed to generalize intelligence across any robot form factor and task.

📺 Watch the episode: https://t.co/2Me04boyjK

[Deepak Pathak @deepakpathak]

@TheHumanoidHub Also transfers humanoids, too. See this video at 2:30 where the robot goes from walking to limping. It's fully emergent; it was never trained on any broken motors (that video is 2yrs old, so we put it at the end for fun).

https://t.co/8hN9ddYXRl

[Skild AI @SkildAI]

Modern AI is confined to the digital world.

At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead.

Our Mission: Artificial General Intelligence grounded in the physical world.
We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world.

Our Vision: Any robot, Any task, One brain.
We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task.

Who are we? A passionate group of scientists & engineers driven by our shared vision.
We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon.

Join us in our mission to build the robot brains of tomorrow.

[Deepak Pathak @deepakpathak]

Excited to share Sim2Reason -- training LLMs in simulation to learn Olympiad-level physics (mechanics)!

Today, LLMs learn science by reading what humans have already written, absorbing distilled knowledge from textbooks and the internet. But human-annotated physics data is fundamentally scarce, and that bottleneck isn't going away.

Analogy to robotics: Sim2Real transformed robotics, where we train in simulation and deploy zero-shot in the real world. We do not try to teach robots by describing physics to them, but they have to experience it.

Approach: Our Sim2Reason makes the same bet we made in robotics -- skip the descriptions, go straight to the source. Let models learn directly from simulated worlds, observing how objects move, collide, and interact, much like scientists build intuition through experiment.

Result: Models trained purely on simulated experience develop transferable physical reasoning skills, improving even on problems that were never simulated. Zero-shot gains on IPhO, IIT JEE Advanced, OlympiadBench — problems the model never saw during training.

[Mihir Prabhudesai @mihirp98]

What if AI learned physics the way Newton did – by experiencing it?

We built Sim2Reason: train LLMs inside virtual worlds governed by real physics laws, zero human annotation.

Result: +5–10% improvement on International Physics Olympiad, zero-shot. 🧵

[Lukas Ziegler @lukas_m_ziegler]

🚨 BREAKING:

Skild AI acquires Zebra Technologies robotics division! 🤯

@SkildAI acquired the robotics division of @ZebraTechnology (formerly Fetch Robotics) to deploy their omni-bodied brain across warehouses, unlocking massive productivity gains and accelerating their data flywheel.

Most warehouse robotics solutions use classical approaches for navigation and routing, but many parts remain human-bottlenecked like moving objects between receptacles.

Zebra Technologies brings one of the most battle-tested warehouse robotics platforms in the industry.

Their Symmetry Fulfillment orchestration platform already coordinates tasks between robots and frontline workers using real-time data from Zebra wearable devices, proven in logistics environments where reliability is mandatory.

The Skild Brain is an omni-bodied foundation model that generalizes across embodiments without retraining from zero. Quadrupeds, humanoids, tabletop arms, mobile manipulators—the same underlying model operates all of them.

Adding the Skild Brain to Zebra's Symmetry platform means robots don't just follow instructions, actually they make informed decisions. The Symmetry platform will expand beyond its current footprint into new verticals, new use cases, and a wider range of robot form factors.

This accelerates Skild's data flywheel, bringing in more diverse data to train the omni-bodied brain.

@deepakpathak 🔥

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[Deepak Pathak @deepakpathak]

More details

https://t.co/Q3Do2jHYTF

[Skild AI @SkildAI]

We have acquired Zebra Technologies’ robotics arm (formerly Fetch Robotics).

This is what happens when orchestration meets intelligence -- a major step toward fully autonomous warehouses.

More robots. More environments. One unified brain.

[Deepak Pathak @deepakpathak]

Excited to announce that @SkildAI has completed the acquisition of Zebra Technologies’ robotics arm (formerly Fetch Robotics).

By combining Zebra's human-robot orchestration platform with omnibodied Skild Brain, we plan to turn warehouses everywhere into hubs of hyper-efficiency.

Imagine a single platform, single brain optimizing every movement of robots as well as human workers in warehouses.

Many of us in the robotics community have used Fetch Robots in the past and have rooted for them over the years, so this acquisition is special for us in many ways.

[Deepak Pathak @deepakpathak]

Bloomberg article

https://t.co/hfec1xn32W

[Bloomberg @business]

Skild AI, a fast-rising startup that makes "brains" for robots, has bought the robotics automation division of Zebra Technologies https://t.co/S0UW6MbPnf

[The Humanoid Hub @TheHumanoidHub]

Skild Brain preparing an omelet with everyday human tools.

The robot drops an eggshell into the bowl at one point but recovers and continues the task.

The ability to self-correct during edge cases is what will make robots dependable for complex, long-horizon missions.