Deepak Pathak

797 posts

Deepak Pathak

Deepak Pathak

@pathak2206

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

Pittsburgh, PA Katılım Mayıs 2013
403 Takip Edilen27K Takipçiler
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Deepak Pathak
Deepak Pathak@pathak2206·
At @SkildAI, we’ve raised $1.4B, bringing our valuation to over $14B. We’re on a generational mission, and I’m grateful to be working alongside an exceptional team. Thanks to our investors for the long-term conviction towards omni-bodied intelligence 🚀 bloomberg.com/news/articles/…
Skild AI@SkildAI

Announcing Series C We’ve raised $1.4B, valuing the company at over $14B With this capital, we will accelerate our mission to build omni-bodied intelligence 🚀 skild.ai/blogs/series-c

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Lightspeed
Lightspeed@lightspeedvp·
Big news from @pathak2206 and the @SkildAI team. This is the kind of development that could shape the entire arc of physical AI.
Deepak Pathak@pathak2206

We’re helping define the factories of the future - Reindustrial Revolution! Really excited about this partnership with @ABBRobotics, @Universal_Robot, and @NVIDIARobotics. It is a big deal for us for two reasons: 1. AI-first reinvention of automation Unlike hand-engineered classical automation: - We deploy the Skild Brain (fully end-to-end neural network), fine-tuned with minimal robot data, and at times, none at all (more on this soon) - The system is inherently robust to real-world disturbances and can be set up for entirely new tasks in a fraction of the time. - In-context memory enables true long-horizon execution. No brittle, step-by-step programming required. - No custom tuning, no big metallic cages, no fancy sensors, just off-the-shelf robots and cameras. Why doesn’t classical automation scale? - Traditional systems rely on custom hardware and painstaking manual engineering, often costing multiples of the robot itself. Even then, they are fragile, and failures emerge if the environment shifts by as little as 0.1 mm. In dynamic & mixed assembly lines with humans and robots, that level of precision is unrealistic. - In mixed production lines, disturbances are inevitable and prohibitively expensive to eliminate. Classical approaches break under this variability unless you over-engineer the environment and spend lots of money (design for automation - DFA, rigid enclosures, heavy sensorization) to make sure everything is 0.1mm level precise, which defeats the point if the setup is going to change quickly. E.g., NVIDIA changes GPU designs every 6months! Long-term vision Imagine a world where anyone with no expertise in controls (a factory worker, a technician, a small business owner) can automate complex factory stations in days. No specialized infrastructure. Just intelligence that adapts. This is more than incremental progress. This is going to revolutionize traditional automation, what we call, reindustrial revolution where automation becomes accessible, flexible, and universal! 2. Data flywheel for Physical AI Industries are the backbone of society and an ideal proving ground for early AI deployments. They offer just enough structure to operate, and real, immediate demand to drive impact. Every deployment across tasks and sectors generates data that improves the omni-bodied Skild Brain, thus fueling a compounding loop where each improvement unlocks the next frontier. This is how we aim to build the largest data flywheel for physical AI. Industrial deployment is only the beginning. It opens the door to semi-structured environments (hospitals, hotels, grocery stores, etc.) where complexity rises, and capability deepens. From there, we move into fully unstructured consumer settings (homes). Step by step, this progression lays the foundation for something much bigger: the emergence of true general autonomy.

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Deepak Pathak
Deepak Pathak@pathak2206·
We’re helping define the factories of the future - Reindustrial Revolution! Really excited about this partnership with @ABBRobotics, @Universal_Robot, and @NVIDIARobotics. It is a big deal for us for two reasons: 1. AI-first reinvention of automation Unlike hand-engineered classical automation: - We deploy the Skild Brain (fully end-to-end neural network), fine-tuned with minimal robot data, and at times, none at all (more on this soon) - The system is inherently robust to real-world disturbances and can be set up for entirely new tasks in a fraction of the time. - In-context memory enables true long-horizon execution. No brittle, step-by-step programming required. - No custom tuning, no big metallic cages, no fancy sensors, just off-the-shelf robots and cameras. Why doesn’t classical automation scale? - Traditional systems rely on custom hardware and painstaking manual engineering, often costing multiples of the robot itself. Even then, they are fragile, and failures emerge if the environment shifts by as little as 0.1 mm. In dynamic & mixed assembly lines with humans and robots, that level of precision is unrealistic. - In mixed production lines, disturbances are inevitable and prohibitively expensive to eliminate. Classical approaches break under this variability unless you over-engineer the environment and spend lots of money (design for automation - DFA, rigid enclosures, heavy sensorization) to make sure everything is 0.1mm level precise, which defeats the point if the setup is going to change quickly. E.g., NVIDIA changes GPU designs every 6months! Long-term vision Imagine a world where anyone with no expertise in controls (a factory worker, a technician, a small business owner) can automate complex factory stations in days. No specialized infrastructure. Just intelligence that adapts. This is more than incremental progress. This is going to revolutionize traditional automation, what we call, reindustrial revolution where automation becomes accessible, flexible, and universal! 2. Data flywheel for Physical AI Industries are the backbone of society and an ideal proving ground for early AI deployments. They offer just enough structure to operate, and real, immediate demand to drive impact. Every deployment across tasks and sectors generates data that improves the omni-bodied Skild Brain, thus fueling a compounding loop where each improvement unlocks the next frontier. This is how we aim to build the largest data flywheel for physical AI. Industrial deployment is only the beginning. It opens the door to semi-structured environments (hospitals, hotels, grocery stores, etc.) where complexity rises, and capability deepens. From there, we move into fully unstructured consumer settings (homes). Step by step, this progression lays the foundation for something much bigger: the emergence of true general autonomy.
Skild AI@SkildAI

Robotics is a data problem. Today, we’re partnering with @ABBRobotics, @Universal_Robot, and @NVIDIARobotics to deploy the Skild Brain across real-world industries from manufacturing to factory lines. This will help us build the world’s biggest data flywheel for physical AI.

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Deepak Pathak
Deepak Pathak@pathak2206·
Yes precisely. Most of such sectors that can be automated via custom programming are already doing so. For complex and “fast changing” setups, hardware customized / hardcoded automation offers no economic value even for big players. Margins don’t work. Plus, one can’t automate deformable stuff with classical automation so they introduce human stations in between which adds more randomness. AI helps solve both the problems in one go and hence is critical to enable automation for everyone from large players to even SMBs.
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Abhinav Das
Abhinav Das@Abhindas1·
Super impressive demo, especially the robustness to setup changes. That said, in high-throughput manufacturing, a lot of this is typically handled with fixturing + constrained environments trading flexibility for cycle time and repeatability. And every second of delay compounds across the supply chain. Feels like the real unlock is where flexibility actually wins despite that, not just replaces classical automation
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Chris Paxton
Chris Paxton@chris_j_paxton·
This is the first time that I have seen Skild doing live demos in public! Demonstrating neural nets for precision manufacturing
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Deepak Pathak
Deepak Pathak@pathak2206·
Hey @BradPorter_ @willknight ! Nice to hear from you. Here is what's going on and a few reasons why it is a BIG deal for the future of industrialization. Unlike classical automation, this is: - Fully end-to-end neural network (Skild Brain) finetuned with a small amount of robot data (or even zero real-world data) - Robust to disturbances and extremely fast to set up for any new task - Long-horizon task with in-context memory. No step-by-step programming - No custom tuning, no fancy sensors, just vanilla cameras Why not classical automation: - Current automation stations require extensive hand engineering and are extremely costly (typically 3x or more than the robot itself). Unfortunately, if the task needs ultra precision, they don't transfer if the setup changes by even 0.1mm, which is very difficult to ensure in a mix of human-operated stations with automation. - If you look at the live demo for 5-10 minutes, and as we swap racks, you will see the robot needs to adjust the drill or its actions several times due to disturbances in setup. Hence, classical methods fail, so you need a learning approach. Unless you spend a shit ton of money (design for automation DFA, robot in an enclosed cell, heavy sensorization, etc.) to make sure everything is 0.1mm level precise, which beats the point if the setup is going to change quickly. E.g., NVIDIA changes GPU designs every 6months! - Oh, and this, this is not a made-up task -- it is actually done by humans in Foxconn factory: reuters.com/business/media… Vision: Imagine anyone with no expertise in controls (factory worker, handyman, etc) being able to automate arbitrarily complex factory stations in a couple of days on their own with no fancy setup -- this is going to revolutionize traditional automation!
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Brad Porter
Brad Porter@BradPorter_·
@chris_j_paxton @servo_boyd I’m not quite sure I’m following what they’re showing. Precise motions for manufacturing is exactly what these robot arms were designed for. Our cars are assembled and welded this way.
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Lukas Ziegler
Lukas Ziegler@lukas_m_ziegler·
Ok so @SkildAI just taught a robot an industrial task in 10-15 hours That’s awesome! GTC keeps surprising ;)
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Deepak Pathak
Deepak Pathak@pathak2206·
@macjshiggins lol... he is clearly texting someone! But great observation skills 😜
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CMU Robotics Institute
CMU Robotics Institute@CMU_Robotics·
Congrats to Associate Professor Deepak Pathak (@pathak2206) for receiving a 2026 Young Investigator Award from @USNavyResearch! Pathak's project addresses how to make AI systems scale beyond controlled settings in the real world to solve complex tasks. bit.ly/40gL0CB
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Pratham Jain
Pratham Jain@PrathamJainAI·
@pathak2206 @SkildAI Hi , I’m looking an internship at skild AI from August 2026 for a year and have been working on vla and I was able to teach robots to fold clothes in my apartment . But I want to go and deep dive into physical ai . I’m based out of Banglore . Would love to work at skild AI
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NVIDIA Robotics
NVIDIA Robotics@NVIDIARobotics·
.@SkildAI Co-founder & CEO @pathak2206 breaks down the shift from hardware-first robotics to a "general-purpose brain" capable of powering everything from humanoids to industrial arms with a single AI model. 🤖 Discover more robotics and AI breakthroughs shared at NVIDIA Live: Setting the Stage ➡️ nvda.ws/49Yf1Nc
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Yuda Song
Yuda Song@yus167·
RL on LLMs inefficiently uses one scalar per rollout. But users regularly give much richer feedback: "make it formal," "step 3 is wrong." Can we train LLMs on this human-AI interaction? We introduce RL from Text Feedback, with 1) Self-Distillation; 2) Feedback Modeling (1/n) 🧵
Yuda Song tweet media
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Rootti Labbe
Rootti Labbe@promptChatGpt·
This contradicts @ylecun's claims about great LLMs' limitations and #robots' prowess he said, is just a matter of planned actions
Deepak Pathak@pathak2206

At @SkildAI, we’ve raised $1.4B, bringing our valuation to over $14B. We’re on a generational mission, and I’m grateful to be working alongside an exceptional team. Thanks to our investors for the long-term conviction towards omni-bodied intelligence 🚀 bloomberg.com/news/articles/…

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Deepak Pathak
Deepak Pathak@pathak2206·
At @SkildAI, we’ve raised $1.4B, bringing our valuation to over $14B. We’re on a generational mission, and I’m grateful to be working alongside an exceptional team. Thanks to our investors for the long-term conviction towards omni-bodied intelligence 🚀 bloomberg.com/news/articles/…
Skild AI@SkildAI

Announcing Series C We’ve raised $1.4B, valuing the company at over $14B With this capital, we will accelerate our mission to build omni-bodied intelligence 🚀 skild.ai/blogs/series-c

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Skild AI
Skild AI@SkildAI·
Ever lost your AirPods? Here, a different embodiment assembles 10+ AirPods cases in a row, requiring extreme precision and dexterity.
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