Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

“Say a robot drives into a wall. Why did it do that?” Nikolaus West explains why robotics teams need clearer visibility into sensor data, and how Rerun is helping push physical AI from impressive demos to real reliability. Link to the full episode with Lukas Biewald in comments.

Weights & Biases

48,129 subscribers

196,608 views • 8 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

LLMs had the internet. Robotics doesn’t, which is why we’re bringing the Scale AI data engine into the physical world. Robotics won’t be solved by blindly collecting 100x more demos. Raw trajectories need structure (intent, subgoals, failures, recoveries, edge cases, quality signals) or you just average mistakes. At Scale Labs, we’re building that data flywheel, mapping the right data to the right capabilities and scaling what works globally !

LLMs had the internet. Robotics doesn’t, which is why we’re bringing the Scale AI data engine into the physical world. Robotics won’t be solved by blindly collecting 100x more demos. Raw trajectories need structure (intent, subgoals, failures, recoveries, edge cases, quality signals) or you just average mistakes. At Scale Labs, we’re building that data flywheel, mapping the right data to the right capabilities and scaling what works globally !

jade

15,262 views • 3 months ago

As robotics teams scale, collecting real-world data can quickly become slow and expensive. 📉 Lightwheel is changing that with a simulation-first platform built on our technology, helping customers generate a 100:1 simulated-to-real data ratio for training autonomous systems. Together, we’re turning synthetic and real data into more capable, more reliable robots. Read the full success story 🔗

As robotics teams scale, collecting real-world data can quickly become slow and expensive. 📉 Lightwheel is changing that with a simulation-first platform built on our technology, helping customers generate a 100:1 simulated-to-real data ratio for training autonomous systems. Together, we’re turning synthetic and real data into more capable, more reliable robots. Read the full success story 🔗

NVIDIA Robotics

10,840 views • 4 months ago

Most AI is built to replace humans. We're building AI to understand them. Introducing Atypica's Subjective World Model. A foundation for understanding how people express themselves, tell their stories, make decisions, and behave under real-world constraints. Not just what users say — but why they choose, hesitate, and act. It powers AI Research, AI Personas, AI Panels, and AI Interviews, helping teams turn user research from a quarterly project into a permanent capability. Watch the video to see how it works and what teams are already building with Atypica. Start to explore this AI-native consumer research platform from here:

Most AI is built to replace humans. We're building AI to understand them. Introducing Atypica's Subjective World Model. A foundation for understanding how people express themselves, tell their stories, make decisions, and behave under real-world constraints. Not just what users say — but why they choose, hesitate, and act. It powers AI Research, AI Personas, AI Panels, and AI Interviews, helping teams turn user research from a quarterly project into a permanent capability. Watch the video to see how it works and what teams are already building with Atypica. Start to explore this AI-native consumer research platform from here:

atypica.AI

16,388 views • 14 days ago

From the latest episode with Ivan Poupyrev from Archetype AI The idea is simple but revolutionary: -- using sensor data, similar to how humans observe the physical world through our biological sensors -- to let machines 'talk' to the physical world. In the case of packages, this means not just knowing where it is, but what's happening to it in real-time.

From the latest episode with Ivan Poupyrev from Archetype AI The idea is simple but revolutionary: -- using sensor data, similar to how humans observe the physical world through our biological sensors -- to let machines 'talk' to the physical world. In the case of packages, this means not just knowing where it is, but what's happening to it in real-time.

This Week in Startups

131,725 views • 2 years ago

AI is shaking up every industry from Earth to space and beyond. During the AI in Space panel, Mark Mozena from @Planet dives into the ways AI is helping teams make better decisions and understand data. Catch the full replay.

AI is shaking up every industry from Earth to space and beyond. During the AI in Space panel, Mark Mozena from @Planet dives into the ways AI is helping teams make better decisions and understand data. Catch the full replay.

HPE

19,080 views • 5 months ago

.Ilya Sutskever on why OpenAI gave up on robotics: "Progress comes from the combination of compute and data. There was no path to data from robotics. I'd say that now it is possible to create a path forward, but one needs to really commit. You could imagine a gradual path of improvement where you build more robots, they do more things, you collect more data and so on. But you need to really love robots and need to be really willing to solve all the physical and logistical problems of dealing with them. It's not the same as software at all."

.Ilya Sutskever on why OpenAI gave up on robotics: "Progress comes from the combination of compute and data. There was no path to data from robotics. I'd say that now it is possible to create a path forward, but one needs to really commit. You could imagine a gradual path of improvement where you build more robots, they do more things, you collect more data and so on. But you need to really love robots and need to be really willing to solve all the physical and logistical problems of dealing with them. It's not the same as software at all."

Dwarkesh Patel

325,857 views • 2 years ago

💡Instead of moving data to AI, why not bring AI to your data? Feifei Li, SVP and President of International Business at Alibaba Cloud Intelligence Group, explains how embedding LLMs into databases, and processing and managing multimodal data with platforms, is transforming how enterprises build intelligent apps. It’s a smarter, faster, and more scalable way to unlock real-time insights.

💡Instead of moving data to AI, why not bring AI to your data? Feifei Li, SVP and President of International Business at Alibaba Cloud Intelligence Group, explains how embedding LLMs into databases, and processing and managing multimodal data with platforms, is transforming how enterprises build intelligent apps. It’s a smarter, faster, and more scalable way to unlock real-time insights.

Alibaba Group

84,685 views • 8 months ago

In a new interview, our CEO Gabriel Hattar shares how daGama is evolving from a discovery app into real-world digital infrastructure, and why Proof of Presence is one of the most important steps in our history. "This is not just another feature. This phase is about transforming the daGama from a platform that shows trust into a platform that can prove trust in the physical world." – Gabriel Hattar Watch the full interview to understand what we’re building, why it matters, and how you’re part of it 👇

In a new interview, our CEO Gabriel Hattar shares how daGama is evolving from a discovery app into real-world digital infrastructure, and why Proof of Presence is one of the most important steps in our history. "This is not just another feature. This phase is about transforming the daGama from a platform that shows trust into a platform that can prove trust in the physical world." – Gabriel Hattar Watch the full interview to understand what we’re building, why it matters, and how you’re part of it 👇

daGama

22,218 views • 7 months ago

Push a human off balance and they react instantly. Now try building that into a robot… During a recent visit to Hangzhou, hosted by the Zhejiang Provincial Government, we saw a live balance test from Unitree robotics G1 robot. This is where control, perception, and hardware have to move as one in real time.

Push a human off balance and they react instantly. Now try building that into a robot… During a recent visit to Hangzhou, hosted by the Zhejiang Provincial Government, we saw a live balance test from Unitree robotics G1 robot. This is where control, perception, and hardware have to move as one in real time.

Megatron

43,247 views • 2 months ago

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Jensen Huang says agentic AI is not a chatbot. It is a "digital robot" -- and the next bottleneck is not the model. It is first-person data. "Agentic AI is just a digital robot. It understands, it reasons, it plans, and it acts and uses tools." Then he makes the bigger jump: "The world's video data is from a third person, not first person. And so agentic systems, robotic systems, physical AI -- the data is the hardest problem." That is the hidden constraint in robotics. LLMs got the internet. Physical AI needs demonstrations, synthetic worlds, reinforcement learning, and first-person experience. This is why Jensen keeps calling it a stack: robots are not waiting for one magical model. They need the data engine, the simulator, the reward system, the runtime, and the computer. The next wave of AI is not just software that talks. It is software that sees, plans, acts, and eventually moves through the world.

Karl Mehta

14,140 views • 1 month ago

Very cool breakthrough by our physical AI and robotics teams-- Vulcan is the first robot that combines sight and touch, and can feel its way through cluttered spaces the way humans do. Vulcan is helping make work safer by handling ergonomically challenging tasks, while creating opportunities for our teammates to grow their skills in robotics maintenance.

Very cool breakthrough by our physical AI and robotics teams-- Vulcan is the first robot that combines sight and touch, and can feel its way through cluttered spaces the way humans do. Vulcan is helping make work safer by handling ergonomically challenging tasks, while creating opportunities for our teammates to grow their skills in robotics maintenance.

Andy Jassy

143,985 views • 1 year ago

Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, Physical Intelligence cofounder Chelsea Finn explains what it takes to build general-purpose robots that work in the real world. She shares how reinforcement learning pushed robot throughput up 2x, how their systems can run autonomously for hours, and why she believes robotics is entering its GPT era: moving from specialized models toward general-purpose systems that can work across tasks, robots, and environments. 00:00 — The State of Physical Intelligence 01:23 — What It Takes to Make Robots Useful 05:11 — The Reliability Problem 07:43 — Reinforcement Learning for Robotics 09:35 — Learning From Failures 12:43 — Training Robots to Improve Themselves 14:21 — Can a Robot Work for 13 Hours Straight? 17:36 — Why Robots Need Memory 21:22 — Building a General-Purpose Robot 25:02 — From Fine-Tuning to Out-of-the-Box Models 27:35 — Training on All the Data 30:20 — One Model That Beats the Specialists 31:21 — Compositional Generalization 37:49 — The GPT Era of Robotics 39:49 — Q&A

Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, Physical Intelligence cofounder Chelsea Finn explains what it takes to build general-purpose robots that work in the real world. She shares how reinforcement learning pushed robot throughput up 2x, how their systems can run autonomously for hours, and why she believes robotics is entering its GPT era: moving from specialized models toward general-purpose systems that can work across tasks, robots, and environments. 00:00 — The State of Physical Intelligence 01:23 — What It Takes to Make Robots Useful 05:11 — The Reliability Problem 07:43 — Reinforcement Learning for Robotics 09:35 — Learning From Failures 12:43 — Training Robots to Improve Themselves 14:21 — Can a Robot Work for 13 Hours Straight? 17:36 — Why Robots Need Memory 21:22 — Building a General-Purpose Robot 25:02 — From Fine-Tuning to Out-of-the-Box Models 27:35 — Training on All the Data 30:20 — One Model That Beats the Specialists 31:21 — Compositional Generalization 37:49 — The GPT Era of Robotics 39:49 — Q&A

Y Combinator

31,950 views • 1 day ago

‼️WHAT JUST HAPPENED?! AI HAS A TRADES PROBLEM The companies building the future do not just need chips, code, and capital. They need electricians, welders, HVAC techs, and plumbers. In this episode, Patrick Ney explains how fifty years of steering young people away from the trades created a shortage that is now slowing everything from data centers to energy infrastructure. 0:40 - Why BlackRock just bet on the skilled trades 4:55 - How the trades shortage was built over decades 4:45 - Why AI is suddenly running into a blue-collar problem 8:30 - AI still needs people to build the real world 10:56 - What the money looks like in the trades versus college 13:10 - Why this shortage is global, not just American 16:07 - The stigma that still pushes young people away from the trades

‼️WHAT JUST HAPPENED?! AI HAS A TRADES PROBLEM The companies building the future do not just need chips, code, and capital. They need electricians, welders, HVAC techs, and plumbers. In this episode, Patrick Ney explains how fifty years of steering young people away from the trades created a shortage that is now slowing everything from data centers to energy infrastructure. 0:40 - Why BlackRock just bet on the skilled trades 4:55 - How the trades shortage was built over decades 4:45 - Why AI is suddenly running into a blue-collar problem 8:30 - AI still needs people to build the real world 10:56 - What the money looks like in the trades versus college 13:10 - Why this shortage is global, not just American 16:07 - The stigma that still pushes young people away from the trades

NewsForce

89,488 views • 4 months ago

Collecting robot data at scale is key to deploying working manipulation policies, and the team from Tutor Intelligence Tutor is here to tell us about how to accomplish it. Their new announcement: a massive, 100-robot “data factory,” with a behind-the-scenes look at how to build a teleoperation platform and how to make robots and policies that are useful for their customers. Tutor Intelligence is a full-stack robotics company: they build robot arms, they sell robot arms, they write the software and they train neural networks. Josh Gruenstein, Jesse Michel, Shiraz, and Joe McCalmon, and Joe McCalmon join us to tell us more about how they scale both teleop data and human interventions from their teleoperators in order to train the policies they need. Watch Episode #85 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

Collecting robot data at scale is key to deploying working manipulation policies, and the team from Tutor Intelligence Tutor is here to tell us about how to accomplish it. Their new announcement: a massive, 100-robot “data factory,” with a behind-the-scenes look at how to build a teleoperation platform and how to make robots and policies that are useful for their customers. Tutor Intelligence is a full-stack robotics company: they build robot arms, they sell robot arms, they write the software and they train neural networks. Josh Gruenstein, Jesse Michel, Shiraz, and Joe McCalmon, and Joe McCalmon join us to tell us more about how they scale both teleop data and human interventions from their teleoperators in order to train the policies they need. Watch Episode #85 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

RoboPapers

36,041 views • 2 months ago

🎤 Overheard at #CES2026: Physical AI is moving intelligence from the cloud into the real world, and AMD is powering that shift with a full portfolio that enables robots to sense, decide, and act in real time across industries like healthcare, agriculture, and manufacturing.

🎤 Overheard at #CES2026: Physical AI is moving intelligence from the cloud into the real world, and AMD is powering that shift with a full portfolio that enables robots to sense, decide, and act in real time across industries like healthcare, agriculture, and manufacturing.

AMD

19,488 views • 6 months ago

🆕 Claude Cowork, Skills, and the Future of AI Coworkers Felix Rieseberg has spent years working at the interface layer, from Electron and the Slack desktop app to now helping build Claude Cowork. In this episode, Felix explains why execution is getting so cheap that teams can “build all the candidates,” why Anthropic is betting on local-first agent workflows, and why the future of AI products may belong less to chatbots and more to systems that can actually do knowledge work.

🆕 Claude Cowork, Skills, and the Future of AI Coworkers Felix Rieseberg has spent years working at the interface layer, from Electron and the Slack desktop app to now helping build Claude Cowork. In this episode, Felix explains why execution is getting so cheap that teams can “build all the candidates,” why Anthropic is betting on local-first agent workflows, and why the future of AI products may belong less to chatbots and more to systems that can actually do knowledge work.

Latent.Space

66,874 views • 4 months ago