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.

Intelligent general purpose robots need "physical common sense" . #GeminiRobotics ER 2 is just one of many safety layers needed. Lots more work to do. Safety report: Asimov-agentic benchmark: A fun red-teaming exercises with Apollo: Google DeepMind #Robotics

Carolina Parada

3,377 subscribers

13,773 views • 12 days ago •via X (Twitter)

#GeminiRobotics

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Most robotics AI models suffer from the "stop-and-think" problem. They take a static picture, pause to reason, execute an action, and repeat. In the real world, that latency causes spills, collisions, and failed tasks. Google DeepMind just launched Gemini Robotics ER 2: an embodied reasoning model that thinks and acts at the speed of the physical world. Here's why this is a step-change for physical AI engineering: Traditional robotics models rely on static snapshots. But knowing *when* a task is done, such as when to stop pouring coffee into a cup or when a trash bag is securely tied, requires continuous temporal awareness. Gemini Robotics ER 2 integrates directly with the bidirectional streaming Gemini Live API to reason about what comes next while simultaneously executing motor actions. What makes Gemini Robotics ER 2 different: 🎯 91.3% accuracy on live video moment-finding (0.96s mean absolute distance) at 4x the execution speed of frontier models 📈 Continuous progress tracking across 5 completion stages (57.4% accuracy) to self-correct mid-task without restarting 🛠️ Native agentic tool orchestration that commands lower-level VLA models, navigation APIs, and Google Search 🤝 Multi-robot collaboration allowing physically diverse machines (like Apptronik's Apollo 2 humanoid and Franka's FR3 Duo arm) to hand off tasks in shared spaces 🛡️ Built-in physical safety that autonomously halts robots when humans enter a workspace and resumes once clear

Most robotics AI models suffer from the "stop-and-think" problem. They take a static picture, pause to reason, execute an action, and repeat. In the real world, that latency causes spills, collisions, and failed tasks. Google DeepMind just launched Gemini Robotics ER 2: an embodied reasoning model that thinks and acts at the speed of the physical world. Here's why this is a step-change for physical AI engineering: Traditional robotics models rely on static snapshots. But knowing when a task is done, such as when to stop pouring coffee into a cup or when a trash bag is securely tied, requires continuous temporal awareness. Gemini Robotics ER 2 integrates directly with the bidirectional streaming Gemini Live API to reason about what comes next while simultaneously executing motor actions. What makes Gemini Robotics ER 2 different: 🎯 91.3% accuracy on live video moment-finding (0.96s mean absolute distance) at 4x the execution speed of frontier models 📈 Continuous progress tracking across 5 completion stages (57.4% accuracy) to self-correct mid-task without restarting 🛠️ Native agentic tool orchestration that commands lower-level VLA models, navigation APIs, and Google Search 🤝 Multi-robot collaboration allowing physically diverse machines (like Apptronik's Apollo 2 humanoid and Franka's FR3 Duo arm) to hand off tasks in shared spaces 🛡️ Built-in physical safety that autonomously halts robots when humans enter a workspace and resumes once clear

Karl Weinmeister

28,107 views • 14 days 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

My conversation with Sergey Levine (Sergey Levine). Sergey is the co-founder of Physical Intelligence -- a company building foundation models that can control any robot to do any task in any environment. The company's thesis is that generality is more scalable than specialization, meaning that a model trained across many different robots and tasks will ultimately outperform any system built to do one thing well (eg, just wash dishes). Sergey is a researcher by background, but I think you will appreciate how practical and commercially grounded this conversation is. We discuss: - Why changing a diaper will be the last task a robot masters - The simulation v. real-world data debate - How multimodal LLMs give robots common sense - Moravec's Paradox + Robot Olympics - Why robots can do long-horizon tasks now - A realistic timeline for robots in our homes I should note that I am an investor in Physical Intelligence -- I made the investment because I believe it is one of the most important companies tackling the problem of robotics. Enjoy! Timestamps: 0:00 Intro 2:39 Defining Physical Intelligence 5:19 The Challenge of Building General Models 6:34 The Stakes and Future of General Purpose Robotics 8:15 Pros and Cons of Humanoid Robots 10:12 Historical Milestones in Robotics Research 15:31 Combining Generative AI and Deep RL 21:24 Moravec's Paradox 25:33 Kitchen Robots 29:30 Simulation vs. Real-World Data 30:48 The Robot Olympics 36:31 The Physiological Reality of Embodiment 38:56 Controversies in the Robotics Community 44:18 What Makes a Great Researcher 48:27 How Businesses Should Prepare for Robotics 54:09 Tracking Progress Through Research Papers 57:02 The Next Step: Mid-Level Reasoning 1:02:00 The Kindest Thing

My conversation with Sergey Levine (Sergey Levine). Sergey is the co-founder of Physical Intelligence -- a company building foundation models that can control any robot to do any task in any environment. The company's thesis is that generality is more scalable than specialization, meaning that a model trained across many different robots and tasks will ultimately outperform any system built to do one thing well (eg, just wash dishes). Sergey is a researcher by background, but I think you will appreciate how practical and commercially grounded this conversation is. We discuss: - Why changing a diaper will be the last task a robot masters - The simulation v. real-world data debate - How multimodal LLMs give robots common sense - Moravec's Paradox + Robot Olympics - Why robots can do long-horizon tasks now - A realistic timeline for robots in our homes I should note that I am an investor in Physical Intelligence -- I made the investment because I believe it is one of the most important companies tackling the problem of robotics. Enjoy! Timestamps: 0:00 Intro 2:39 Defining Physical Intelligence 5:19 The Challenge of Building General Models 6:34 The Stakes and Future of General Purpose Robotics 8:15 Pros and Cons of Humanoid Robots 10:12 Historical Milestones in Robotics Research 15:31 Combining Generative AI and Deep RL 21:24 Moravec's Paradox 25:33 Kitchen Robots 29:30 Simulation vs. Real-World Data 30:48 The Robot Olympics 36:31 The Physiological Reality of Embodiment 38:56 Controversies in the Robotics Community 44:18 What Makes a Great Researcher 48:27 How Businesses Should Prepare for Robotics 54:09 Tracking Progress Through Research Papers 57:02 The Next Step: Mid-Level Reasoning 1:02:00 The Kindest Thing

Patrick OShaughnessy

133,833 views • 4 months ago

For decades, we’ve dreamed of robots that can seamlessly step into our world and lend a hand. Today, we take a major stride toward making that dream a reality: Introducing Gemini Robotics 2 from Google DeepMind, the intelligence layer powering the next generation of truly adaptable robots. This major advance unlocks intelligent whole-body control, advanced dexterity, and even multi-robot collaboration 🤯. Ok but... how does a robot actually "think"? Real-world tasks take time and planning. To manage that complexity, our new embodied reasoning model, Gemini Robotics ER 2, acts as the robot’s high-level brain, enhancing the robot’s capabilities to: — Observe the environment — Reason about the actions needed to complete the task — Coordinate with the vision-language-action model to carry out actions — Track progress until the job is done This setup allows robots to execute complex multi-step workflows, self-correct if a step fails, and adapt to completely novel situations. Learn more about Gemini Robotics ER 2 (and our two other brand new models) here:

Google AI

481,014 views • 14 days 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

Touch: A Game-Changer in Dexterity for Robots You can’t be responsive to the world if you can’t feel it. That’s true for people—and it's just as true for robots. We’re zeroed in on a practical, yet innovative, approach to creating general purpose robots designed to perform physical work. And that means hands, powered not just by vision, but by touch. Vision alone falls short. There are many instances where our vision is obstructed while performing work tasks–like grabbing items out of a bin–you lose line of sight. You need tactile intelligence—texture, pressure, contact—with real-time feedback to move faster, smarter, and with confidence. Watch a full explainer video on Sanctuary AI’s latest touch sensors on our blog: #Robotics #AI #HumanoidRobots #Dexterity #TactileSensing #SanctuaryAI

Touch: A Game-Changer in Dexterity for Robots You can’t be responsive to the world if you can’t feel it. That’s true for people—and it's just as true for robots. We’re zeroed in on a practical, yet innovative, approach to creating general purpose robots designed to perform physical work. And that means hands, powered not just by vision, but by touch. Vision alone falls short. There are many instances where our vision is obstructed while performing work tasks–like grabbing items out of a bin–you lose line of sight. You need tactile intelligence—texture, pressure, contact—with real-time feedback to move faster, smarter, and with confidence. Watch a full explainer video on Sanctuary AI’s latest touch sensors on our blog: #Robotics #AI #HumanoidRobots #Dexterity #TactileSensing #SanctuaryAI

Sanctuary AI

53,269 views • 1 year ago

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Mario Gabriele 🦊

27,871 views • 5 months ago

We profiled the most hyped robotics company in the world. Inside Physical Intelligence with Co-founder Lachy Groom Exclusive interview in the Physical Intelligence robotics lab, who’re backed by top investors to build robots which work in the real world. Not just scripted environments. We cover: 0:00 So…What is Physical Intelligence? 1:41 Why can’t we solve Marovec’s Paradox? 2:33 Live Demo: Meet the Machines 4:17 Why robots still struggle today… 6:14 Meet PI’s Investors (Thrive Capital) 7:19 Teaming up with the mega-brilliant co-founder group of Karol Haussman, Sergey Levine, Chelsea Finn, Brian Ichter, Adnan Esmail & Quan Vuong 9:20 PI’s Robot Capabilities 11:37 How are they making intelligent robots? 13:43 The Future Impact of Everyday Robots 15:49 Jeff Bezos Investment in Physical Intelligence 16:43 What will the next 1-3 years look like? 17:54 Revealing PI’s Work Culture 19:23 Outro

We profiled the most hyped robotics company in the world. Inside Physical Intelligence with Co-founder Lachy Groom Exclusive interview in the Physical Intelligence robotics lab, who’re backed by top investors to build robots which work in the real world. Not just scripted environments. We cover: 0:00 So…What is Physical Intelligence? 1:41 Why can’t we solve Marovec’s Paradox? 2:33 Live Demo: Meet the Machines 4:17 Why robots still struggle today… 6:14 Meet PI’s Investors (Thrive Capital) 7:19 Teaming up with the mega-brilliant co-founder group of Karol Haussman, Sergey Levine, Chelsea Finn, Brian Ichter, Adnan Esmail & Quan Vuong 9:20 PI’s Robot Capabilities 11:37 How are they making intelligent robots? 13:43 The Future Impact of Everyday Robots 15:49 Jeff Bezos Investment in Physical Intelligence 16:43 What will the next 1-3 years look like? 17:54 Revealing PI’s Work Culture 19:23 Outro

Sachin Shah

119,486 views • 6 months ago

The occupation just leveled the neighborhoods next to us with a robots carrying tons of explosives. Ours is next. There’s nowhere left to run. No shelter. No safety. How do we survive drones, snipers, bombs, airstrikes? Is no one able to stop this massacre? #SaveGaza

Sensitive content

The occupation just leveled the neighborhoods next to us with a robots carrying tons of explosives. Ours is next. There’s nowhere left to run. No shelter. No safety. How do we survive drones, snipers, bombs, airstrikes? Is no one able to stop this massacre? #SaveGaza

Ahmed El-Madhoun

29,641 views • 11 months ago

.steve kerr: I'm endorsing President Biden because he supports implementing common sense gun safety measures. I know Donald Trump will not do that, so it's a simple choice for me. It’s too important to save lives not to speak out. Gun violence is the number one killer of children. 80% of people want universal background checks, it doesn't matter your political affiliation. We have to implement gun safety laws and President Biden is willing to do the work to make that happen

.steve kerr: I'm endorsing President Biden because he supports implementing common sense gun safety measures. I know Donald Trump will not do that, so it's a simple choice for me. It’s too important to save lives not to speak out. Gun violence is the number one killer of children. 80% of people want universal background checks, it doesn't matter your political affiliation. We have to implement gun safety laws and President Biden is willing to do the work to make that happen

Headquarters

523,109 views • 2 years ago

Neel Nanda is leading a Google DeepMind research team at 26. He and I discuss: • How that happened • “If your safety work doesn't advance capabilities, it's probably bad safety work” • Should people work at the safest or most reckless AI company? • An AI PhD – with these timelines?! • How to best operate in a big frontier AI company • Neel's distinctive uses of LLMs and which cold emails he answers • A common reasoning error in AI alignment • Why he (Neel Nanda) refuses to share his p(doom) This is part 2 of our conversation, part 1 was a comprehensive update on his research area: mechanistic interpretability, which I'll link below. Links to this episode of the 80,000 Hours Podcast below — enjoy!

Neel Nanda is leading a Google DeepMind research team at 26. He and I discuss: • How that happened • “If your safety work doesn't advance capabilities, it's probably bad safety work” • Should people work at the safest or most reckless AI company? • An AI PhD – with these timelines?! • How to best operate in a big frontier AI company • Neel's distinctive uses of LLMs and which cold emails he answers • A common reasoning error in AI alignment • Why he (Neel Nanda) refuses to share his p(doom) This is part 2 of our conversation, part 1 was a comprehensive update on his research area: mechanistic interpretability, which I'll link below. Links to this episode of the 80,000 Hours Podcast below — enjoy!

Rob Wiblin

111,865 views • 11 months ago

The need to come together for the cause of road safety is crystal clear - because just one careless driver can put countless lives at risk. One moment of recklessness at a red light can lead to chaos for many. Join us at Sadak Suraksha Ki Pathshaala to truly understand why every action on the road matters. #SadakSurakshaAbhiyaan #ParvaahKarengeSurakshitRahenge #FollowtrafficRules #DriveSafe Amitabh Bachchan

The need to come together for the cause of road safety is crystal clear - because just one careless driver can put countless lives at risk. One moment of recklessness at a red light can lead to chaos for many. Join us at Sadak Suraksha Ki Pathshaala to truly understand why every action on the road matters. #SadakSurakshaAbhiyaan #ParvaahKarengeSurakshitRahenge #FollowtrafficRules #DriveSafe Amitabh Bachchan

Nitin Gadkari

17,713 views • 1 year ago

Damn. DeepMind's generalist AI agent SIMA 2 evolved from basic instruction-following to actual reasoning companion. Uses vision and keyboard/mouse like a human player, works across dozens of games without touching game code. The robotics angle is obvious - if you can generalize across virtual 3D worlds, physical embodiment is next. Limited research preview for now, probably because the self-improvement stuff needs more safety work.

Damn. DeepMind's generalist AI agent SIMA 2 evolved from basic instruction-following to actual reasoning companion. Uses vision and keyboard/mouse like a human player, works across dozens of games without touching game code. The robotics angle is obvious - if you can generalize across virtual 3D worlds, physical embodiment is next. Limited research preview for now, probably because the self-improvement stuff needs more safety work.

Bilawal Sidhu

48,638 views • 9 months ago

Robotics is entering the mainstream! 👀 Let's accept this fact. Consumptionism – stonks. Possibilities (HW + SW) – stonks. Demographics - not stonks. Modern robots are often designed to interact with our legacy world, whether it's a household, a factory, or a shipyard. That means they often need to work with existing machines, tools, and spaces. In short: retrofitting. Improving old machines with new technologies without major interference in their operation. Modern way of integrating things. You don't have to change the entire environment to deploy a robot. The robot will adapt, just as human would when freshly hired. The promise is to have an army of robots to help with the world designed for us. I also believe that general purpose robots, are not a direct competition for single purpose machines (like a highly efficient specialized packaging machine, for example). It's like adding a robot to this carriage. Specialized machine could be a car ;) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Robotics is entering the mainstream! 👀 Let's accept this fact. Consumptionism – stonks. Possibilities (HW + SW) – stonks. Demographics - not stonks. Modern robots are often designed to interact with our legacy world, whether it's a household, a factory, or a shipyard. That means they often need to work with existing machines, tools, and spaces. In short: retrofitting. Improving old machines with new technologies without major interference in their operation. Modern way of integrating things. You don't have to change the entire environment to deploy a robot. The robot will adapt, just as human would when freshly hired. The promise is to have an army of robots to help with the world designed for us. I also believe that general purpose robots, are not a direct competition for single purpose machines (like a highly efficient specialized packaging machine, for example). It's like adding a robot to this carriage. Specialized machine could be a car ;) ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

14,396 views • 6 months ago

Grateful for the courage of Principal Kirk Moore in Oklahoma who ran toward danger, took a bullet, and put himself between harm and his students. This is what heroism looks like. But let’s be clear: this is a kind of bravery that should never be required. Our children’s leading killer is gun violence. We cannot just praise courage we must match it. Honor him not just with words, but with action. We must do more to pass common sense gun safety laws to save more of our children.

Grateful for the courage of Principal Kirk Moore in Oklahoma who ran toward danger, took a bullet, and put himself between harm and his students. This is what heroism looks like. But let’s be clear: this is a kind of bravery that should never be required. Our children’s leading killer is gun violence. We cannot just praise courage we must match it. Honor him not just with words, but with action. We must do more to pass common sense gun safety laws to save more of our children.

Cory Booker

26,474 views • 3 months ago

Every time a sanitation worker enters a sewage chamber, the risk is real. That reality led Shubham Vishvakerma of ARC Robotics to build robots that step in where human lives were once at risk. With Kotak’s support, his purpose-led mission is making sanitation safer and more dignified. When one story is shared, many are inspired. Watch his entire journey on Hausla Talks Season 2 - Hausle se Hauslon Tak. Link: #HauslaHaiTohHoJayega #KotakMahindraBank #HauslaTalksSeason2

Every time a sanitation worker enters a sewage chamber, the risk is real. That reality led Shubham Vishvakerma of ARC Robotics to build robots that step in where human lives were once at risk. With Kotak’s support, his purpose-led mission is making sanitation safer and more dignified. When one story is shared, many are inspired. Watch his entire journey on Hausla Talks Season 2 - Hausle se Hauslon Tak. Link: #HauslaHaiTohHoJayega #KotakMahindraBank #HauslaTalksSeason2

Kotak Mahindra Bank

25,017 views • 7 months ago

.Sergey Levine is one of the world's leading robotics researchers (and co-founder of Physical Intelligence). He thinks fully autonomous robots are much closer than people realize - when I pushed him on a prediction, he said 5 years to robots that can autonomously run your household). The big game changer, in his view, is that we finally have LLMs with common sense and prior knowledge that we can scaffold the robot models around. We get into all the reasons this might not happen, the data flywheel from deployment, architecture, simulation, manufacturing, and much more. Enjoy! 0:00:00 – Timeline to widely deployed autonomous robots 0:22:12 – Why robotics will scale faster than self-driving cars 0:32:15 – How vision-language-action models work 0:50:26 – Improvements needed for brainlike efficiency 1:02:48 – Learning from simulation 1:14:08 – How much will robots speed up AI buildouts? 1:22:54 – If hardware’s the bottleneck, does China win by default?

.Sergey Levine is one of the world's leading robotics researchers (and co-founder of Physical Intelligence). He thinks fully autonomous robots are much closer than people realize - when I pushed him on a prediction, he said 5 years to robots that can autonomously run your household). The big game changer, in his view, is that we finally have LLMs with common sense and prior knowledge that we can scaffold the robot models around. We get into all the reasons this might not happen, the data flywheel from deployment, architecture, simulation, manufacturing, and much more. Enjoy! 0:00:00 – Timeline to widely deployed autonomous robots 0:22:12 – Why robotics will scale faster than self-driving cars 0:32:15 – How vision-language-action models work 0:50:26 – Improvements needed for brainlike efficiency 1:02:48 – Learning from simulation 1:14:08 – How much will robots speed up AI buildouts? 1:22:54 – If hardware’s the bottleneck, does China win by default?

Dwarkesh Patel

211,158 views • 11 months ago

Sometimes the smartest robot isn't the one with the most AI. It's the one with the best mechanics. 🤖 Inspired by the legs of an ostrich, BirdBot achieves remarkably energy-efficient walking using a passive clutch system that reduces the need for complex control. No endless calculations. No constant adjustments. Just intelligent design inspired by nature. This highlights a lesson robotics keeps relearning: Nature has already solved many of the problems engineers are trying to crack. The future of robotics may not come from more computing power. It may come from better biomechanics. What animal do you think has the most to teach robotics? 🎥 Media: Max Planck Institute for Intelligent Systems and The University of California ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators. #Robotics #BirdBot #Biomimicry #Engineering #Biorobotics #FutureTech #Innovation #Automation

Sometimes the smartest robot isn't the one with the most AI. It's the one with the best mechanics. 🤖 Inspired by the legs of an ostrich, BirdBot achieves remarkably energy-efficient walking using a passive clutch system that reduces the need for complex control. No endless calculations. No constant adjustments. Just intelligent design inspired by nature. This highlights a lesson robotics keeps relearning: Nature has already solved many of the problems engineers are trying to crack. The future of robotics may not come from more computing power. It may come from better biomechanics. What animal do you think has the most to teach robotics? 🎥 Media: Max Planck Institute for Intelligent Systems and The University of California ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators. #Robotics #BirdBot #Biomimicry #Engineering #Biorobotics #FutureTech #Innovation #Automation

CTO ROBOTICS Media

15,451 views • 28 days ago