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Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a...

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Khurram Javed

3,937 subscribers

52,110 views • 7 months ago •via X (Twitter)

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In order for robots to be deployed in the real world, performing tasks of real value, they must be reliable. Unfortunately, even more, most robotic demos work maybe 70-80% of the time at best. The way to get better reliability is to do real-world reinforcement learning: having the robot teach itself how to perform the task up to a high level of success. The key to doing this is to start with a core of expert human data, use that to train a policy then iteratively improve it, until finally finishing with on-policy reinforcement learning. Kun Lei talks through a unified framework for imitation and reinforcement learning based on PPO, which enables this improvement process. In this episode, Kun Lei explains the theory behind his reinforcement learning method and how it allowed his robot to run in a shopping mall juicing oranges for seven hours at a time, among experiments on a wide variety of tasks and embodiments. Watch episode 58 of RoboPapers now, hosted by Michael Cho - Rbt/Acc and Chris Paxton!
1:11:51

In order for robots to be deployed in the real world, performing tasks of real value, they must be reliable. Unfortunately, even more, most robotic demos work maybe 70-80% of the time at best. The way to get better reliability is to do real-world reinforcement learning: having the robot teach itself how to perform the task up to a high level of success. The key to doing this is to start with a core of expert human data, use that to train a policy then iteratively improve it, until finally finishing with on-policy reinforcement learning. Kun Lei talks through a unified framework for imitation and reinforcement learning based on PPO, which enables this improvement process. In this episode, Kun Lei explains the theory behind his reinforcement learning method and how it allowed his robot to run in a shopping mall juicing oranges for seven hours at a time, among experiments on a wide variety of tasks and embodiments. Watch episode 58 of RoboPapers now, hosted by Michael Cho - Rbt/Acc and Chris Paxton!

RoboPapers

18,813 views • 5 months ago

Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)
1:00

Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)

Abhishek Gupta

11,994 views • 1 year ago

Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!
53:45

Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

RoboPapers

38,334 views • 28 days ago

"My kids do not go to school, and have never been to school." "Not because I don't value learning, I value learning very much. I just disagree with the belief that school is a necessary component for learning." "I believe children are always learning, and when allowed to pursue something they are actually interested in, something they have a need to learn, something that is relevant to their lives and has real world context, I believe that learning is exponentially more effective than the coercive curriculums of the conventional educational model." Agree or disagree? 🤔
0:25

"My kids do not go to school, and have never been to school." "Not because I don't value learning, I value learning very much. I just disagree with the belief that school is a necessary component for learning." "I believe children are always learning, and when allowed to pursue something they are actually interested in, something they have a need to learn, something that is relevant to their lives and has real world context, I believe that learning is exponentially more effective than the coercive curriculums of the conventional educational model." Agree or disagree? 🤔

Wide Awake Media

40,908 views • 1 year ago

Helix is now learning directly from human video data We have already trained on data collected in the real world, including Brookfield residential units To our knowledge, this is the first instance of a humanoid robot learning navigation end-to-end using only human video
0:24

Helix is now learning directly from human video data We have already trained on data collected in the real world, including Brookfield residential units To our knowledge, this is the first instance of a humanoid robot learning navigation end-to-end using only human video

Figure

46,041 views • 9 months ago

Everybody is talking about recursive self-improvement (RSI) and meta learning. Here is my old 2020 talk about this [1]. It has aged well. Example: humans still define the starts & ends of trials of many modern meta learners. My RSI systems since 1994 LEARN to (re)define them [2]! [1] Meta Learning Machines in a Single Lifelong Trial (talk for workshops at ICML 2020 and NeurIPS 2021, based on earlier talks since 1994). Abstract: the most widely used machine learning algorithms were designed by humans and thus are hindered by our cognitive biases and limitations. Can we also construct meta learning algorithms that can learn better learning algorithms so that our self-improving AIs have no limits other than those inherited from computability and physics? This question has been a main driver of my research since I wrote a thesis on it in 1987 [2]. Here I summarize our work on meta reinforcement learning with self-modifying policies in a single lifelong trial (since 1994), and mathematically optimal meta-learning through the self-referential Gödel Machine (since 2003). Many additional publications on meta-learning since 1987 can be found in the RSI overview [2]. [2] J. Schmidhuber (AI Blog, 2020-2025). 1/3 century anniversary of first publication on recursive self-improvement (RSI) and meta learning machines that learn to learn (1987). For its cover I drew a robot that bootstraps itself. 1992-: gradient descent-based neural meta learning. 1994-: meta reinforcement learning with self-modifying policies. 1997: meta RL plus artificial curiosity and intrinsic motivation. 2002-: asymptotically optimal meta learning for curriculum learning. 2003-: mathematically optimal Gödel Machine. 2020-: new stuff!
23:42

Everybody is talking about recursive self-improvement (RSI) and meta learning. Here is my old 2020 talk about this [1]. It has aged well. Example: humans still define the starts & ends of trials of many modern meta learners. My RSI systems since 1994 LEARN to (re)define them [2]! [1] Meta Learning Machines in a Single Lifelong Trial (talk for workshops at ICML 2020 and NeurIPS 2021, based on earlier talks since 1994). Abstract: the most widely used machine learning algorithms were designed by humans and thus are hindered by our cognitive biases and limitations. Can we also construct meta learning algorithms that can learn better learning algorithms so that our self-improving AIs have no limits other than those inherited from computability and physics? This question has been a main driver of my research since I wrote a thesis on it in 1987 [2]. Here I summarize our work on meta reinforcement learning with self-modifying policies in a single lifelong trial (since 1994), and mathematically optimal meta-learning through the self-referential Gödel Machine (since 2003). Many additional publications on meta-learning since 1987 can be found in the RSI overview [2]. [2] J. Schmidhuber (AI Blog, 2020-2025). 1/3 century anniversary of first publication on recursive self-improvement (RSI) and meta learning machines that learn to learn (1987). For its cover I drew a robot that bootstraps itself. 1992-: gradient descent-based neural meta learning. 1994-: meta reinforcement learning with self-modifying policies. 1997: meta RL plus artificial curiosity and intrinsic motivation. 2002-: asymptotically optimal meta learning for curriculum learning. 2003-: mathematically optimal Gödel Machine. 2020-: new stuff!

Jürgen Schmidhuber

217,809 views • 4 months ago

notes on my reinforcement learning for rocket league side quest so far: i’m in awe that — we live in a time where i can train a reinforcement learning model that is learning to play rocket league and at the same time, test community made rocket league models. where, both processes can run in parallel & locally on my hardware. in the game window — i’m playing against a reinforcement learning ppo model that’s running locally. and in the vscode window — i’m training my own reinforcement learning model (just a dummy example script for now). also, the rocket league botting ecosystem has incredible tooling where you can test out different reinforcement learning ideas against other algorithms + it has great documentation for linux and windows. it’s honestly a beautiful hidden gem of a rabbit hole :)
0:24

notes on my reinforcement learning for rocket league side quest so far: i’m in awe that — we live in a time where i can train a reinforcement learning model that is learning to play rocket league and at the same time, test community made rocket league models. where, both processes can run in parallel & locally on my hardware. in the game window — i’m playing against a reinforcement learning ppo model that’s running locally. and in the vscode window — i’m training my own reinforcement learning model (just a dummy example script for now). also, the rocket league botting ecosystem has incredible tooling where you can test out different reinforcement learning ideas against other algorithms + it has great documentation for linux and windows. it’s honestly a beautiful hidden gem of a rabbit hole :)

naklecha

46,302 views • 1 year ago

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."
2:13

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."

TBPN

98,711 views • 5 months ago

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."
2:13

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."

TBPN

29,667 views • 5 months ago

I don’t know if we live in a Matrix, but I know for sure that robots will spend most of their lives in simulation. Let machines train machines. I’m excited to introduce DexMimicGen, a massive-scale synthetic data generator that enables a humanoid robot to learn complex skills from only a handful of human demonstrations. Yes, as few as 5! DexMimicGen addresses the biggest pain point in robotics: where do we get data? Unlike with LLMs, where vast amounts of texts are readily available, you cannot simply download motor control signals from the internet. So researchers teleoperate the robots to collect motion data via XR headsets. They have to repeat the same skill over and over and over again, because neural nets are data hungry. This is a very slow and uncomfortable process. At NVIDIA, we believe the majority of high-quality tokens for robot foundation models will come from simulation. What DexMimicGen does is to trade GPU compute time for human time. It takes one motion trajectory from human, and multiplies into 1000s of new trajectories. A robot brain trained on this augmented dataset will generalize far better in the real world. Think of DexMimicGen as a learning signal amplifier. It maps a small dataset to a large (de facto infinite) dataset, using physics simulation in the loop. In this way, we free humans from babysitting the bots all day. The future of robot data is generative. The future of the entire robot learning pipeline will also be generative. 🧵
0:58

I don’t know if we live in a Matrix, but I know for sure that robots will spend most of their lives in simulation. Let machines train machines. I’m excited to introduce DexMimicGen, a massive-scale synthetic data generator that enables a humanoid robot to learn complex skills from only a handful of human demonstrations. Yes, as few as 5! DexMimicGen addresses the biggest pain point in robotics: where do we get data? Unlike with LLMs, where vast amounts of texts are readily available, you cannot simply download motor control signals from the internet. So researchers teleoperate the robots to collect motion data via XR headsets. They have to repeat the same skill over and over and over again, because neural nets are data hungry. This is a very slow and uncomfortable process. At NVIDIA, we believe the majority of high-quality tokens for robot foundation models will come from simulation. What DexMimicGen does is to trade GPU compute time for human time. It takes one motion trajectory from human, and multiplies into 1000s of new trajectories. A robot brain trained on this augmented dataset will generalize far better in the real world. Think of DexMimicGen as a learning signal amplifier. It maps a small dataset to a large (de facto infinite) dataset, using physics simulation in the loop. In this way, we free humans from babysitting the bots all day. The future of robot data is generative. The future of the entire robot learning pipeline will also be generative. 🧵

Jim Fan

165,246 views • 1 year ago

We spoke with Michael Mignano & Nir Zicherman about Oboe; a new AI-powered learning app that lets you build personalized courses on any topic with just a prompt. As Nir put it: "People do not learn in one path, and they don't learn with one format. So a core philosophy of ours is if you're going to build a learning platform, you have to learn it the way that people actually learn. And the way that people actually learn is multimodal."
0:47

We spoke with Michael Mignano & Nir Zicherman about Oboe; a new AI-powered learning app that lets you build personalized courses on any topic with just a prompt. As Nir put it: "People do not learn in one path, and they don't learn with one format. So a core philosophy of ours is if you're going to build a learning platform, you have to learn it the way that people actually learn. And the way that people actually learn is multimodal."

TBPN

17,903 views • 10 months ago

Would you want to be a teacher on your staff as a principal? I have often asked teachers, would you want to be a learner in your own classroom, but the question above for administrators could be even more critical. If we do not support those closest to students every day in an effective manner, it is much harder for things to improve in schools. If we want learning to look different in classrooms, then leadership must also evolve. This doesn’t mean that everything done in the past has been wrong. Some things that mattered 50 years ago will matter now, both in learning and leadership. But replicating everything that was done in the past, whether it was effective or not, isn’t a great strategy for moving schools forward. I was blessed to learn from some amazing principals in my career, but I also learned about things that I hated as a teacher and swore that I would do my best not to replicate those strategies. I wrote this in my upcoming book co-authored with Allyson Apsey (Allyson Apsey) titled, “What Makes a Great Principal”: “If we do things in our schools and classrooms that were done hundreds of years ago that still work today, we should continue to do them. On the other hand, if we do new stuff just because it is new, but it doesn’t work, we shouldn’t be doing it. Whatever works for our community is where our focus should be, no matter when it originated.” What worked in the past? What would you change? What would you have wanted as a teacher, and how can you make that happen? Simply replicating the strategies of the past, whether good or bad, will not necessarily lead education to a better future. Innovation is crucial to leadership as much as it is to teaching and learning.
1:25

Would you want to be a teacher on your staff as a principal? I have often asked teachers, would you want to be a learner in your own classroom, but the question above for administrators could be even more critical. If we do not support those closest to students every day in an effective manner, it is much harder for things to improve in schools. If we want learning to look different in classrooms, then leadership must also evolve. This doesn’t mean that everything done in the past has been wrong. Some things that mattered 50 years ago will matter now, both in learning and leadership. But replicating everything that was done in the past, whether it was effective or not, isn’t a great strategy for moving schools forward. I was blessed to learn from some amazing principals in my career, but I also learned about things that I hated as a teacher and swore that I would do my best not to replicate those strategies. I wrote this in my upcoming book co-authored with Allyson Apsey (Allyson Apsey) titled, “What Makes a Great Principal”: “If we do things in our schools and classrooms that were done hundreds of years ago that still work today, we should continue to do them. On the other hand, if we do new stuff just because it is new, but it doesn’t work, we shouldn’t be doing it. Whatever works for our community is where our focus should be, no matter when it originated.” What worked in the past? What would you change? What would you have wanted as a teacher, and how can you make that happen? Simply replicating the strategies of the past, whether good or bad, will not necessarily lead education to a better future. Innovation is crucial to leadership as much as it is to teaching and learning.

George Couros

22,738 views • 2 years ago

“He’s not taking it seriously” - tell that to the (much bigger) version of myself that I was a year ago. Listen; I’m just trying to be real. I’m not trying to make excuses, even if I have. This past month was hard for me, especially sharing it the way that I did. But regardless of that I’m not gonna let that low point or anyone on the internet take away from the progress that I’ve made over the YEARS of struggling and learning that I’ve gone through. I will never give up on this journey, I still plan to hit 399 by the end of April; I have a plan, I have a schedule, and I’m going to do my best to follow it. I apologize if this feels like giving attention directly to the negativity, but I felt like these thoughts / this response and the *hope* that I feel right now regarding my journey really need to be shared. I’m PROUD of what I’ve accomplished, even knowing I still have a long ways to go. I see the positive, I promise, and I’m sorry that I can’t always take the time to respond to more messages. #weightlossjourney
1:19

“He’s not taking it seriously” - tell that to the (much bigger) version of myself that I was a year ago. Listen; I’m just trying to be real. I’m not trying to make excuses, even if I have. This past month was hard for me, especially sharing it the way that I did. But regardless of that I’m not gonna let that low point or anyone on the internet take away from the progress that I’ve made over the YEARS of struggling and learning that I’ve gone through. I will never give up on this journey, I still plan to hit 399 by the end of April; I have a plan, I have a schedule, and I’m going to do my best to follow it. I apologize if this feels like giving attention directly to the negativity, but I felt like these thoughts / this response and the *hope* that I feel right now regarding my journey really need to be shared. I’m PROUD of what I’ve accomplished, even knowing I still have a long ways to go. I see the positive, I promise, and I’m sorry that I can’t always take the time to respond to more messages. #weightlossjourney

Ethan Benard

11,651 views • 3 months ago

Sam Altman on what you need to do to survive in the age of artificial intelligence. "You are about to enter the greatest golden age of human possibility..." To thrive in that world, the skills that matter most are: - Deep familiarity with the tools - Staying abreast of changes - Developing a great intuition for AI tools, where things are going, and how to make use of it - Resilience and the ability to learn things fast and evolve yourself with technology I know most of this stuff is a pretty big no-brainer for anyone paying attention, but here's the takeaway: AI upskilling and keeping up with AI are possibly the most important skills in the world right now. And the most fascinating part is that new AI developments and tools come out so fast that everyone is constantly learning. I'd be willing to bet that if you're a complete beginner but spend 3 months of dedicated learning, you'd be fully caught up to power users. In other words: Spend 3 months learning → work 2-4x faster and future-proof yourself. There's so much opportunity for anyone who just starts now.
0:38

Sam Altman on what you need to do to survive in the age of artificial intelligence. "You are about to enter the greatest golden age of human possibility..." To thrive in that world, the skills that matter most are: - Deep familiarity with the tools - Staying abreast of changes - Developing a great intuition for AI tools, where things are going, and how to make use of it - Resilience and the ability to learn things fast and evolve yourself with technology I know most of this stuff is a pretty big no-brainer for anyone paying attention, but here's the takeaway: AI upskilling and keeping up with AI are possibly the most important skills in the world right now. And the most fascinating part is that new AI developments and tools come out so fast that everyone is constantly learning. I'd be willing to bet that if you're a complete beginner but spend 3 months of dedicated learning, you'd be fully caught up to power users. In other words: Spend 3 months learning → work 2-4x faster and future-proof yourself. There's so much opportunity for anyone who just starts now.

Rowan Cheung

854,262 views • 2 years ago

Some great videos from the Gemini VLA release. These kinds of dexterous tasks require tons of coordination and interact with materials that are very hard to model in simulation. Good use of e2e learning and yet more evidence this is the right direction for robotics
0:33

Some great videos from the Gemini VLA release. These kinds of dexterous tasks require tons of coordination and interact with materials that are very hard to model in simulation. Good use of e2e learning and yet more evidence this is the right direction for robotics

Chris Paxton

30,949 views • 1 year ago

🚨 Chamath explains Grok 4's breakthrough and how Elon leapfrogged the competition in AI: Elon Musk and the team at xAI understood "The Bitter Lesson" by Richard Sutton Chamath Palihapitiya: "(The Bitter Lesson) basically says in a nutshell, that you're always better off, when you're trying to solve an AI problem, taking a general learning approach that can scale with computation. Because it ultimately proves to be the most effective." "The alternative would be something that's much more human labored and human involved that requires human knowledge." "So the first method, what it essentially allows you to do is view any problem as an endless, scalable search or learning task." "As it's turned out, whether it's chess, or Go, or speech recognition, or computer vision, whenever there was two competing approaches, one that used general computation and one that used human knowledge, the general computation problem always won." " And so it creates this bitter lesson for humans that want to think that we are at the center of all of this critical learning and all of these leaps, and what these results show is a general computational approach that doesn't require as much human labeling can actually get to the answer and better answers faster."
3:57

🚨 Chamath explains Grok 4's breakthrough and how Elon leapfrogged the competition in AI: Elon Musk and the team at xAI understood "The Bitter Lesson" by Richard Sutton Chamath Palihapitiya: "(The Bitter Lesson) basically says in a nutshell, that you're always better off, when you're trying to solve an AI problem, taking a general learning approach that can scale with computation. Because it ultimately proves to be the most effective." "The alternative would be something that's much more human labored and human involved that requires human knowledge." "So the first method, what it essentially allows you to do is view any problem as an endless, scalable search or learning task." "As it's turned out, whether it's chess, or Go, or speech recognition, or computer vision, whenever there was two competing approaches, one that used general computation and one that used human knowledge, the general computation problem always won." " And so it creates this bitter lesson for humans that want to think that we are at the center of all of this critical learning and all of these leaps, and what these results show is a general computational approach that doesn't require as much human labeling can actually get to the answer and better answers faster."

The All-In Podcast

597,321 views • 1 year ago

📝 AI/ML for Biology & Healthcare: A Learning Path This blog is highly inspired by my experience and exploration in the field of ML for biology and healthcare. Everything that I've been studying for the past years and a more structured way to learn the foundational knowledge to tackle problems in this impactful field. Here's what I covered in the blog: — Programming & Engineering — AI & Machine Learning — Mathematics for ML — Healthcare — Biology & Biolomedicine This is what I hoped to have when I first started learning all this. 🔗 link:
0:20

📝 AI/ML for Biology & Healthcare: A Learning Path This blog is highly inspired by my experience and exploration in the field of ML for biology and healthcare. Everything that I've been studying for the past years and a more structured way to learn the foundational knowledge to tackle problems in this impactful field. Here's what I covered in the blog: — Programming & Engineering — AI & Machine Learning — Mathematics for ML — Healthcare — Biology & Biolomedicine This is what I hoped to have when I first started learning all this. 🔗 link:

TK • 木下

26,889 views • 7 months ago

🎥 || Lewis Hamilton after first runs in the SF-26. “Immediately you’re trying to get a feel for anything, feeling through your body, through the G-long, through the vibrations of the seat and the suspension. You’re trying to pick up all these little bits of information in the little time that you have and then just to be in Fiorano, you know, to be at Ferrari, to be here, to know that this is a start of a long journey.” “But one that I know that it just feels right, I think the team is in the best place, it’s been, I think for the time I’ve been here. And seeing everyone so motivated and driven, I think we’re really on it.” “We’ve got a lot of work to do to really make sure we do all the diligence and we really understand this car and we really have to be quick to react, be very dynamic but I feel like all hands are on deck so I’m sure we can do that.”
1:03

🎥 || Lewis Hamilton after first runs in the SF-26. “Immediately you’re trying to get a feel for anything, feeling through your body, through the G-long, through the vibrations of the seat and the suspension. You’re trying to pick up all these little bits of information in the little time that you have and then just to be in Fiorano, you know, to be at Ferrari, to be here, to know that this is a start of a long journey.” “But one that I know that it just feels right, I think the team is in the best place, it’s been, I think for the time I’ve been here. And seeing everyone so motivated and driven, I think we’re really on it.” “We’ve got a lot of work to do to really make sure we do all the diligence and we really understand this car and we really have to be quick to react, be very dynamic but I feel like all hands are on deck so I’m sure we can do that.”

sin ⁴⁴

106,625 views • 5 months ago