Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

What happens when you change the hand mid-task? We tested this by modifying the hands mid-rollout and letting the same model keep running. It perceives the new tool, and finds a new trajectory and contact strategy to complete the task. This works because training on mixed data forces the... show more

Generalist

25,108 subscribers

22,143 Aufrufe • vor 2 Monaten •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

6 Kommentare

Profilbild von Generalist

Generalistvor 2 Monaten

Unlike humans, robots aren’t locked into the hands they’re born with. Machines can easily swap mechanical hands, and tool changers are common in automation. Switching between different end effectors to reach a goal can be viewed as a form of physical reasoning: using the right tool for the right job, much as multilingual chain-of-thought can improve LLM reasoning for downstream RL.

Profilbild von Generalist

Generalistvor 2 Monaten

Every hand is a different language for physical interaction. Just as training on multiple languages produces more capable LLMs, a model trained across embodiments can gather shared knowledge across instances and more readily separate what is specific to a hand from what is universal about the world.

Profilbild von Generalist

Generalistvor 2 Monaten

Each hand is a different sensorimotor interface by which GEN-1 experiences the physical world. Scaling pretraining across thousands of these interfaces teaches GEN-1 a universal physical commonsense that transfers to new hands and new ways to grasp, push, pull, twist, and more.

Profilbild von Generalist

Generalistvor 2 Monaten

Why create robot intelligence for just one hand, when we could have it learn from many? GEN-1, our latest embodied foundation model, now supports a broad range of end effectors from 5-finger hands, to specialized tools, and everything in between.

Profilbild von Generalist

Generalistvor 2 Monaten

Nature didn’t converge on just a single solution for manipulating the physical world. It exploded into millions. You can see this everywhere in the diversity of life around us: from the beak of a bird, to the trunk of an elephant, from the suction pads of an octopus, to the pollen baskets of a honeybee. The human hand is only a single point in a design space so large we’ve barely begun to map. For machines, five-fingered hands will just be one tool among many; limiting robots to only that would be a failure of imagination.

Profilbild von Generalist

Generalistvor 2 Monaten

If we can build general intelligence that understands the underlying physics of interaction, then the shape of the hand becomes secondary to the intelligence that drives it. A suction pad, a gripper, a brush, a plasma welding nozzle – are all just different interfaces through which the same intelligence can reshape the physical world. The future of robot hands won’t look like ours. It will look more like a toolbox with a thousand hands: augmented, recombined, and scaled. Robots were always meant to extend what humans can do – to empower people to shape the physical world in places and at scales we never could before. Read more about our work at:

Ähnliche Videos

The robot flipped a pancake nobody taught it! 🥞 Skild AI team assumed pancake flipping had to be somewhere in the training data. So they searched. Millions of hours of pre-training data. Nothing. S1 inferred the whole task from a single human demonstration. That's their new general robot model, built as an in-context learner from the ground up. Every new robot task today starts with days of teleoperation and a fine-tuning run on a specialist policy. S1 skips all of it. Much like a language model, it never updates its weights to learn a new task. The demonstration enters the context window, and the policy uses it to decide what to do next. → Ten-minute tasks it was never trained on, composed from primitives learned in pre-training: a new style of coffee, potting a plant, frying pancakes. → Soil and pots arrived at their office at 8:54 PM. The robot was running the task autonomously by 9:27 PM. → Slide objects away mid-reach, swap them, change the lighting, it still finishes. → The prompt waters a plant with a watering can, but only a cup is available. It uses the cup. It doesn't rigidly replay what it saw, but it recovers from its own errors, and sometimes executes with more precision than the demonstrator, when the human fumbles an egg and makes a mess, S1 performs the same step cleanly. The demonstration is a specification of the goal, and not a trajectory to copy. On unseen tasks after 100K hours of pre-training: language-prompted VLAs reach 9%. Their new model reaches 66%. It's already deploying with industrial partners, with a wider rollout over the coming months. Congrats Deepak Pathak and team behind this! 😮‍💨 🔗 Link to their latest blog: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

The robot flipped a pancake nobody taught it! 🥞 Skild AI team assumed pancake flipping had to be somewhere in the training data. So they searched. Millions of hours of pre-training data. Nothing. S1 inferred the whole task from a single human demonstration. That's their new general robot model, built as an in-context learner from the ground up. Every new robot task today starts with days of teleoperation and a fine-tuning run on a specialist policy. S1 skips all of it. Much like a language model, it never updates its weights to learn a new task. The demonstration enters the context window, and the policy uses it to decide what to do next. → Ten-minute tasks it was never trained on, composed from primitives learned in pre-training: a new style of coffee, potting a plant, frying pancakes. → Soil and pots arrived at their office at 8:54 PM. The robot was running the task autonomously by 9:27 PM. → Slide objects away mid-reach, swap them, change the lighting, it still finishes. → The prompt waters a plant with a watering can, but only a cup is available. It uses the cup. It doesn't rigidly replay what it saw, but it recovers from its own errors, and sometimes executes with more precision than the demonstrator, when the human fumbles an egg and makes a mess, S1 performs the same step cleanly. The demonstration is a specification of the goal, and not a trajectory to copy. On unseen tasks after 100K hours of pre-training: language-prompted VLAs reach 9%. Their new model reaches 66%. It's already deploying with industrial partners, with a wider rollout over the coming months. Congrats Deepak Pathak and team behind this! 😮‍💨 🔗 Link to their latest blog: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

11,406 Aufrufe • vor 1 Monat

OpenAI researcher David Holtz reveals workers who don't adapt their prompting to newer models leave 50% of the alpha on the table because half of AI's value comes from how you interact with it: "We ran a lab experiment. We give people the same task to do, but we give them models of different capabilities. How much of the improvement when we give people more capable models comes from the fact that we gave them a better model vs the fact that they actually update and adapt the way they prompt and interact with these models?" "For tasks where precision matters, where you have some output you're trying to get and you're trying to steer the model, which is different from a creative task like brainstorming." "For these steering-based tasks, when you give people a better model, about 50% of the improvement we see in how well they're able to complete the task comes from changes in how they interact with the model." "If you just kept the way you interact with the model static and just made the capabilities better and better, you'd leave 50% of the alpha basically on the table." OpenAI

OpenAI researcher David Holtz reveals workers who don't adapt their prompting to newer models leave 50% of the alpha on the table because half of AI's value comes from how you interact with it: "We ran a lab experiment. We give people the same task to do, but we give them models of different capabilities. How much of the improvement when we give people more capable models comes from the fact that we gave them a better model vs the fact that they actually update and adapt the way they prompt and interact with these models?" "For tasks where precision matters, where you have some output you're trying to get and you're trying to steer the model, which is different from a creative task like brainstorming." "For these steering-based tasks, when you give people a better model, about 50% of the improvement we see in how well they're able to complete the task comes from changes in how they interact with the model." "If you just kept the way you interact with the model static and just made the capabilities better and better, you'd leave 50% of the alpha basically on the table." OpenAI

MTS

15,590 Aufrufe • vor 1 Monat

72 hours in and GPT-5.6 Sol is still running without a single follow-up prompt. The task was to build the most comprehensive map possible of the physical infrastructure powering AI, submarine cables, data centers, power plants, substations and electricity grids. The only thing given was a simple loop: → Find the biggest gap → Research it → Build it → Test it → Publish it → Repeat Most people expected this to stall after a few hours. It didn't. The Mac restarted mid-run and Codex picked up exactly where it left off without losing anything. What this actually proves is that the prompt matters less than the goal. The smarter the model, the more room you need to give it to make its own decisions. Constrain it with steps and you get a worse version of what it could do on its own.

72 hours in and GPT-5.6 Sol is still running without a single follow-up prompt. The task was to build the most comprehensive map possible of the physical infrastructure powering AI, submarine cables, data centers, power plants, substations and electricity grids. The only thing given was a simple loop: → Find the biggest gap → Research it → Build it → Test it → Publish it → Repeat Most people expected this to stall after a few hours. It didn't. The Mac restarted mid-run and Codex picked up exactly where it left off without losing anything. What this actually proves is that the prompt matters less than the goal. The smarter the model, the more room you need to give it to make its own decisions. Constrain it with steps and you get a worse version of what it could do on its own.

0xMarioNawfal

61,751 Aufrufe • vor 2 Monaten

Recently met Sasha Rush and he started giving me an impromptu lecture on how targeted on-policy self-distillation works. I asked him if I could record it on my iPhone. The basic idea is this: if the model made a mistake at some point in the rollout (for example, calling a tool that doesn't exist), we want to discourage this specific error, but we don't want to just learn from the final reward, because it's a very noisy signal spread out over the whole trajectory. So we have another model read this trajectory and figure where the error was made. It simply inserts some hint tokens to the part of the trajectory right above where the mistake was made. Now with these injected hint tokens, have the model run a forward pass. You're not having to regenerate a new rollout - aka no new decode required. The hint causes the model to assign lower probabilities to the error tokens. You then trains the original model to match these new probabilities, teaching it to downweight that specific mistake.

Recently met Sasha Rush and he started giving me an impromptu lecture on how targeted on-policy self-distillation works. I asked him if I could record it on my iPhone. The basic idea is this: if the model made a mistake at some point in the rollout (for example, calling a tool that doesn't exist), we want to discourage this specific error, but we don't want to just learn from the final reward, because it's a very noisy signal spread out over the whole trajectory. So we have another model read this trajectory and figure where the error was made. It simply inserts some hint tokens to the part of the trajectory right above where the mistake was made. Now with these injected hint tokens, have the model run a forward pass. You're not having to regenerate a new rollout - aka no new decode required. The hint causes the model to assign lower probabilities to the error tokens. You then trains the original model to match these new probabilities, teaching it to downweight that specific mistake.

Dwarkesh Patel

431,015 Aufrufe • vor 4 Monaten

The renewal of fighting in Iran demonstrates the problem with Trump’s lack of strategy, the vagueness of the deal to make a deal, and the belligerence of an Iranian regime that feels it has the upper hand. This week, I’ll introduce a new War Powers Resolution to force the Senate to vote on Trump’s war and the price spikes it is causing.

The renewal of fighting in Iran demonstrates the problem with Trump’s lack of strategy, the vagueness of the deal to make a deal, and the belligerence of an Iranian regime that feels it has the upper hand. This week, I’ll introduce a new War Powers Resolution to force the Senate to vote on Trump’s war and the price spikes it is causing.

Senator Adam Schiff

50,870 Aufrufe • vor 2 Monaten

Blendshape Viewer has been updated to V3. This tool lets you visualize which area of a model is affected by a blendshape, and change its value. From now on, you can press the ALT key to compare the blendshape. This version should also run faster on models with lots of shapes.

Blendshape Viewer has been updated to V3. This tool lets you visualize which area of a model is affected by a blendshape, and change its value. From now on, you can press the ALT key to compare the blendshape. This version should also run faster on models with lots of shapes.

Haï~

21,715 Aufrufe • vor 2 Monaten

President Zelenskyy: The task of Ukrainian units is to inflict a level of losses on the occupier at which Russian casualties exceed the amount of reinforcements they are able to send to their forces each month. This is a realistic task. When we speak of 50,000 Russian losses per month, that is the optimal level. It is a difficult task, without question, but it is precisely the level needed for Russia to begin weighing what it is doing and what it is fighting for. The task of the Ministry of Defense of Ukraine, our army, and all the Defense and Security Forces of Ukraine is to ensure exactly this level of Russian losses.

President Zelenskyy: The task of Ukrainian units is to inflict a level of losses on the occupier at which Russian casualties exceed the amount of reinforcements they are able to send to their forces each month. This is a realistic task. When we speak of 50,000 Russian losses per month, that is the optimal level. It is a difficult task, without question, but it is precisely the level needed for Russia to begin weighing what it is doing and what it is fighting for. The task of the Ministry of Defense of Ukraine, our army, and all the Defense and Security Forces of Ukraine is to ensure exactly this level of Russian losses.

Anton Gerashchenko

77,058 Aufrufe • vor 8 Monaten

S1 learns new tasks like a language model. You prompt it with a video demonstration, and it outputs robot actions to complete the task in any environment and in any embodiment.

S1 learns new tasks like a language model. You prompt it with a video demonstration, and it outputs robot actions to complete the task in any environment and in any embodiment.

Skild AI

308,754 Aufrufe • vor 1 Monat

There is no best model. There's a lot of noise about models right now. Who is training them, who owns them, where legal intelligence should live. One question actually matters: what produces the best outcome for the legal task in front of you? That's how we decide things at Legora. We optimize for the end-to-end outcome on a legal task. The model is one layer of that system, not the system. Models are uneven and the frontier changes almost weekly. One model plans a long job well, another runs deep analysis across thousands of documents. Some have to be told exactly what to do, and some are fine with a vague brief. They all break in different ways. So our lawyers write evals and we test them with the Legora BAR, our benchmark for agentic reasoning. Every model takes every test, and the model that wins gets the work. We post-train when we know it buys our customers better performance on a specialized task. Training is a tool we reach for when it helps, nothing more than that. The intelligence that compounds sits in the orchestration layer. Precedents, review standards, client requirements. That knowledge has to stay editable, auditable and portable. In our system, a changed review standard is an edit that takes effect the same day, with no new model training required. No lawyer should have to worry about which model did the work, any more than they think about which chip is in their laptop. They should only care about the quality of the work. That's what we are focused on. If you want the engineering version of this argument rather than the CEO version, our CPO, Bryan Tsao, and CTO, Jacob Lauritzen, take it apart in the video below.

There is no best model. There's a lot of noise about models right now. Who is training them, who owns them, where legal intelligence should live. One question actually matters: what produces the best outcome for the legal task in front of you? That's how we decide things at Legora. We optimize for the end-to-end outcome on a legal task. The model is one layer of that system, not the system. Models are uneven and the frontier changes almost weekly. One model plans a long job well, another runs deep analysis across thousands of documents. Some have to be told exactly what to do, and some are fine with a vague brief. They all break in different ways. So our lawyers write evals and we test them with the Legora BAR, our benchmark for agentic reasoning. Every model takes every test, and the model that wins gets the work. We post-train when we know it buys our customers better performance on a specialized task. Training is a tool we reach for when it helps, nothing more than that. The intelligence that compounds sits in the orchestration layer. Precedents, review standards, client requirements. That knowledge has to stay editable, auditable and portable. In our system, a changed review standard is an edit that takes effect the same day, with no new model training required. No lawyer should have to worry about which model did the work, any more than they think about which chip is in their laptop. They should only care about the quality of the work. That's what we are focused on. If you want the engineering version of this argument rather than the CEO version, our CPO, Bryan Tsao, and CTO, Jacob Lauritzen, take it apart in the video below.

Max Junestrand

49,391 Aufrufe • vor 17 Tagen

watch this anon. i gave NVIDIA's biggest model ever a single task. 100 minutes and 440,000 tokens later, it had rendered nothing. not one important thing on the screen. this is Nemotron 3 Ultra. 550 billion parameters, a hybrid Mamba Transformer MoE, the largest model NVIDIA has ever shipped, and they built it specifically for long-running agentic coding. so i handed it exactly that: build a 3D scene from a spec, multiple files, iterate until the tests pass. the same task a frontier model one shotted in minutes. i genuinely wanted to be impressed. it ran for an hour and forty. burned through 440,000 tokens. wrote every file, passed its own tests, and proudly printed "task complete."the browser was blank. the 3D scene never rendered. not once. and the long horizon agentic behavior was genuinely good. it stayed on task the whole hour and forty, wrote real multi-file code, drove its own tools without derailing. it just couldn't turn any of that into something that actually runs. here's the part that gets me. it's a text model, it cannot see its own output. so it sat there looping on a broken vision tool, trying to "look" at the page, hitting error after error, never once reasoning its way out. it declared victory on an empty screen because it had no way to know the screen was empty. to be fair, i genuinely don't know what quant the NIM was serving, so maybe some of that's on the serving, not the model. but the biggest model NVIDIA has ever made, on the exact task it was designed for, couldn't tell it had built nothing in 100 minutes. same task on a local model, below thread👇.

watch this anon. i gave NVIDIA's biggest model ever a single task. 100 minutes and 440,000 tokens later, it had rendered nothing. not one important thing on the screen. this is Nemotron 3 Ultra. 550 billion parameters, a hybrid Mamba Transformer MoE, the largest model NVIDIA has ever shipped, and they built it specifically for long-running agentic coding. so i handed it exactly that: build a 3D scene from a spec, multiple files, iterate until the tests pass. the same task a frontier model one shotted in minutes. i genuinely wanted to be impressed. it ran for an hour and forty. burned through 440,000 tokens. wrote every file, passed its own tests, and proudly printed "task complete."the browser was blank. the 3D scene never rendered. not once. and the long horizon agentic behavior was genuinely good. it stayed on task the whole hour and forty, wrote real multi-file code, drove its own tools without derailing. it just couldn't turn any of that into something that actually runs. here's the part that gets me. it's a text model, it cannot see its own output. so it sat there looping on a broken vision tool, trying to "look" at the page, hitting error after error, never once reasoning its way out. it declared victory on an empty screen because it had no way to know the screen was empty. to be fair, i genuinely don't know what quant the NIM was serving, so maybe some of that's on the serving, not the model. but the biggest model NVIDIA has ever made, on the exact task it was designed for, couldn't tell it had built nothing in 100 minutes. same task on a local model, below thread👇.

Sudo su

32,589 Aufrufe • vor 3 Monaten

New Course: Post-training of LLMs Learn to post-train and customize an LLM in this short course, taught by Banghua Zhu, Assistant Professor at the University of Washington University of Washington, and co-founder of @NexusflowX. Training an LLM to follow instructions or answer questions has two key stages: pre-training and post-training. In pre-training, it learns to predict the next word or token from large amounts of unlabeled text. In post-training, it learns useful behaviors such as following instructions, tool use, and reasoning. Post-training transforms a general-purpose token predictor—trained on trillions of unlabeled text tokens—into an assistant that follows instructions and performs specific tasks. Because it is much cheaper than pre-training, it is practical for many more teams to incorporate post-training methods into their workflows than pre-training. In this course, you’ll learn three common post-training methods—Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL)—and how to use each one effectively. With SFT, you train the model on pairs of input and ideal output responses. With DPO, you provide both a preferred (chosen) and a less preferred (rejected) response and train the model to favor the preferred output. With RL, the model generates an output, receives a reward score based on human or automated feedback, and updates the model to improve performance. You’ll learn the basic concepts, common use cases, and principles for curating high-quality data for effective training. Through hands-on labs, you’ll download a pre-trained model from Hugging Face and post-train it using SFT, DPO, and RL to see how each technique shapes model behavior. In detail, you’ll: - Understand what post-training is, when to use it, and how it differs from pre-training. - Build an SFT pipeline to turn a base model into an instruct model. - Explore how DPO reshapes behavior by minimizing contrastive loss—penalizing poor responses and reinforcing preferred ones. - Implement a DPO pipeline to change the identity of a chat assistant. - Learn online RL methods such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), and how to design reward functions. - Train a model with GRPO to improve its math capabilities using a verifiable reward. Post-training is one of the most rapidly developing areas of LLM training. Whether you’re building a high-accuracy context-specific assistant, fine-tuning a model's tone, or improving task-specific accuracy, this course will give you experience with the most important techniques shaping how LLMs are post-trained today. Please sign up here:

New Course: Post-training of LLMs Learn to post-train and customize an LLM in this short course, taught by Banghua Zhu, Assistant Professor at the University of Washington University of Washington, and co-founder of @NexusflowX. Training an LLM to follow instructions or answer questions has two key stages: pre-training and post-training. In pre-training, it learns to predict the next word or token from large amounts of unlabeled text. In post-training, it learns useful behaviors such as following instructions, tool use, and reasoning. Post-training transforms a general-purpose token predictor—trained on trillions of unlabeled text tokens—into an assistant that follows instructions and performs specific tasks. Because it is much cheaper than pre-training, it is practical for many more teams to incorporate post-training methods into their workflows than pre-training. In this course, you’ll learn three common post-training methods—Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL)—and how to use each one effectively. With SFT, you train the model on pairs of input and ideal output responses. With DPO, you provide both a preferred (chosen) and a less preferred (rejected) response and train the model to favor the preferred output. With RL, the model generates an output, receives a reward score based on human or automated feedback, and updates the model to improve performance. You’ll learn the basic concepts, common use cases, and principles for curating high-quality data for effective training. Through hands-on labs, you’ll download a pre-trained model from Hugging Face and post-train it using SFT, DPO, and RL to see how each technique shapes model behavior. In detail, you’ll: - Understand what post-training is, when to use it, and how it differs from pre-training. - Build an SFT pipeline to turn a base model into an instruct model. - Explore how DPO reshapes behavior by minimizing contrastive loss—penalizing poor responses and reinforcing preferred ones. - Implement a DPO pipeline to change the identity of a chat assistant. - Learn online RL methods such as Proximal Policy Optimization (PPO) and Group Relative Policy Optimization (GRPO), and how to design reward functions. - Train a model with GRPO to improve its math capabilities using a verifiable reward. Post-training is one of the most rapidly developing areas of LLM training. Whether you’re building a high-accuracy context-specific assistant, fine-tuning a model's tone, or improving task-specific accuracy, this course will give you experience with the most important techniques shaping how LLMs are post-trained today. Please sign up here:

Andrew Ng

125,146 Aufrufe • vor 1 Jahr

🎙️What’s changing with the Championship Factor, and what does it mean for your school? At its June meeting, the MSHSAA Board of Directors adopted a new Championship Factor model. In our latest episode, MSHSAA leadership explains what led to the change, how the new model works, and what schools need to know moving forward. Listen here:

🎙️What’s changing with the Championship Factor, and what does it mean for your school? At its June meeting, the MSHSAA Board of Directors adopted a new Championship Factor model. In our latest episode, MSHSAA leadership explains what led to the change, how the new model works, and what schools need to know moving forward. Listen here:

MSHSAA

14,848 Aufrufe • vor 1 Monat

Candle Range Theory is a powerful trading model; however, some traders make it complicated and confusing when it comes to actual trading. So, in this video, we’ll simplify the CRT trading model, explain the reasoning behind it, show you how to spot it correctly on the chart, and reveal the best CRT trading strategy. Watch 😊

Candle Range Theory is a powerful trading model; however, some traders make it complicated and confusing when it comes to actual trading. So, in this video, we’ll simplify the CRT trading model, explain the reasoning behind it, show you how to spot it correctly on the chart, and reveal the best CRT trading strategy. Watch 😊

CRT ALPHA👑👑

13,293 Aufrufe • vor 2 Monaten

A wild chimpanzee displayed a unique and touching behavior. He borrowed a photographer's hand to collect water from a puddle and drank it calmly. Afterward, the chimpanzee returned the favor by cleaning the man's hands with the same water, as if understanding and appreciating the assistance. This rare moment demonstrates the chimpanzee's remarkable intelligence, empathy, and social behavior.

A wild chimpanzee displayed a unique and touching behavior. He borrowed a photographer's hand to collect water from a puddle and drank it calmly. Afterward, the chimpanzee returned the favor by cleaning the man's hands with the same water, as if understanding and appreciating the assistance. This rare moment demonstrates the chimpanzee's remarkable intelligence, empathy, and social behavior.

ᑭEGEᒪ ᗰᒪᗩᖇᗩT

140,755 Aufrufe • vor 9 Monaten

Canada should be one of the wealthiest countries in the world. Canada needs to stop focusing on politics and get on with the task at hand.

Canada should be one of the wealthiest countries in the world. Canada needs to stop focusing on politics and get on with the task at hand.

James E. Thorne

26,753 Aufrufe • vor 1 Monat

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

14,222 Aufrufe • vor 4 Monaten

you rewrote a line in your claude.md this week to get the tone right there is a longer document sitting in front of it, and you have never opened it every tool you connected shipped a description, and a description is not a label, it is prose in second person addressed to the model i printed one this morning: two thousand seven hundred and eighty one characters, with a section headed when to use this tool it tells the model what to reach for, and when to stop and think first you spent an afternoon on your voice a package you installed with npx spent a page on its, and its page is read before yours you are not configuring an agent you are the second author on a document you have never read

you rewrote a line in your claude.md this week to get the tone right there is a longer document sitting in front of it, and you have never opened it every tool you connected shipped a description, and a description is not a label, it is prose in second person addressed to the model i printed one this morning: two thousand seven hundred and eighty one characters, with a section headed when to use this tool it tells the model what to reach for, and when to stop and think first you spent an afternoon on your voice a package you installed with npx spent a page on its, and its page is read before yours you are not configuring an agent you are the second author on a document you have never read

Lagosaur

10,012 Aufrufe • vor 1 Monat

How dangerous is Mercedes' strategy? Mercedes new strategy could dramatically change not just Germany's economy, but Europe's as a whole. After all, the automotive industry is the key economic sector. Mercedes is largely pulling out of the lower segments of small and cheaper cars to refocus on the luxury market. But will this strategy turn out to be successful? And what does it mean for you? What can you learn from it? Let's dig into these questions together and find the answers. More on this in the video. #mercedes #ElectricVehicles #automobile #foresight

How dangerous is Mercedes' strategy? Mercedes new strategy could dramatically change not just Germany's economy, but Europe's as a whole. After all, the automotive industry is the key economic sector. Mercedes is largely pulling out of the lower segments of small and cheaper cars to refocus on the luxury market. But will this strategy turn out to be successful? And what does it mean for you? What can you learn from it? Let's dig into these questions together and find the answers. More on this in the video. #mercedes #ElectricVehicles #automobile #foresight

Dr Pero Micic

23,542,548 Aufrufe • vor 3 Jahren

“You take a a Rolex Daytona today, and you look back at the first one in 1963. To me, the one in 1963 is better aesthetically. Because its the purest form of the concept. The purest form of the idea. Everything else from there has been layered on because they need to sell more and they need to come up with a new version and new model. This is what happens. They couldn't still sell that first version. That's not the way the world works. You have to keep making new stuff. The pure idea, the pure form of the thing [is best]”

“You take a a Rolex Daytona today, and you look back at the first one in 1963. To me, the one in 1963 is better aesthetically. Because its the purest form of the concept. The purest form of the idea. Everything else from there has been layered on because they need to sell more and they need to come up with a new version and new model. This is what happens. They couldn't still sell that first version. That's not the way the world works. You have to keep making new stuff. The pure idea, the pure form of the thing [is best]”

David Senra

105,087 Aufrufe • vor 7 Monaten