正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

How robust can model predictive control be if we can solve each trajectory optimization to global optimality? On the contact-rich push-T problem, we show that model-based global optimization is so robust that it never fails, even if the model is not even correct! We achieve global optimality via sparse... show more

Heng Yang

4,874 subscribers

28,660 次观看 • 1 年前 •via X (Twitter)

科学技术新闻政治教育

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Malaria is a complex & unforgiving disease, but one that we can end if we work together. Fredros Okumu & JenHatmaker discuss the global impacts of malaria and how we can advance progress against it on this episode of “Make Me Care About…”

Malaria is a complex & unforgiving disease, but one that we can end if we work together. Fredros Okumu & JenHatmaker discuss the global impacts of malaria and how we can advance progress against it on this episode of “Make Me Care About…”

Gates Foundation

496,642 次观看 • 3 年前

Evaluating policies on a real robot can be painful. Can we use a world model to get a rough estimate of how good a policy is? Checkout "Evaluating Robot Policies in a World Model". Paper: Demo: Code:

Evaluating policies on a real robot can be painful. Can we use a world model to get a rough estimate of how good a policy is? Checkout "Evaluating Robot Policies in a World Model". Paper: Demo: Code:

Sherry Yang

36,568 次观看 • 1 年前

Norman Powell on the trade deadline: “We just focus on the moment and what we can control and if moves do happen we support and if they don’t — we lock in with the group that we have and go out there and try to get wins.” (via Zachary Weinberger)

Norman Powell on the trade deadline: “We just focus on the moment and what we can control and if moves do happen we support and if they don’t — we lock in with the group that we have and go out there and try to get wins.” (via Zachary Weinberger)

𝙃𝙚𝙖𝙩𝘾𝙪𝙡𝙩𝙪𝙧𝙚

13,117 次观看 • 4 个月前

🚨How do LLMs acquire human values?🤔 We often point to preference optimization. However, in our new work, we trace how and when model values shift during post-training and uncover surprising dynamics. We ask: How do data, algorithms, and their interaction shape model values?🧵

🚨How do LLMs acquire human values?🤔 We often point to preference optimization. However, in our new work, we trace how and when model values shift during post-training and uncover surprising dynamics. We ask: How do data, algorithms, and their interaction shape model values?🧵

Mehar Bhatia

41,217 次观看 • 7 个月前

#Habs Mike Matheson on officiating "That is a good question, and we are wondering ourselves. But it is out of our control, so it is not something to worry about. We need to keep playing and keep focusing on the things we can control." #GoHabsGo #NHL #Hockey

#Habs Mike Matheson on officiating "That is a good question, and we are wondering ourselves. But it is out of our control, so it is not something to worry about. We need to keep playing and keep focusing on the things we can control." #GoHabsGo #NHL #Hockey

Chris G

20,359 次观看 • 1 个月前

“Last night BTS’ fans ever since we announced it. The ARMY have already raised over $10k just like that just by going online & fundraising. We didn’t even ask them to do so. The ARMY are so amazing.” - Hugh Evans (Founder & CEO of Global Citizen) via Billboard News

“Last night BTS’ fans ever since we announced it. The ARMY have already raised over $10k just like that just by going online & fundraising. We didn’t even ask them to do so. The ARMY are so amazing.” - Hugh Evans (Founder & CEO of Global Citizen) via Billboard News

lala⁷ ⊙⊝⊜

57,868 次观看 • 1 个月前

Mark Carney, "Canada can’t solve all the world’s problems" "But we can show that another way is possible" "That the arc of history isn’t destined to be warped towards authoritarianism and exclusion" "It can still bend towards progress and justice"

Mark Carney, "Canada can’t solve all the world’s problems" "But we can show that another way is possible" "That the arc of history isn’t destined to be warped towards authoritarianism and exclusion" "It can still bend towards progress and justice"

Farrukh

25,710 次观看 • 5 个月前

we're never getting a model that can do UX, are we?

we're never getting a model that can do UX, are we?

Codetaur

178,270 次观看 • 16 天前

Judging by this pattern... Are y'all telling me that if America leaves the UN... we could literally achieve global peace?

Judging by this pattern... Are y'all telling me that if America leaves the UN... we could literally achieve global peace?

Kendrick

513,174 次观看 • 4 个月前

Maduro: The petroleum stream deformed everything. We did not choose to have a model that depended on petroleum. We did not decide that. When I was born in the year ’62, they had already imposed the model of capitalism that depended on petroleum, and we were a petroleum colony of the United States.

Maduro: The petroleum stream deformed everything. We did not choose to have a model that depended on petroleum. We did not decide that. When I was born in the year ’62, they had already imposed the model of capitalism that depended on petroleum, and we were a petroleum colony of the United States.

Clash Report

13,954 次观看 • 5 个月前

This is amazing! Someone trained a 1.5B model (with GRPO) to solve mazes! model:

This is amazing! Someone trained a 1.5B model (with GRPO) to solve mazes! model:

Victor M

139,407 次观看 • 1 年前

Blackrock CEO Larry Fink moments ago: "One of the main foundations of the US is that we are a global stabilizer. Obviously in the last week we have not been a global stabilizer. Is this going to persist? Are there going to be new global stabilizers in the world?"

Blackrock CEO Larry Fink moments ago: "One of the main foundations of the US is that we are a global stabilizer. Obviously in the last week we have not been a global stabilizer. Is this going to persist? Are there going to be new global stabilizers in the world?"

Ed Krassenstein

122,214 次观看 • 1 年前

I put a lens after the single slit and see how aperture size affects the focusing of light, and we can observe that a larger aperture can achieve a better focus. That got me thinking, if we want to pinpoint a problem, it's better to view it from multiple perspectives. #optics

I put a lens after the single slit and see how aperture size affects the focusing of light, and we can observe that a larger aperture can achieve a better focus. That got me thinking, if we want to pinpoint a problem, it's better to view it from multiple perspectives. #optics

Hans Chiu

26,483 次观看 • 1 年前

ICML 2026: Latent Reasoning in TRMs is Secretly a Policy Improvement Operator Why does recursive reasoning, especially latent reasoning, actually work? The theory is still young, and even mechanistic explanations are limited. We close part of this gap by showing that latent reasoning is secretly doing policy improvement. Each recursion pushes the model steadily toward the target. Based on this view, we propose an algorithm that boosts learning and inference efficiency by up to 18x.

ICML 2026: Latent Reasoning in TRMs is Secretly a Policy Improvement Operator Why does recursive reasoning, especially latent reasoning, actually work? The theory is still young, and even mechanistic explanations are limited. We close part of this gap by showing that latent reasoning is secretly doing policy improvement. Each recursion pushes the model steadily toward the target. Based on this view, we propose an algorithm that boosts learning and inference efficiency by up to 18x.

Arip

23,733 次观看 • 9 天前

Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)

Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)

Animesh Garg

52,279 次观看 • 2 年前

Excited to release Minimalist Compliance Control! We achieve robust, compliant robot interaction across robot arms, dexterous hands, and humanoids, with NO force sensors or learning. If you’re wondering what remains, please see the thread below😉 Website:

Excited to release Minimalist Compliance Control! We achieve robust, compliant robot interaction across robot arms, dexterous hands, and humanoids, with NO force sensors or learning. If you’re wondering what remains, please see the thread below😉 Website:

Haochen Shi

30,749 次观看 • 3 个月前

Each of these homes were built in a factory in 2 hours, and assembled on-site in 3 hours. This is the type of innovation Canada is embracing in order to build the homes we need, faster. Canadian industry and Canadian workers have the creativity and ambition required to meet this moment. By working together, we can, and we will, solve the housing crisis.

Each of these homes were built in a factory in 2 hours, and assembled on-site in 3 hours. This is the type of innovation Canada is embracing in order to build the homes we need, faster. Canadian industry and Canadian workers have the creativity and ambition required to meet this moment. By working together, we can, and we will, solve the housing crisis.

Sean Fraser

347,082 次观看 • 2 年前

Rubio on Syria: We like the trajectory. We have to keep it on that trajectory. We've got good agreements in place. The key now is implementation, and we'll be very involved in that regard.

Rubio on Syria: We like the trajectory. We have to keep it on that trajectory. We've got good agreements in place. The key now is implementation, and we'll be very involved in that regard.

Open Source Intel

15,675 次观看 • 4 个月前

Rubio on Syria: We like the trajectory. We have to keep it on that trajectory. We've got good agreements in place. The key now is implementation, and we'll be very involved in that regard.

Rubio on Syria: We like the trajectory. We have to keep it on that trajectory. We've got good agreements in place. The key now is implementation, and we'll be very involved in that regard.

Clash Report

15,080 次观看 • 4 个月前