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The models are getting better everyday. This is a pretty contact-rich task, and forces us to reckon with the fact that our policies not only need to learn the quirks of different robots, but also have to do it in a way that generalizes to the fine-grained subtleties of...

61,885 просмотров • 2 месяцев назад •via X (Twitter)

Комментарии: 20

Фото профиля Albert Wenger 🌎🔥⌛
Albert Wenger 🌎🔥⌛2 месяцев назад

Thrilled to see it!

Фото профиля Caitlin Kalinowski
Caitlin Kalinowski2 месяцев назад

Impressive.

Фото профиля Jyothi Swaroop Kasina
Jyothi Swaroop Kasina1 месяц назад

Amazing how naturally the robot performs. Curious to get your thoughts on how task level reasoning would eventually come in the GEN Models. Would a separate model eventually sit on top of GEN-1 for longer horizon tasks, the way Gemini Robotics paired ER 2 with a VLA?

Фото профиля Arash Tajik
Arash Tajik2 месяцев назад

impressive.

Фото профиля clankr
clankr2 месяцев назад

What really matters at this point is how much of it was actually achieved in one shot.

Фото профиля Psalter
Psalter1 месяц назад

I think robotic arms will be the first large-scale robots that people use in their work and daily lives

Фото профиля Xingxin HE
Xingxin HE1 месяц назад

Wow! Curious if this is the contribution mainly from the post-training team?

Фото профиля Saboor
Saboor21 дней назад

This is getting kinda crazy

Фото профиля Zu
Zu2 месяцев назад

Impressive!

Фото профиля Leo Lin
Leo Lin1 месяц назад

I think friction is where robotics benchmarks meet reality. A policy that ignores material, pressure, wear, and contact variation may look intelligent in simulation but remain brittle on the factory floor.

Фото профиля Continuum Labs
Continuum Labs1 месяц назад

The biggest breakthroughs in robotics will come from mastering the physics of the real world. Exciting progress 👏

Фото профиля Andrew Johnson
Andrew Johnson2 месяцев назад

the threaded nut is fascinating because the ending torque influences where it’ll end up

Фото профиля Viktor Smirnov
Viktor Smirnov1 месяц назад

We operate a permitted commercial kitchen in SF serving real paying customers, and we're opening it up as a live training and evaluation site for robots (real orders as ground truth, egocentric capture from working cooks, food-safe protocols handled by us). Nobody's put a humanoid in front of a paying customer's order yet would love to show you what that could look like. Open to 15 min?

Фото профиля John Hanzl
John Hanzl2 месяцев назад

Oh - I see - they’re practicing for harvesting human organs. Tracks. AI’s can’t get embodied fast enough…

Фото профиля Aryan Dhawan
Aryan Dhawan27 дней назад

The wedging case is a good test because a visually plausible trajectory can still be wrong once friction changes. Recovery after that kind of failure would be really informative.

Фото профиля Andile (Ethan)
Andile (Ethan)2 месяцев назад

What gripper is this?

Фото профиля Arnon ∈ ℝ³
Arnon ∈ ℝ³1 месяц назад

x algo doing it's work

Фото профиля Premium Domain Broker
Premium Domain Broker1 месяц назад

The future of intelligent vision has a name. A powerful brandable domain for AI-powered robotics, computer vision, autonomous systems, industrial inspection, smart cameras & next-generation automation. Short. Memorable. Built for the AI + Robotics era. 🚀 💬 Available for acquisition — DM for details.

Фото профиля The Embodied Era
The Embodied Era2 месяцев назад

Actuator-level adaptation seems to be the hard part. Almost everyone ships one model per platform, which is why nobody in robotics has software margins. If GEN-1 moves across actuators without retuning, the moat stops being the robot. Precision is downstream of that.

Фото профиля Huiyi Li
Huiyi Li22 дней назад

通用人工智能领域的联合创始人,好优秀!真诚想认识一下,交个朋友✨

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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 subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Khurram Javed

52,110 просмотров • 10 месяцев назад