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Evaluation is a critical bottleneck in building robot foundation models. Check out our latest work RoboLab, led by Jenai Xuning Yang, which addresses this exact challenge. Its a high-fidelity simulation environment for testing these models. A truly generalist policy should be able to complete these tasks zero-shot, and this...

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Ankit Goyal

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30,015 Aufrufe • vor 5 Monaten •via X (Twitter)

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Profilbild von Emerson S
Emerson Svor 5 Monaten

@xuningy You can’t improve what you can’t measure

Profilbild von Sannia Ali 🇵🇰🇺🇸
Sannia Ali 🇵🇰🇺🇸vor 5 Monaten

@xuningy Impressive work tackling evaluation challenges in robot foundation models through high fidelity simulation. How close are we to reliable zero shot performance? 👏

Profilbild von Anjali Yadav
Anjali Yadavvor 5 Monaten

@xuningy How do you see RoboLab helping bridge the gap between simulation and real world robot performance? Any plans for physical robot testing?

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We have 6 different models of CNC machines already designed and validated for production but we would need crores to scale up production and offer it to other manufacturers. Hence, we only build machines for our own production facility. Capital is a bottleneck for us. Currently we just have a tiny space within our own factory which is our R&D lab. We are a team of mere three people. Scaling hardware isn’t easy but it’s something that must be done for us to truly be self-reliant. Really appreciate your efforts Sridhar Vembu to build these hardware tools here. We must build more factories and hardware products in the country. Long way to go before I sleep.
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39,960 Aufrufe • vor 11 Monaten

We also worked with the folks at JigSpace to view one of our scans in 3D (this is a Luer lock–check out the thread for more on how these ingenious medical connectors work).
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We also worked with the folks at JigSpace to view one of our scans in 3D (this is a Luer lock–check out the thread for more on how these ingenious medical connectors work).

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44,651 Aufrufe • vor 2 Jahren

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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

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World Labs CEO Dr. Fei-Fei Li & SceniX Co-Founder Yunzhu Li on the data bottleneck in robotics and how world models help: "The lack of data in training, the lack of data in evaluation, this is very, very different from language models, where data is abundant on the internet." "And we know that in order for robotics to work, we have to somehow unlock the power of scaling law. But where does that come from?" "It's a profound problem that everybody's battling with in robotics." "We see a lot of unlock in being able to do this whole process through the modeling of the environments." "Being able to create these digital worlds... that's just going to unlock so much more potential for being able to replace all the costly and the unsafe data in the real environments with the data generated from the worlds for robots to be able to do scalable learning and evaluations." Fei-Fei Li Yunzhu Li martin_casado

a16z

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20,661 Aufrufe • vor 2 Monaten