Deplace AI

Helping companies monitor, optimize, and automate industrial workflows with AI & Robotics

[Deplace AI @DeplaceAI]

@omnia_io @IlirAliu_ That’s another solid source, a lot of promise there.

[Deplace AI @DeplaceAI]

Thank you for sharing our work @IlirAliu_

Human motion holds a lot of knowledge to fuel Physical AI!

[Ilir Aliu @IlirAliu_]

You will soon be able to teach robots what human are doing… using natural language.

I spoke to one of the founders a couple of months ago on my podcast:

An API that takes raw human videos and returns detailed language annotations describing the motion.

@DeplaceAI is building something wild:

Not just basic labels, it captures:

✅ motion semantics
✅ relative positions
✅ cause and effect
✅ task outcomes
✅ and more

It’s built on top of research in point tracking, segmentation, and learning from demos;

all to make it easier to train robots and embodied agents without manual labeling.

They’re offering early access and sharing some of the datasets they collected via their global network of video collectors.

Thanks for sharing, @MilcentPedro !

🔗 https://t.co/Gu3DvSrwrj

One of the most interesting motion-to-language interfaces I’ve seen.

If you’re working in robotics, vision, or LfD, it’s worth checking out.

[Pedro Milcent @MilcentPedro]

First day at @VivaTech & @NVIDIAGTC with an interesting keynote from Jensen and with #robotics & Physical AI companies represented.

Not every day you see @Tesla_Optimus and @engineered_arts under the same roof!

[Pedro Milcent @MilcentPedro]

We have been seeing great progress in using human demos in Physical AI models.

This paper by @ryan_hoque, @peide_huang, and team shows how a large-scale dataset can help scale this: 800+ hours of human demos for manipulation.

📄: https://t.co/ZId2at7VOZ

[Pedro Milcent @MilcentPedro]

Great new paper by @LihanZha, @ApurvaBadithela, @mzhangio, @justinlidard, @Majumdar_Ani, @allenzren, @shahdhruv_, and team.

As we scale data collection for robotics, it must be done intelligently by choosing which data variations to focus resources on. This paper provides a great foundation for that, will definitely be using it at @DeplaceAI 📈

Check it out: https://t.co/EYD752xoX4

[Pedro Milcent @MilcentPedro]

DexWild is a great new paper by @deepakpathak, @mohansrirama, @_tonytao_ , and team. It introduces the DexWild system to collect human demos at scale, enabling robust #robot policies that generalize to novel environments, tasks, and embodiments. Highly relevant to our data work at @DeplaceAI 🤖

[Pedro Milcent @MilcentPedro]

New paper from @pabbeel, @junyi42 & team! They show how visual imitation enables context-learning for humanoids, using a Real2Sim2Real pipeline that turns videos into transferable skills 📹

👉 https://t.co/BkflcSJvjZ

[Deplace AI @DeplaceAI]

Deplace is glad to be a part of the NVIDIA Inception Program, accelerating the future of Physical AI with powerful tools and support from @nvidia!

[Pedro Milcent @MilcentPedro]

We’re happy to join the Nvidia Inception Program! Excited to accelerate the development of our robotics #data solutions with the support of @nvidia. They have been doing great work supporting Physical AI innovation🤖🚀

[Pedro Milcent @MilcentPedro]

🎥 Kicking off our first 1-minute Physical AI paper review with: Physics-Driven Data Generation for Contact-Rich Manipulation

A great paper by @lujieyang98, @hjterrysuh, @tarikkelestemur, @RussTedrake, Tong Zhao, Bernhard Paus Græsdal, Jiuguang Wang & Tao Pang.

📄 Check it out: https://t.co/RIlHd9n5w0

[Pedro Milcent @MilcentPedro]

Chatted with @IlirAliu_ about what we’re building @DeplaceAI, how our team got together, and the founder life in the Paris #robotics scene. Check it out 👇