What would robotics look like if everything worked perfectly? The trick is to work backward from that vision.

1. Research. 🎯 Goal: Discover new principles or models.📦 Output: Papers, prototypes, foundational ideas.⚠️ Risk: High, but creates the future. “No real users yet, just an intuition and a theory & may be a POC.”. ——-. 2. Technology Development.🎯 Goal: Make research usable at.

🤖 Why can’t robots see and manipulate yet? (🔔Just a Reminder).Because physical difficulty ≠ robotic difficulty. What feels easy to humans is still insanely hard for machines. It still requires:. ✅ High-quality 3D perception in cluttered, dynamic environments.✅

I get asked many times what is the best way to learn CUDA ? If you are super new to CUDA then you can follow NVIDIA's official learning path .👨🏽‍💻 NVIDIA: There are books on CUDA but I personally find them little outdated (still worth reading but can be

Concepts > syntax. Welcome to the polyglot era. AI is killing ‘I’m a Python dev’ vibes. The identity of being tied to one language (like “Python developer”) is becoming outdated. Now you’re just a “developer.” LLMs handle syntax, you handle concepts. Your job is to understand.

Everyone's talking about AI breakthroughs, but here's why we haven't seen an "OpenAI moment" for robotics yet and what needs to happen before we do. While there's incredible progress happening, we're still missing some critical pieces:. The Physical World is Hard. AI may be

☕️ Interesting Sunday morning’s ☀️ read. This paper introduces UltraMem, a new way to make big AI models faster and more efficient. As AI models get bigger, they need more computing power and memory, which makes them slow and expensive to run. A popular method called Mixture of

Are Generalist AI Models the Future of Robot Learning?. Reinforcement learning (RL) has led to impressive robot demos, but deploying these controllers in the real world remains challenging. Training a policy in simulation is one thing, making it work reliably on a physical robot

Build → Measure → Learn. There are 52 weeks in a year. ->> You might lose 1-2 weeks to being sick. ->> Another 1-2 weeks might be bad days/unproductive. ->> Add 2-3 weeks for holidays and breaks. That still leaves you with ~45 weeks to build and experiment. In today’s world,.

RT @ClementDelangue: The future of robotics is open! . Excited to see Pi0 by @physical_int being the first foundational robotics model to b….

Handy Notebook:

Let’s keep the politics aside for the time being and let’s just appreciate the Key technical Innovations that DeepSeek-R1 and DeepSeek-V3 brought into the AI world which helped them compete with and even surpass ( in some cases ) OpenAI’s Models 🚀 . No matter what’s your view.

🚀How Cold-Start Data Made DeepSeek-R1 Better?. It seems to be very clear from the paper that cold-start data played a big role in improving DeepSeek-R1 by addressing the weaknesses of its predecessor, (DeepSeek-R1-Zero), and boosting its reasoning abilities. 🤔 But first, what

How DeepSeek used distillation to make Smaller Models Smarter?. Lately, there’s been a lot of talk about DeepSeek’s innovative methods. One specific approach standout is how they used distillation to make smaller AI models just as smart as the big ones. Big models are powerful,

🚀 NVIDIA’s Cosmos: A New Era for Robotics Training and Simulation. Robotics is entering a new era, thanks to NVIDIA's Cosmos World Foundation Model Platform. It is a game-changing tool that makes training robots faster, safer, and smarter. Designed for robotics and other

I think people often believe intuition is some kind of magic.But really, it comes from learning a lot, spotting patterns, and using past experiences to make quick decisions. It’s not about guessing, it’s about figuring out what’s most likely right based on what you already know.

Aligning text, images, and videos into a common embedding space is still a fundamental problem in multimodal learning (Despite of all the ongoing progress).

As we look at the progress in AI, it’s worth asking: are benchmarks truly the right way to measure meaningful advancements? Too often, we see AI models celebrated for beating synthetic tests or solving niche challenges, but what does that actually translate to in the real world?.