Deep learning curriculum for robotics. [📍 Link to Playlist below ] Modern robotics workflows often integrate deep learning models with traditional algorithms for mapping, localization, and control. This playlist builds it from scratch: Single neurons → Backprop → CNNs →

Hardware kit for robot learning https://t.co/dHKTVP36Wp

T800 officially steps into the ring. 🤖🥊 Just went a few rounds with Muay Thai legend Banchamek. How would you rate its performance?!

Clawdbot 😂

robotics is just software + physics

@stepjamUK He saw the best minds in the world wasting years on plumbing. At the Dyson Robot Learning Lab, he realized: "We are all solving the same boring problems instead of solving intelligence." So he founded @Neuracore_AI Watch the complete interview with him here:

If you work in Robot Learning, you probebly know this name: @stepjamUK • PhD from Imperial College (Dyson Lab) • Postdoc at Berkeley (with Pieter Abbeel) • Creator of RLBench (the standard benchmark for robot learning)

It’s like building your own database before building a web app. Insane. Yet every robotics team, from university labs to warehouse giants, is rebuilding the same pipeline from scratch. T his is why "Sim-to-Real" is so hard. The plumbing is broken.

If you start a robotics company today, you don't start by building robots. You start by building Tools. You need a way to record data. Visualize it. Sync 12 different sensors. Train models. Deploy them.... It takes months. And it adds zero value to your

Robotics is obsessed with foundation models and humanoids. It’s missing the most critical piece. One founder just raised $3M to build the “AWS for robots.” Fixing the silent bottleneck for most robotics startup: Data Infrastructure

@JessePeltan More people should visit China

The Future is Embodied 🚀 The next decade belongs to robots that can see, feel, and learn. Key Components of Robotics Igniting Old Industries: Chips: The brain of robotics, enabling intelligent decision-making. IoT (Internet of Things): Connecting machines and systems for

The "Why" – Market Dynamics & Risks 📈 The numbers are staggering: $327B by 2034. Opportunities: Predictive AI is growing at 16.8% annually. Bubble Risks: We watch for over-valuation in companies without clear commercialization paths. The Play: Betting on the "picks and

The "How" – The Hardcore Supply Chain ⚙️ Hardware is the new software. Key components define the moat: Core Components: Chips, CNC precision, and IoT connectivity. Sensors: Environmental interaction is the biggest bottleneck; advanced touch and vision sensors are the "eyes"

The "Brain" – Data and Physical AI 🧠 The shift from "Model Creators" to "Real-World Infrastructure" is the 2026 frontier. Data Collection: The "sim-to-real" gap is closing. Model Architecture: Moving toward foundation models for robotics (Analytical AI). Migration: How easily

The "Who" – Standards and Benchmarking 📊 How do VCs evaluate a robotics startup? Revenue First: In a high-interest environment, proof of commercial traction is non-negotiable. The Universe Tree: We deconstruct the ecosystem from "Intelligent Elements" (brains) to "Physical

The "Where" – High-ROI Landing Scenarios 📍 Robotics is moving beyond the cage. We focus on "Budget, Necessity, and Imperfection." Service: High demand in care/companion services and EHS digitalization. Strategy: Look for "unperfect" solutions that solve "perfect" needs

2026 is the year humanoids go mainstream

UK-based startup 'Humanoid' announced KinetIQ, an AI framework with a Vision-Language-Action (VLA) model at its core.

Elon : For Optimus, it’s difficult to match the flywheel of the existing vehicle fleet. Robots will work in areas where "Humans can do it, but machines are hard to retrofit."