Train a TensorFlow object detection model – then deploy... it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:show more

Yeah! Teleoperation on low-cost robot arm from Tau Robotics... working natively at 200Hz with and a very simple code. We are working a video tutorial to make it easy for anyone to reproduce our setup (in simulation or with a real robot). Stay tuned 😋show more

New in LeRobot: DM0.5 by Dexmal 🤖 A 4B... VLM with a 680M flow-matching action expert, pretrained on 7 robot embodiments. Right now it's the top open-source model on RoboColiseum. Fine-tune it on your robot: --policy.path=lerobot/dm05_base Docs 👇show more

NVIDIA finally released Neuralangelo's source code! The model can... turn videos from any device into detailed 3D structures, fully replicating buildings, sculptures, or other real aworld objects or spaces virtually. Here's how it works: A model utilizes a 2D video with multiple angles of an object or scene. I selects frames from different viewpoints to understand depth, size, and shape. The AI creates an initial 3D representation, similar to a sculptor shaping a subject. The render is optimized to enhance details, like a sculptor refining texture. The outcome is a 3D object or scene suitable for virtual reality, digital twins, or robotics.show more

Wow Nvidia has just released a tool to turn... any image into an explorable 3D world And they made it 100% open source 🔥 - Model available on Hugging Face - UI code available on GitHub Lyra 2.0 creates a world you can walk through, turn around in, and even drop a robot into for simulation.show more

Pothole detection on the road in real time using... Ultralytics YOLO26! 🕳️ Manual road inspections are slow, costly, and hard to scale. With object detection, potholes can be identified directly from street-level images or video feeds, enabling faster and more consistent road condition monitoring. How I built this demo: ✅ Trained a segmentation model on a custom dataset. ✅ Generated mask contours for each pothole. ✅ Leveraged the onnx-exported model for faster processing. #Pothole #RoadDamage #AIshow more

img2threejs - turn one object photo into a code-only... procedural Three.js model Open-source toolkit that rebuilds the object in a single reference image as procedural Three.js code (no mesh downloads), quality-gated by a render-vs-reference loop - strong for hard-surface objectsshow more

This dev built a prototype soccer game using the... new Meta Quest camera API to track a real ball with simple color detection and it actually works surprisingly well! Source:show more

Meta's SAM 3 is a beast. before: collect data,... train custom object detector, use tracker to estimate object motion - days now: track anything with text prompt - seconds real-time object detection will never be the same.show more

JUST IN: Reimagine Robotics has just emerged from stealth!... 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →show more

Super excited about Hydra-0 from Hongyu Li and team!... The key idea is to use flow as a shared visual interface across embodiments/objects for controllable video generation, allowing a single generalist world model to learn from human, handheld-gripper, and robot interaction data. My favorite result is the video below: start from a real video of a human doing the task (left), extract the desired object flow, and condition the model on that flow (right). The model then hallucinates a plausible robot motion that could produce the same object motion. Very cool glimpse of how a generalist world model can bridge human demonstrations and robot control.show more

Object tracking with FastSAM using Ultralytics Python package 🎉💪... FASTSAM-s model is employed for real-time object detection, combined with botsort object tracking to further boost performance. 🐻 Code link 👇 #FastSAM #MachineLearning #Researchshow more

A simple MPC controller for path tracking implemented in... python. Few resources actually teach it without assuming a PhD first. This open-source project does exactly that: An iterative MPC tracking controller (built with CVXPY and MuJoCo) that shows how to make a robot (or autonomous vehicle) follow a path and avoid obstacles in real time. → Autonomous logistics, robot navigation, vehicle control; there's a path-following algorithm behind all of it. → Most implementations are black boxes. This one shows you what's actually happening. → It uses convex optimization (no non-linear solver), and still handles real vehicle physics through iterative linearization. What makes it practical is that you can run it with the mushr car model in MuJoCo, or headless without any physics simulation if you just want to understand the logic. If you're making the jump from classical control to real-time optimisation, this is a very good starting point. 📌 Repository: ——- Weekly robotics and AI insights. Subscribe free:show more

High school students built an autonomous ball-collecting robot! 🎾... A group of high school students built a robot that picks up balls and shoots them into a bin while moving without stopping, with impressive speed and accuracy. It combines mechanical design, sensors, and software making constant adjustments in real time while the robot is driving. When teenagers can build systems this sophisticated, the talent pipeline for the robotics industry is accelerating! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →show more

🤖Why is robot manipulation still an open challenge? This... video shows a kitting task -- packing multiple items into a single product. No robot today can do this autonomously. Big challenges = big opportunities for research and industry. #robotics #manipulation #automationshow more

😢 Sad: in Miami, a delivery robot decided to... end its life by waiting for a train on the tracks “A rare hardware malfunction occurred in the robot,” a spokesperson for Coco Robotics told People.show more

Imagine having a ping pong robot! 🏓 Researchers and... developers building physical AI: meet Reachy 2 from Pollen Robotics, an open-source, humanoid robot for real-world experimentation. It’s a bimanual mobile manipulator: each 7-DOF arm mimics human proportions and can lift up to 3 kg, giving dexterity for object handling. It can be controlled with Python and ROS2 Humble, or go straight into VR teleoperation, use a headset to move Reachy’s arms, hands, and head, and see through its cameras as if you’re in the robot’s own body. Want it to move around? A mobile base with three omnidirectional wheels, rich sensors, and LiDAR lets Reachy 2 navigate and explore its surroundings smoothly. 🗺️ Under the hood, it’s powered by a CPU system that’s ready for machine learning, perfect for loading AI frameworks and testing new models from Hugging Face directly on the robot. Keep making robots more, and more accessible Pollen team! ... and keep making more open source models to make robots more mainstream clem 🤗!show more

Ready to bring AI into the physical world? 🤖... Join our livestream on Tuesday, August 11th at 9:00am PT to learn how to: 🧠 Run and fine-tune NVIDIA GR00T 1.7 🔗 Connect models, sensors, and actuators with ROS 2 🦾 Deploy a model on the SO-101 robot arm to execute a real-world task 🎙️Guest speaker Nikodem Bartnik will also share the latest advances and highlights from the open-source robotics community. Add to calendar:show more

MolmoAct2 is landing in LeRobot! Ai2's open Action Reasoning... Model combines a Molmo2-ER vision-language backbone with a flow-matching continuous action expert to predict robot action chunks from images, language instructions, and proprioceptive state. An open robot foundation model built for real-world control, with strong out-of-the-box performance and easy fine-tuning in LeRobot. Pick-and-place inference running on NVIDIA DGX Spark! Blog: Paper: Thanks to Ai2 Jiafei Duan Haoquan Fangshow more

The fastest way to learn Claude Code isn't the... docs. It's asking Claude. Just ask how do hooks work. Then ask it to write you a hook. Then ask it to turn your release checklist into a skill. It knows itself better than any tutorial.show more