NEURA Robotics has unveiled the 4NE1 Gen 4. The new version is 20% lighter, with a slimmer body, black textile covering and upgraded hands for more precise gripping. One detail worth watching is its “smart limbs.” Each limb has local reflexes and can respond independently to changes around it. The robot runs on Neuron AI, combining 3D vision, touch, force, audio and internal body-state data. It learns from real robot data, human demonstrations and simulation. Skills can also be shared across connected robots through the Neuraverse platform. The 4NE1 Gen 4 is already operating in NEURA’s training facilities and is being prepared for series production.

Ever wondered how many different robotics problems one company can tackle? Pollen Robotics has developed a range of projects covering humanoid robots, locomotion, grasping and robot learning. Here are 6 projects to know: 1. Reachy 2: A humanoid robot for research, manipulation and human-robot interaction. 2. Reachy Mini: A desktop robot for AI experiments, voice interaction and creative coding. 3. Microduck: A compact bipedal robot exploring walking, sensing and object interaction. 4. AmazingHand: An open-source robotic hand project. 5. PincOpen: An open-source parallel gripper for grasping objects. 6. Grabette: A data collection project connected to robotics learning workflows. What caught my attention is the variety of hardware involved. Walking, manipulating objects and collecting robot training data each require different engineering approaches. Pollen Robotics joined Hugging Face in 2025. Which project would you choose to work on?

🇨🇳 The 30 Chinese leaders in humanoid robotics: • 1 AgiBot · 2 Unitree · 3 UBTECH · 4 Galbot · 5 Leju Robotics · 6 EngineAI · 7 Fourier · 8 XPeng · 9 Robot Era · 10 Kepler · 11 LimX Dynamics · 12 X-Humanoid · 13 Noetix Robotics · 14 Astribot · 15 Dobot · 16 Xiaomi · 17 Booster Robotics · 18 Galaxea AI · 19 AI² Robotics · 20 PUDU Robotics · 21 DEEP Robotics · 22 MagicLab · 23 GAC · 24 Chery & AiMOGA · 25 X Square Robot · 26 Keenon Robotics · 27 EX-Robots · 28 Lingbao CASBOT · 29 Tencent Robotics X · 30 Shanghai Humanoid Center The list isn’t finished yet. I’m going to complete it with another graphic and turn it into a thread.

Unpopular opinion: the best home robot might be one that does nothing for you. Mori, from One Kind Artifact (OKA), won't answer every question, control your smart home or handle household chores. That's part of its design. It looks like a soft mushroom lamp. Small movements, changes in light and sound, and guided breathing exercises are how it interacts with you. No long conversations. No list of tasks. Just a quiet presence in the room. The details: • Around 30 cm tall • Camera and microphones • On-device AI processing, according to OKA • $649 launch price for the limited Founder Edition What caught my attention is how little Mori asks from its owner. Most home robots are built around tasks. Mori takes a different approach, focusing on how a robot makes you feel. Would you spend $649 on a robot for companionship, or would you rather have one that actually does things around the house? Footage: @onekindartifact

Before you build a robot, watch this. Here are 18 open source robots, shown with their 3D models. 1. Microban 2. MiniLoong 3. Poppy Humanoid 4. Otto DIY 5. pib 6. Mini Pupper 7. Petoi Bittle 8. Stanford Doggo 9. SpotMicro 10. OpenQuadruped 11. Puguette 12. SO-ARM100 13. RoArm-M2 14. OpenMANIPULATOR-X 15. LeKiwi 16. TurtleBot3 Burger 17. OpenBot 18. Duckiebot These projects cover humanoids, quadrupeds, robotic arms and mobile robots. Before building, check each project's official repository. Hardware specifications, 3D design files and licences vary between projects. My personal pick would be SO-ARM100. I'd like to explore its design and see what I could build with it. Which of these 18 robots would you build first? 3D model credits: Microban (Rhoban, CC BY-NC-SA 4.0) · Poppy Humanoid (Poppy Project, CC BY-SA 4.0) · Otto DIY (ottobot. org, CC BY-SA 4.0) · SpotMicro design by Deok-yeon Kim / KDY0523 (CC BY 3.0) · OpenBot wheel model: FreeCAD library (CC BY 3.0) · Duckiebot model: Duckietown (duckietown. com) · All other models from their respective project repositories.

Stop throwing away your old phone. It could still be useful. Here's why.

Why do robots have to look like humans? Meet EELS a 4-meter 100 kg snake like robot developed by NASA JPL to explore terrain where wheels and legs struggle. It uses rotating screw threads to move across snow sand and ice while lidar and stereo cameras help it map its surroundings. The original goal? Explore the icy moon Enceladus in search of signs of life. EELS is still a prototype but it raises an interesting question are we designing robots for how humans move or for what the environment demands?

I asked Claude to build an office for the @tavus PALs... I was not expecting it to build THAT kind of office

There's one thing 99% of people don't know about openArm 2.0

What if a robot arm could let you feel what it touches? That is the idea behind bilateral teleoperation on OpenArm 2.0, the open-source humanoid arm developed by Enactic. How it works: 1. A human moves a leader arm. 2. The follower arm copies the motion, joint by joint. 3. When the follower hits resistance, its motors register the extra load. 4. That force is sent back to the leader, so the operator feels the contact and adjusts. Why it matters: Many robot learning methods start with human demonstrations recorded by teleoperation. If the operator cannot feel contact, they push too hard, crush objects or stop too early. Force feedback gives gentler more precise demonstrations which means better training data for imitation learning. Key specs: 7 joints per arm: shoulder elbow and wrist A camera inside each gripper for close-range vision 4.1 kg nominal payload per arm, gripper included 6 kg peak payload for short durations Full mechanical design available: STEP, STL and URDF files MuJoCo simulation models included Reality check: Open hardware is not plug and play. Autonomous manipulation still needs control software, calibration and a lot of testing. Would you build one to collect your own training data?