Short clip of the hydraulic exosksleton's arms test. It can also move automatically through an Arduino Mega built into it.

I enjoy learning about robots, the various manufacturers and what they're capable of. I started this directory of sorts to discover and track the capabilities of different robots. The goal is to benchmark and catalogue capabilities, reviews and maintenance in a comparable manner and quickly expose people to the world of robots. It's called omblet.com

Lamp is a desk lamp with a body: five STS3215 bus servos (base, arm, head) with position and load feedback, a wide-angle camera, a mic array, a speaker and a ring light. It runs an open-source OS that lets an AI agent, such as Claude Code, Codex, Hermes or OpenClaw, see, talk and move through it.

We started the repo on 24 March. October has been our busiest month since: 4,092 commits on the main development branch, more than double April, the previous high at 1,615. The hardware is fixed for now while we focus on production, so the work was software, plus what it takes to build many lamps instead of one. Here's what changed.

1. A way to program it

A common question is whether you can modify it with Claude Code. There's now a documented path for that. docs/develop.md takes about 10 minutes: a fake lamp that runs on your laptop, so you don't need the hardware, Python and JavaScript SDKs, pairing, and lamp-mcp.

Skills moved to format 2. You check a skill, push it, sign it with your own key, and share it. Lamp refuses a package that is unsigned, changed, or renamed.

2. Knowing who said what

Lamp can ties each line to the face and voice that said it, and keeps each person's memory separate. If you talk to it before it recognizes you, those lines are attached to you once it does.

It only keeps what you said about yourself. Not its own words, not your questions. "Forget everything" now asks once and runs only on the same person's yes. Forgetting one specific thing went from 9 s to under 400 ms on a 10 MB memory store.

3. Camera permissions per skill

A skill gets the camera image only after the owner allows that specific skill. Checking whether someone is in the room is a separate, smaller permission that doesn't include the image. Installing a skill by hand in place of a Store skill removes the permission. Onboarding also asks once whether Lamp may notice facial expressions, and stores only a yes.

4. Giving way to a hand

The servos report real position and load, and Lamp now uses that to tell a push from a fault. If a joint is carried away from its goal, Lamp treats it as a hand, stops what it was doing, and doesn't look around again for 30 s unless asked.

Onboarding has a step for this: hold the base, tilt the head, let go when told. If an arm strains three times, Lamp sits down, rests for 30 minutes, then gets back up.

5. Skills don't touch hardware

·         New lamps calibrate their servos at the factory before the first run

·         A new Orange Pi image boots straight into setup

·         lampOS 0.0.4 and 0.0.5 shipped on 22 Oct, including a fix for a button that could get stuck in a held state

·         The Fusion 360 file for the current body is in the repo.

·         Speech starts streaming within 400 ms of the first part of an answer

·         The evening glow stays at least one level below your own light

·         "Tap to talk" is now "Press to talk".

Not Done Yet

Naming two people in the same room, add-ons, and ignoring lines meant for someone else in a busy room are built but off by default until they pass on real lamps. Mood reading is at 39% on our internal tracker, and we don't count it as done.

I’m pretty new to robotics, and I finally finished assembling my first robotic hand!
It’s based on the open-source AeroHand project, and it took me about 5 days to put everything together.
Honestly, the tendon routing was an absolute nightmare 😭
I kept threading the tendons through the wrong holes, which meant pulling everything out and starting over.
I didn’t have a proper threading tool either, so I spent ages trying all kinds of improvised tricks and shortcuts to get those tiny strings through.
I also didn’t have the right tools for some of the tiny screws, so I ended up stripping a few screw heads.
The design is pretty compact, which made cable management another headache. And even after getting the tendons through, keeping them properly tensioned was still a struggle.
But after five days of trial and error, it’s finally assembled!
I’ve managed to control the fingers using the official testing software, and it can perform some basic grasping movements now.
I’m still a beginner, and there’s a lot I want to learn. Next, I’m hoping to explore feedback and closed-loop control.
Just wanted to share this little milestone!
If anyone has experience assembling tendon-driven robotic hands, I’d love to hear your tips, especially for tendon threading and tensioning!

Hellloooo!

This is a small RL project I've been working on. A simulated Franka Panda learns to pick up a cube and put it on a target pad, using PPO from Stable-Baselines3 in PyBullet.

The main objective of this project was to get an understanding of PyBullet and how the whole workflow of training a robot using reinforcement learning works.

Further updates will be to combine this with a text or voice prompt enabling the user to choose which cube to pick and place at a desired location.

Any further upgrades or inputs are highly appreciated from everyone here!

Checkout the code here: GitHub

From the article, AGIBOT coordinated 110 humanoid robots in a synchronized dance performance that set a Guinness World Record. According to the company, all were standard production models, using its LinkBots platform and centimeter-level positioning to stay in formation.

Dancing is a controlled demonstration, but it raises an interesting question about what comes next: could fleets like this eventually coordinate useful work in warehouses, construction, or disaster response?

What do you think is the biggest hurdle between a synchronized performance and a robot fleet that can handle unpredictable situations together?
source:
https://interestingengineering.com/ai-robotics/110-humanoid-robot-guinness-world-record-dance

Meet Nyro, a 3D-printed desktop companion robot with an expressive owl face, a moving head, and a few friendly hoots. 🦉

#robotics #embodiedai #ai #3dprinting

WHAT CANNOT* Be replaced by a.i./robotics ??

Anyone???