r/robotics

Two months ago, I shared the initial Lamp build here: the 5-DOF arm running on STS3215 bus servos, a single 12V cable, the USB DAC to kill ground noise, and an early pass at Autonomous OS.

After 2 months, lots of updates have been made. And Lamp is more than just a fun desk decoration now

1. It actually sounds like it has a mood

Lamp used to run on that familiar, flat-line TTS. Recently, we switched to ElevenLabs v4 Turbo, and it changed the whole vibe. Pitch and cadence shift naturally, pauses land where they should, and a joke lands like a joke instead of a status report.

We also added an on-device emotion model (emotion2vec) listening to user speech, with the head's ring light subtly reflecting how Lamp is feeling.

The fun part: the personality is just a simple markdown file the agent parses on every turn. Out of the box, it behaves like a warm, curious desk companion that never breaks character with tech jargon like "servos" or "API calls." Tweak a few bullet points in that file, and you get an entirely different roommate. You edit the soul, not the code.

https://preview.redd.it/kbwar29b6fuh1.png?width=1672&format=png&auto=webp&s=a4f92c6ef50fa8fe0d30468fcfdbabe5b5a300df

2. It can read the room (literally)

In the first build, the only inputs were the camera and the mic. I have since wired up a Sensirion SEN63C over the I2C bus, which is a single compact module reporting particulate matter (PM1, PM2.5, PM4, PM10), temperature, humidity, and true CO2.

The HAL caches the latest snapshot in RAM while an OS worker polls it in the background. Lamp only fires an event when environmental metrics drift over time, rather than spamming context on every raw sample (available read-only via Web and MQTT).

The practical payoff: I closed my window while someone was cooking downstairs, and Lamp chimed in that the room was getting stuffy before I even noticed. Because the agent layer only queries abstract keys (pm, co2, temperature, etc...), it does not care what sensor hardware you solder on down the line.

3. The brain is a hot-swappable slot

Early on, Lamp was locked into a single agent runtime. Autonomous OS now treats the agent as a modular backend you define in config, completely decoupled from personality and long-term memory.

I recently migrated Lamp from Hermes to OpenClaw with a single setting change. It kept the exact same voice, the same quirks, and everything it had already learned about me. The agent handles the reasoning; the soul and the memory survive the brain transplant.

Made in Brazillllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll

https://www.youtube.com/@ALMA.GeoffreyAment

Chapter 2, a home theatre, 3D printed parts, motorized projector, home decoration, and DIY electronics -- I spent alot of time figuring out how to control different actuators and such via microcontroller to control the tilt, roll, pitch, and translation of a projector. If you know of anyone else that might be interested in this stuff, sharing to others would really help me out! Hope to see you around here or YouTube :)

Unitree G1 in the MuJoCo copy of my messy apartment, real time. Walking is Unitree's policy, arms are scripted. Yes, it knocks the bunny off the counter at the end.

I've been building a tool that generates digital twins/cousins of real spaces for robot simulation. This is a test of it. The first photo is my messy living room. The next one is the same angle in MuJoCo (with a Go2). Keep swiping to see more.

What is this showcase:
I'm showcasing the digital cousin of my messy apartment (and not the robots). To try it out, I then had a Unitree G1 pick up a toy and drop it into a bin, and pick up a mug from my coffee table and carry it to the kitchen counter. All running in MuJoCo, in real time. What's ours and what isn't: the environment is ours. The walking is Unitree's trained policy. The arm motions are scripted. It's one simulation, and I moved the robot to a new start spot for each of the four shots.

How I created the environment:
I took 8 photos on my phone and our tool built the scene from them. 286 separate parts, each with its own mass and friction, and 30 working joints. Photo inputs are what I used here. But it also takes video and scans.

You can use this environment generation tool right now:
The tool is called Nova3D and is live at: https://studio.nova3d.xyz/robotics
It runs in a browser for free on the Claude or Codex you already have. And there's nothing to install. It isn't open source yet. It works like a chat: upload your photos and say what you want. It has internet access, so it will generate well-known landmarks for you anyway. My messy apartment's digital cousin took ~40 mins. I used opus5.5, and the build fit inside my $20 subscription's 5-hr session limit.

What's a digital cousin?
I call it a 'cousin' because the dimensions are estimated from photos. That may be enough. Stanford's ACDC paper found that policies trained on close copies of a scene transferred to the real one better than policies trained on an exact twin (90% vs 25% in their tests). We haven't benchmarked ours on a real robot yet.

Our origin:
The approach comes from our paper (arXiv, not yet peer reviewed): https://arxiv.org/abs/2607.22738

If you train robots for homes, offices or warehouses:
You can generate your own environment with the tool's link above. If you'd rather I do it, send me photos and tell me which simulator you use. I'll build the first 5 for free, with the same tool. One question for you: is a close copy enough for your work, or do you need measured accuracy?

I’ve been experimenting with turning an M5Stack Stack-chan into a little mobile robot. I combined it with a LEGO motor hub and wheels, recorded driving demonstrations, and trained an ACT policy.

During supervised trials, it learned to follow a line. With a separate set of demonstrations, I also tried driving between guardrails. The video shows the build, data collection, and the wrong turns along the way 😅

Build video: https://www.youtube.com/watch?v=_1pQTt8gqZM

This is also the first showcase of what I’ve built with Robium, an open-source robotics skills repo that I recently released. I used it with AI agents to help build the software and training setup.

GitHub: https://github.com/robium-ai/robium

Has anyone else experimented with learning from demonstrations on a small wheeled robot? I’d be interested to hear what worked for you.

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Running on STM32 Bluepill.

ESP32cam for image steaming.

OpenCV for image processing.

LLM for speech & intent extraction.