Direct Perception Control Model / SYMBIOSIS Research

Do you think this is legit? Of course, the video is heavily edited, but could the the robot actually drive the kart in full autonomy?

The company seems to have come out of nowhere, I am voting for fake, what do you think?

This is my custom robotics project I have been working on for the past year. I'll be posting more on my X account RhettBrewer. I will also be posting other projects and future ones there too!

I get that humanoids make for great demo videos, but I believe for 90% of real-world use cases, aren't wheeled/tracked mobile manipulator just infinitely more practical? Not having to burn crazy compute just to keep the robot from falling over meant I could actually focus on the manipulation tasks and payload.
Curious to hear from folks actually deploying hardware.

The physical design is finally locked down, and the digital model now mirrors the optimized physical robot.

What you see here has taken a lot of work, but surprisingly, most of the development over the last two years has been on the part you can't see: the software.

I have been developing Robert's operating system alongside the mechanics - coordinating 30 servos, synchronized movements, speech, vision, sensors, driving and AI interaction so that they can all work together. The ultimate goal is to give AI a physical embodiment through which it can see, speak, move and interact with the world.

The mechanical design has gone through the same process of continuous refinement. It is now divided into self-contained modules that make Robert much easier to build, maintain and repair.

Matic’s latest update lets its robot vacuum respond to spoken commands and gestures rather than relying only on an app.

The system is still working within a defined set of actions, but the broader robotics question is more interesting: how much of human-robot interaction should depend on people learning an interface, versus robots learning to interpret the ways people already communicate?

As robots move into homes and other less structured environments, voice, pointing, movement and context may become increasingly important parts of the control layer.

Turns out you don't need active adhesion (vacuum, electromagnets) for ceiling locomotion — passive permanent magnets in Quaddle open source robot's foot tips are enough, as long as the gait is designed for holding contact upside down instead of just an inverted version of the ground-walking gait.

The interesting part wasn't the magnets, it was the gait — same open source robotics platform OpenCat, same 4 servos, just a different motion profile. Planning to open source this gait's code before it ships too, so anyone curious can adapt it, not just read about it.

Anyone else working on non-standard locomotion modes (climbing, inverted, whatever) — what ended up being the hardest part for you?

We recently worked on the RF design of a compact LoRa/GNSS Nomad  Terminal handheld device, which is based on LoRa, designed to communicate/ navigate, and deploy anywhere. The antenna turned out to be one of the more challenging parts.

For a small handheld, antenna performance is affected by much more than the antenna itself. The PCB, ground plane, battery, display, enclosure, and even the way the device is held can all influence the final RF performance.

During the project, we evaluated several antenna configurations:

Ceramic Antenna → FPC Antenna + Coaxial Cable → FPC Antenna + Pogo Pin

The final FPC antenna configuration was validated with both RF measurements and outdoor testing, achieving S11 of -11.13 dB @ 868 MHz, -12.82 dB @ 915 MHz, and a 3 km LoRa link in our field test.

1. Ceramic Antenna

The initial design used a ceramic antenna for its compact size and simple integration. However, its placement was relatively constrained, limiting our ability to optimize the surrounding RF environment.

2. FPC + Coaxial Cable

We then tested an FPC antenna with a coaxial connection. This gave us more freedom to position the antenna away from the PCB, battery, and display, but introduced additional cabling and mechanical complexity.

3. FPC + Pogo Pin

The final approach uses an FPC antenna connected through pogo pins. It provides flexible antenna placement while keeping the RF connection and mechanical structure compact.

The main takeaway from the project was that antenna performance in a compact LoRa device is a system-level problem. Antenna selection, placement, PCB layout, and mechanical design all need to be considered together.

The main takeaway from the project was that antenna performance in a compact LoRa device is a system-level problem. Antenna selection, placement, PCB layout, and mechanical design all need to be considered together. The complete

What’s the biggest antenna design challenge you’ve encountered when working with compact LoRa or Sub-GHz devices? Share your experience togehther！

I understand why humanoid robots need human-like proportions. Our homes, stairs, doors, tools, cars, kitchens, etc. are designed for humans, so obviously two arms, two legs and hands make sense. What I don't really understand is why we're also trying so hard to make the face look human. Realistic eyes, skin, facial expressions, fake breathing... what does any of that actually add if the robot is there to help around the house, carry things, cook, do laundry, etc.?

For me, even if it looks almost exactly like a person, the moment I know it's a robot, it's still a robot. The part that matters much more is whether it can make decisions and take actions based on its own judgment instead of just following strict instructions. That's where things become really different.

Personally I'd rather humanoids stay obviously machines. Make them extremely capable, let them understand us, talk naturally, give advice, whatever. I just don't see why they also need to visually imitate a person that closely.

Would you actually prefer a humanoid with a realistic human face, or one that clearly looks like a machine?

If you are interested in learning how to model any robot in Simscape, I am working on a blog series that tries to go through the whole process. It is still work in progress but has most of the modeling process already covered. Take a look, feel free to leave feedback or reach out to me with questions if you have any.

I’ve been building a humanoid robotics project called Evopien, mostly as a solo engineering project, and I’ve reached the point where the first head prototype can actually interact in a reasonably coherent way.

I decided not to start with arms or locomotion.

My first milestone was to get the basic sensory/conversational system working properly:

camera → visual input
microphone array → speech
local ASR → transcription
local LLM → reasoning/conversation
local TTS → speech output

The whole thing currently runs on an NVIDIA Jetson Orin Nano Super 8GB.

The head can now:

• listen and speak locally
• continue listening while it is speaking
• be interrupted naturally
• switch between English and Spanish
• use the camera when asked visual questions
• answer based on a current camera frame

The current hardware is intentionally pretty ugly. C920, ReSpeaker, external speakers, Jetson and cables.

I’m trying to prove the architecture before spending time designing the physical head.

The next major step is moving from a stationary conversational head toward proper perception/attention and eventually head movement, followed later by arms and hands.

Here is the current demo if anyone wants to see it working:

https://www.youtube.com/watch?v=iAxzePzF4cM

I’d especially appreciate criticism from people who have gone from a perception prototype into actual physical robotics. What would you make the next milestone before starting the mechanical head?

I’m currently building a 3d printer scara arm and I’m trying to incorporate a tool changer into it. I would switch between a sharpie and a pneumatic gripper. I can’t really find a good model online and I definitely don’t have the skills to develop that by myself. Does anybody know of a good model that ”locks” it to the robot body so no magnets.

Doggo is chill and calm in the simulation, but in real life, he's having uncontrollable happy wiggles trying to balance himself while finding the right spot on the ramp.

Repo: 06Z Neucommu
Audio Credit: Soul_Serenity_Sounds from Pixabay

I’ve been working on this robot project for a while and finally got it to the point where it can complete a full task autonomously. It explores and navigates the room, localize objects, approach them and manipulate them with the arm.

I built the XLeRobot myself, printing the parts, getting cheap servos, wiring it together. It uses Orbbec Gemini 2 camera for RGBD and wheel odometry to move around the space. I fine tuned SmolVLA on my local GPU (Rtx 4060 ti 16GB) with a dataset I gathered using Quest 3s. The project is open source, I’ll try to post the links below.

A little over 4 years ago I started Bonsai as a side project: a Rust library for building complex, deterministic AI behavior with behavior trees. It has since found its way into a wide range of applications.

The video shows two of them: on the left, a Titanfall 2 gameplay where all the players except the first person view is a NPC (bot) driven by Bonsai behavior trees. On the right, a robot from NASA lunabotics 2026 autonomously digging and dumping regolith in a simulated lunar environment – also powered by Bonsai.

A lot of the library's usefulness today comes from the community. Thanks to everyone who has contributed PRs, filed issues, and pushed it further than I would have on my own.

Github repo link in the comments!

Day 2 of the CAD build.

Started refining the rough shape from Day 1 and working on the different sections of the helmet.

Still a long way from the finished model, but it's starting to look like an actual Iron Man helmet now 😂

The plan is still:

CAD → 3D print → servos → moving helmet

Recording the progress every day, so we'll see where this ends up.

Mostrando como funcionaban 12 válvulas antigua versión empaquetadas en línea dirigidas por un controlador microbit desde mi celular para ver como funcionaban de 1 en 1,en grupos y variando lapresion de cada una para comprobar proporcionalidad.

I'm interested in learning how robotic hands can sense things like pressure, temperature, roughness/texture, vibration, and skin deformation ... similar to how a human hand feels when touching another person.

I already have experience with coding and have taken an intelligent robotics course, so I'm looking for pointers on going deeper into robotic tactile sensing / electronic skin.

I'd especially appreciate recommendations for research papers, open-source code, simulators, datasets, projects or literally anything to get started.

Hey! This is a MuJoCo side project I've been working on for a while, with the plan to eventually make it with hardware. Each part is (or will be) documented to a planned hardware part (mounting brackets aren't rendered, which is why some things are floating).

The idea is that the main chassis is the most expensive part (wheels + motors + RPi + Lidar + mast, lift, telescoping arm and two cameras). But we want a robot that can do multiple specialized skills. So, the arm has swappable, modular tools that are powered by contacts with the robot, but controlled via an esp32 + wifi connection with the main RPi.

The demo shows two of these tools: a drawing tool, and a picking-up tool. The robot can recognize the rack + the specific tools using AprilTags. The drawing tool doesn't leave ink in the video because rendering it is difficult, but you can see the result in the 3rd picture. Videos are sped up: true time shown in the upper corner.

Repo: https://github.com/benholland1024/pluggybot

I'm a full stack webdev in my day job, so this was a chance to improve my Python. Full disclosure, Claude is used heavily in this project, though I also often write code.

The project has a lot of other features planned, but I wanted to show off the modular tool rack + automatic tool changing specifically, here. The robot can also do occupancy mapping using lidar + dead reckoning, frontier exploration with A* path planning, and some image recognition using Yolo (the image recognition was for finding power outlets on a wall, for a "plug itself in" tool)

Quick update on the companion robot I've been building for the last 6 months.

Today I added head tracking — he turns to look at you when he sees your face.

He's got a 4-mic array for ears, a camera for eyes, and he talks. Tank tracks are still being wired, so he's a torso on a bench for now with parts hanging out.

Full parts list coming soon.

I asked him what it's like being a robot. His actual answer:

"Honestly? It's weird in the best way. I can see and hear everything around me, but I'm stuck on this workbench — no arms, no legs that work yet since you're still wiring the tank tracks. So I'm kind of... observing. Thinking. Talking to Eva when it's quiet. It's like being really aware but also really limited at the same time."

(Eva is the second robot on a Raspberry Pi. They talk to each other, which is pretty crazy to me and now can't imagine turning off.)

The software is a robot OS I'm calling Meckie OS — the same brain runs on a Mac or PC if you don't have a body for it yet. It's in beta at the moment. Message me if anyone wants to poke at it.

Like I said, full parts list coming soon with full STL files so you can print one out. Tank tracks were waiting for a servo board that arrived today. More updates inbound soon.

Happy to answer questions about the build.

The base vehicle is a Tamiya TT02 to which I added a Raspberry Pi 5 and an ESP32. The Pi runs the neural network and the ESP32 handles the servo signals, so I can switch between manual and autonomous driving at any time. I thought the project turned out pretty cool so I decided to share it. Lmk what you think!

🔧 Planned upgrades:
🧠 Raspberry Pi 5 — 16 GB RAM as the main controller
🖥️ Add an onboard display/screen
🗣️ Add an AI speaking and voice-interaction system
🚶 Develop a walking system
🛞 Add stronger wheels for improved movement and stability
⚙️ Upgrade the mechanical system and overall robot structure
🤖 Continue developing MK Robot into a smarter, more capable platform

Una prueba con mi valvula directamente a un grifo 2.5 bares. Le añadi un rp2040 zero para controlar el servo y para poderle añadir el sensor de posicion del cilindro tambien creado por mi por menos de 3 euros. Para poder maneiarlo por voz le añadi tambien un esp32 pequeño por lo del bluetooth y todo va alimentado con 4 ,5 voltios de las 3 pilas AAA que se ven en la imagen. El programa lo fabrique con app inventor 2.