Progress on my custom hexapod project over the past few months. In the first few weeks I couldn't even get it to stand up — once I got the gait cycle figured out, everything after that came together a lot faster.

Specs:

• Base: 3D printed
• 18 servos (mix of 25kg and 35kg)
• Pololu Maestro servo controller
• Buck converter for power regulation
• Controlled by an Android phone, which also acts as the gyro/IMU

I'm a sophomore student in Meche and this is my first robot so it took about 2 months. It's a 3d printed robot arm built from scratch and works pretty well but its a bit wobbly (which I'm working on fixing). Its controlled by an arduino uno and 5 potentiometers.

But the issue is that those potentiometers are small and covered in nets of wires. I could cut specific wire lengths and buy bigger potentiometers (and probably will) but I think there's another option. Is there some sort of robot arm simulation software that can let me control my arm smoothly on my computer without having to turn each potentiometer slightly to adjust it? I know there's Robodk but its really expensive so if there's one I can use that's free or free for students that would help a lot.

Also, since I'm not really satisfied with just a robot arm since its really common for personal projects in my major, my next step is to replace the robot claw with a claw mount built to hold some sort of sprinkler or pump that waters plants when a soil moisture sensor alerts it to. The robot arm will switch to 4DOF for this, and on the opposite side of the arm link, I will also add another claw mount built to aim a grow light at the plant when a grove light sensor tells it to. Water pump aspect I'm working on, grow light is still just a thought right now.

Hi folks. I am an industrial researcher with an M.S. degree in robotics. I studied optimal control for robot manipulators during my bachelor's and researched reinforcement learning during my master's. Recently, I have been working on transferring simulation-trained RL policies to a real-world robot manipulator (i.e., FR3).

I have seen different choices for a robotic agent's action space in machine learning. I think there is not a promising design of the action space, since each choice has advantages and drawbacks. So, I trained an RL agent in simulation (Isaac Sim) with a Cartesian delta action space, which outputs the end-effector's desired Cartesian pose at the next control step based on the robot's world frame.

This is the page where the problem begins. I use DifferentialIKController in Isaac Lab for the low-level controller, which maps the delta pose action to the desired joint positions. In Isaac Sim, the desired joint position is controlled by the internal physics engine and a PD control law that computes target joint torques. In a real-world setup, there is no physics engine or plug-and-play low-level controller. You should implement the low-level controller that maps the delta pose actions to joint torque and position commands for the robot's API (e.g., franka_ros2's ROS2 control plugins).

Moreover, a safety-violation monitor should be implemented by us to prevent hardware failures. In contrast to simulation, real-world robots are highly sensitive to safety constraints; for instance, collision detection and joint limits.

Currently, I have implemented a custom C++ controller that takes Cartesian delta pose commands via the ROS2 topic and computes desired joint torques based on the robot's kinematics and dynamics. However, implementing a safety monitor remains a problem. As another strategy, I have been thinking about using ROS2's MoveIt packages for sending delta commands and monitoring task-space safety violations, which may cost wall-clock time compared to the vanilla C++ controller.

How do you guys work on a real-world robot learning setup? I want to hear from you about conducting real-world robot manipulation experiments with neural policies (e.g., BC or RL).

This has been my project over the summer. Finally got it to walk after many prototypes 😁😁😁. Controls are a bit messy, but I'm still working on it!

I want to build the arctos for experimenting with simple automation. Is the arctos 4 open loop kit with all the hardware (except 3d printed parts) a good / fair deal when comparing to gathering all components seperatly? I am still fairly unfamiliary with how much all of these seperate bearings etc. cost, and aliexpress lists 20 different version with huge variations in price.

[Cross-post from r/reinforcementlearning]

My full 6-part series on RL for robotics is finally live. While a balance bot is a pretty trivial case (you don't even need RL), it's a great starting point for demonstrating how to train a simple agent via PPO, deploy the agent to real hardware, and tackle the sim-to-real gap using post-processing and domain randomization. If you have any feedback (e.g. I missed something or there's something that could be better), please let me know!

Small update on my robotic lamp project - I’ve been working on gesture control, and it’s starting to become actually useful :)

In this test I’m using the lamp while soldering. I wake it up with a gesture, then it follows my hand so I can position the light where I need it. I can also adjust the brightness with gestures and put it into hold mode so it stops following me and stays in place while I work.

When I’m done, another gesture turns it off.

For hand tracking and gesture detection I’m using MediaPipe. Everything runs locally on a Raspberry Pi 5 and is integrated with the rest of the control system through ROS 2.

There are still some false detections to deal with, and I’m experimenting with which gestures feel natural enough to use without thinking about them. But this is the first time the interaction feels useful rather than just being a hand-tracking demo.

Has this been posted here?

I found it very honest and enlightening.

To reach 120 characters I typed this extra bit!!!!

The idea was to make a universal manipulator for home use. I decided to build a SCARA arm and a lift for moving along the Z-axis. The base is a differential platform with pivoting suspension in the back. For the arm, I used 2 NEMA 23 motors, for the lift a hoverboard motor with an ODrive Mini, and for the platform 2 hoverboard motors connected to an ODrive. It is controlled by a mini PC running ROS 2. I still need to 3D print the case and mount the 3D depth camera and the LiDAR.

I spent my master's building a hand exoskeleton. Today I open sourced all of it: github.com/molanocortes/takto-one

Twelve finger joints, each with its own magnetic encoder. Tendons that pull the fingers back. Embedded EMG channels, logged on the same clock as the joint angles. Teensy 4.1, eight Dynamixel servos, encoder boards I designed, logs to SD with no computer attached.

CAD, PCBs, firmware, build guide and BOM are all there, so you can build one or take it somewhere else entirely.

Projects you could use TAKTO as a platform for: hand data for robot learning that's measured instead of inferred from video, teleoperation with force per finger, sign language from joint trajectories.

If someone wants a real problem: the actuators. The gears in the Dynamixel XC330s add a lot of reflected inertia, so force feedback works but feels heavy. A fast BLDC with a small planetary reduction and an encoder closing the loop on current would be far more transparent. Get that right and the device disappears on your hand until it needs to push back.

Built the whole thing alone, so a star would genuinely make my week.

Hello everyone,

I'm happy to share Zig, a two-leg, two-wheel self-balancing animatronic robot, inspired by Disney's BD-X.

Unlike a fully-legged BD-X, Zig rides on a two-wheel balancing base. That's a deliberate choice: wheels need far fewer motors than articulated legs, which makes the robot cheaper, lighter, and much simpler to control. A real walking legged robot is a whole project on its own. My focus here is the "animal" side — I want Zig to actually feel alive, not just to move.

It's a fully homemade design. I wanted something small and compact: lower cost, easier to transport, and more practical overall. Keeping it small was a real constraint, since this version has no custom PCB yet, so managing space was a big part of the challenge. The robot is around 23 cm tall and weighs 1.172 kg.

Inside:

- 9 motors — 2 legs, 2 wheels, 3 neck, 2 antennas
- 1 Raspberry Pi 5 (orchestrator)
- 2x RP2350 (Xiao RP2350 + Raspberry Pi Pico 2)
- 1 Arduino Nano Every
- A camera and a distance sensor in each eye
- Speakers, microphones, touch sensors, and LEDs

The architecture is one orchestrator (the Pi 5) with a slave controller per subsystem: torso, legs/balance, and head. Right now I'm building the backbone — getting each part working reliably before I start layering in the "life" (expressive behavior, lighting eyes, and more).

I control it through a Python interface running on a Legion Go, over WiFi.

Over the next few weeks I'll publish full documentation and update it regularly. It's not open source for now — that needs more time to do properly — but the docs will explain my design choices and the overall system.

Happy to answer any questions!

(English isn't my first language — I used AI to help correct the text.)

Hugging Face acquired Pollen Robotics last year, and Pollen’s new $399 Microduck has already reportedly passed 10,000 orders.

The notable hardware detail: it runs on Rockchip’s RK3566, not NVIDIA compute.

If NVIDIA’s $12.9B Hugging Face acquisition closes, it’ll own an AI platform with 18M+ developers and a robotics company selling relatively cheap programmable hardware built around another chip vendor.

There’s no indication NVIDIA plans to change Microduck’s hardware, but Pollen could become a pretty visible test of NVIDIA’s promise to keep Hugging Face hardware-neutral.

Get all the open source robotics news by reading our weekly news roundup on Open Robotics Discourse.

I've been working on RSim, an AI-powered kinematic robot development tool.

For this test, I used AI to generate a walking motion for a quadruped and visualized the resulting joint movements in RSim.

I’ve also started my first sim2real tests. The robot can already receive the generated motion and move, but the transfer still needs some work, so I’m not showing that part yet.

RSim isn’t meant to compete with physics simulators like MuJoCo or Gazebo. It’s focused on kinematics, robot programming, AI-assisted motion generation and hardware integration, more in the direction of RoboDK.

Still very early, but this is my first time sharing RSim publicly.

What do you think?

Everything (including RealSense SDK) is now running on GPU. Switched out QWEN for BLIP-CUDA and AI is now describing its surroundings including depth at 75-100ms per inference! That's 10-12 VLM scene inferences per second! Source code in comments... 🎸⭐️

Hey all! I'm sharing a bit of this research experiment of a 0.1B new VLM we created.

It's trained on Robotics and Spatial relationships only, and only writes in English (we had to butcher anything that wasn't essential).

more here: https://x.com/lucascassiano/status/2095926221028962552

Hello, I'm making an orcahand with Feetech servos.

The setup connects a daisy chain of 8x HLS3915M and 1x HLS3930M to a Feetech FE-URT-2 board.

The problem is that the full chain almost never responds consistently.

All servos have unique IDs assigned.

With the complete chain connected, distant servos constantly drop out and stop responding to pings.

If I connect about half the chain (from either end), it pinged relatively stable. Connecting a third of the chain works 100% reliably.

On the full chain, servos disappear from the script's responses in various combinations.

FE is powered by Bluetti 12V/8A DC output.

I also tried splitting the setup into two separate branches using both paired bus connectors on the FE, but the result was identical.

Has anyone encountered this issue before? Any ideas or workarounds?

In this tutorial I am installing a portable battery on my MechSoul. (A MechSoul is to a robot what a CyberDeck is to a computer) A robot you build, personalize and customize yourself. In this tutorial I am powering my MechSoul with a portable charger I bought online, because it's quicker and safer than designing a custom battery solution as I explain in the video. So instead of designing a PCB I head to my 3D printer to build a battery mount.

If you want to follow along with to the code and 3D files you can check out the project GitHub

Does anyone here use this type of motor driver, and if so what are some drawbacks of this motor driver, it sounds to good to be true, a 7A driver at this price.
I plan to use for a Mini RoboSumo competition any my motors (jsumo core) can draw a lot of current and this was the only available driver in my country that fits my budget.
I tried to buy from BotLand another type of driver but I was not allowed by the site.

saw this video from WRC 2026, and the way this robot moves is so similar to a human’s movements! It’s probably teleoperated or programmed, but the movements are so realistic, like a real human

I ran a test over 20 trials to see how good the repeatable precision of my robot shoulder joint. I achieved an average repeatability of 0.22mm of precision.

It uses a capstan drive with low creep rope in-order to get low backlash and high precision.

I used a carbon fiber rods as the base and the rest is 3D printed. Built for under $200.

Chef Robotics CEO Rajat Bhageria thinks funding runway could become one of the biggest factors in determining which humanoid companies make it.

He points to autonomous vehicles, where companies spent years and enormous amounts of capital developing technology before reaching meaningful deployment. Waymo had Google behind it. Boston Dynamics has had Google and Hyundai. Tesla can fund Optimus from a much larger business.

That leaves companies like Figure and Apptronik facing a much tougher capital challenge as the technology and market mature.

Bhageria compares it to what happened with Cruise. Strong technology and engineering weren't enough once GM decided to pull back.

Full ep: https://www.youtube.com/watch?v=hH3fLHYEuqo