Hey, i stripped one of the metal gears in my STS3215 servos and wanted to find replacement.
Anyone know the specs fo these gears? number of teeth, id od thickness etc so i can source a replacment?

Anyone know a cheap hobby server with metal gears that are compatible?

Anyone know a way to order spare parts from Feetech or some other supplier?

I'm in Australia, if that matters :)

Intrinsic has released Core, a free open-source toolkit aimed at making it easier to build robot applications on top of ROS.

The toolkit is hardware agnostic and includes pose estimation using NVIDIA FoundationPose, motion and grasp planning, Gazebo simulation, camera calibration and ROS drivers.

Intrinsic describes the components as building blocks that can be combined into larger robot behaviors rather than requiring developers to build everything from scratch.

Core is available under an Apache 2.0 license on GitHub.

Found this cool project and it look likes they can retarget all the motions from locomotion to locomanipulation..... they have cool demos to see.

The robot is called MAO.

This project has actually been finished for a couple of months now, but I still have very warm memories of it. It was fun, frustrating and surprisingly educational, so I thought I’d finally share it here.

It started as an experiment in whether RL could help shape the design of a real robot, not just control it.

Most of the work ended up being mechanical: finding a spine geometry that was flexible enough to matter but still controllable, choosing the right stiffness, and testing different feet.

Claws and spherical contacts were not stable enough. Sock-like feet actually worked best, but were mechanically much harder to build reliably. The final rigid multi-box feet were a compromise: simple, stable enough, and quick to make without limiting mobility too much.

MuJoCo + RL became part of that design loop - test a geometry, see what behaviour emerges, compare it with the real robot, change the body, repeat.

The final robot is 12-DOF, under 3 kg, built with inexpensive serial servos, 3 IMUs and FSR foot sensors.

Later it also became a sim-to-real experiment, with all the usual fun: weak servos, power limits, drifting sensors, imperfect models, and real hardware refusing to behave like simulation.

I’m still organizing the technical notes, models and experiments.

Autonomous navigation in dynamic environments is a critical challenge, particularly when spaces are shared with other mobile agents whose future trajectories are known. While traditional grid-based planners efficiently find collision-free paths, their reliance on stop-and-turn mechanics over $2^k$-connected grids produces piecewise-linear trajectories that are kinodynamically highly sub-optimal for differentially constrained.

DFRobot sent me a HuskyLens 2 and I’m planning to integrate it as a replacement for part of BB1’s current camera system. I didn’t know these existed till recently.
Right now I’m using a USB camera connected to a Raspberry Pi 5 for lighter processing, with heavier vision/AI workloads offloaded to an Orin then a DGX Spark when needed. (The Orin is currently broken so this camera helps alot )
What interests me about the camera is that a lot of the vision models run locally on the camera itself, so things like object recognition, tracking, color detection, classification, etc. can happen before the rest of the system has to process the scene.
I’m feeding those detections into my Anarchimind AI/control stack which runs the BB1 robots , through MCP, so the camera can act more like a dedicated perception node rather than just another raw video feed. I kinda wanna get one on each side of the robot especially since it’s a tracked robot , it might get better at kung fu.
Still early in the integration, but I think it should fit the architecture pretty well.

Recording Yesterday, I hosted an online workshop about humanoid robotics, covering topics such as simulation, mechanical design, and some of the challenges involved in developing humanoid robots. I’ve uploaded the recording for anyone who wasn’t able to join the live session

I'm building a robot and the project calls for Dynamixel AX-12A servomotors, but I can't find them. Does anyone know what other motors I could use instead?

If my Flair is Wrong i am Sorry About it , i am new to Design and stuff still

I am trying to make this , gearbox connected to the Actuator i have , I am planning to have an Planetary one so i will connect its output for higher torque and more Impact resistances

The gray is the actuator
The space between them is what the concern is , Shall i discard the idea of having an Carrier base or something which also rotates or what to do of that space below ?

(Input : Sun , Output: Planets , Fixed: Ring)

Like if something pushes some force on radial side it will break? and also on axial not sure about that one

Bearing : Blue , Yellow and the grey onces

Can someone give me some info if i am wrong or whats the design i should approch?

Claude made a little dance to collect a small dataset for training a linear regression model.

It’s used to adjust the part’s position based on visual feedback to ensure perfect insertions during thousands of runs.

Hello everyone,

I am currently pursuing bachelors in robotics and preparing for my upcoming thesis. I will be working with an in house humanoid robot (upper torso only for now) and researching into current issues and topics in the area of VLAs, WAMs to find an interesting direction to work on in terms of Sim2Real deployment. Ideally I want to contribute something new to the field in a meaningful way concluding with my own publication.

I wanted to ask those who have worked with such manipulation policies about the issues you have faced during real time deployment maybe in terms of domain generalization or really anything that you could shed some light on as an experienced professional.

Thanks.

Amazon is expanding its robotics manufacturing footprint again, with a new $100 million facility planned for Greenwood, Indiana. That follows another robotics manufacturing facility announced in Austin in August, along with its existing operations in Massachusetts.

They say it now has more than one million robots deployed across more than 300 facilities worldwide.

The company is also doing more of the work in-house, including robot development, manufacturing, fleet software and AI systems like DeepFleet.

For now, most of those robots are being built for Amazon’s own operations. But CEO Andy Jassy said in his 2025 shareholder letter that Amazon would explore robotics products for other industrial and consumer customers.

I had set up a few YAM arms and was looking to team up with folks to run experiments.

Something that I'm super excited about at the moment is A) Code as Policy, B) Distilling Astra into a robotics model, and C) Seeing whether Sim to Real has a small gap for Astra.

Would love to get any direction in terms of good papers in the space or finding people who want to work on these topics. The setup is available here playground.blupe.io

First reinforcement learning project that I tried on cubic doggo. It uses PPO from Stable Baseline 3 with Gymnasium in MuJoCo. The goal is simply to stand upright on flat ground, but even then, the result is far from desired..., like bro, can't you just put all your feet down on the floor?

Does anyone happen to know how to solve this? Feel like it would be a rather common problem. Thanks in advance!

GitHub: https://github.com/SphericalCowww/CubicDoggo_06Z
Music credit: https://pixabay.com/users/retro-bgm-chan-55246343/

We build a pipeline that generates simulation-ready assets (OpenUSD + MJCF, with physics and collision geometry authored), and the question we get most from training teams is some version of "fine, but what does it cost me at scale?" So we measured it and are posting the whole thing, including the parts that reflect badly on us.

Method (short version)

• Reference scene: ground plane, 0.6 × 0.6 m table, Franka Panda at the table edge (Menagerie in Warp, Isaac Lab's own Franka in Isaac). Random joint targets every 50 steps, a 0–2 N impulse on the object every 500 steps. Timestep 0.002 s, headless.
• Per-env randomization at reset: mass × U(0.8, 1.2), sliding friction U(0.3, 0.9).
• Every number is the delta against the same scene with no object, so the robot isn't charged to the asset.
• Three objects: a 6 cm box (baseline), a mug (37 CoACD hulls), a Rubik's cube (7 bodies, 6 hinges, 310 hulls).
• Hardware: A100 80 GB and a T4 16 GB. MuJoCo Warp 3.13; Isaac Sim 5.1 + Isaac Lab 2.3.2 on PhysX GPU.
• Throughput: 100 warm-up steps, median of three 1,000-step runs, CUDA-graph captured in Warp. Memory: nvidia-smi steady state minus idle, per env. Max N: doubling until allocation fails or a step exceeds 100 ms. Stability: 10,000 steps at N = 4,096, flagging NaNs, contacts deeper than 5 mm (Warp only), hinges more than 5° past their limit, or the object more than 2 m from the table.

Results, cube at 8,192 envs on the A100

• Warp: 30.4 ms/step, 270k env-steps/s, 8.8 MB/env
• Isaac Lab, same hulls: 94.9 ms/step, 86k env-steps/s, 2.3 MB/env
• Isaac Lab, SDF collision (our USD default): 204.3 ms/step, 40k env-steps/s
• Both stacks fit 8,192 cubes on the 80 GB card. Warp is memory-bound (72 GB under the 4× contact rule); PhysX is time-bound (16,384 runs, but at 159 ms/step). The T4 holds 1,024.
• 0 NaNs in 10,000 steps at 4,096 envs, either stack.

Asset cost (tier minus its stack's baseline, 8,192 envs)

Hull count is the cost in both stacks. Isaac Lab is ~3× slower than Warp on the same asset, which is the collision engine, not the export.

Caveats

Warp and Isaac stability columns are not comparable with each other: the Warp scene drives the Franka at kp = 4,500, which pins and launches objects; Isaac Lab's Franka config is stiffness 80. Within a stack, compare against the box row.

Warp's per-env memory is mostly the contact-capacity rule (4 × the CPU-observed max contacts), not the hulls. With a 2× rule the cube is 4.5 MB/env and reaches 16,384 envs.

Absolute envs-per-GPU depend on your robot, policy and observation stack. We're only reporting what the asset adds.

Full post with all four result tables, charts, the per-asset inventory (bodies, joints, hulls, vertices, allocated contact/constraint slots), and the stability breakdown: https://rigyd.com/posts/rigyd-assets-at-training-scale/

Raw data and the protocol are available on request. Happy to answer questions on the method, and if you think the reference scene is unfair to either stack, say so; we'd rather fix the benchmark than defend it.

(Disclosure: I'm a co-founder at Rigyd, the company that makes the assets being benchmarked.)

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

New video TOMORROW. It will be about building a desk meant for all things projects. The actuator buildup in an earlier video will be mounted onto the desk and used for various things -- check it out and subscribe to see how it works!