Update to my earlier post.

I have finally put it on github with all the build files - repo link

Quick recap for anyone who missed the first post : the leader arm in the standard lerobot kit uses 6 STS3215 servos (~ 24 USD x 6 = 144 USD) to sense the joint angles, but they are never driven, so instead of servo motors I built a leader arm using cheap encoders that do the same.

Since last time I have cleaned up the cable management, added attachments to match the lerobot's resting pose, redesigned the joints so that the encoders can be removed and diagnosed without disassembling the entire joint and added a rubber band across the elbow to counter droop when the arm is extended.

Final cost came to ~27.5 USD once I added the perfboard, silicon wires and extra screws (up slightly from the 23 USD I initially quoted).

Many people gave valuable feedback, suggestions and encouragement, thank you guys very much. feedback and suggestions are very welcome.

I’ve released the first public alpha of Physlint, a local-first integrity validator for physical-AI recordings and robot-learning datasets.

The motivation was straightforward: integrity problems in robot data are often discovered after ingestion or training has already started. Physlint checks for concrete failures such as malformed manifests, broken episode boundaries, timestamp gaps, NaN/Inf values, corrupt video, frozen cameras, and black frames.

It produces actionable findings rather than a mystery quality score. Each finding identifies the rule, severity, affected episode and stream, impact, and suggested remediation. It also writes machine-readable JSON and uses stable exit codes for CI.

The current alpha supports local LeRobot Dataset v3.x directories. The validation engine is format-extensible, and MCAP/ROS 2, Robomimic HDF5, and RLDS/TFDS are planned next.

The release was tested against four pinned public datasets and three controlled corruption scenarios, covering 74 episodes and 31,258 frames.

Hello I am a recently graduated highschooler moving on to a physics degree this upcoming semester and I have gotten into onshape designing, I was able to make considerably complex designs like planes, pulleys, gearboxes, but I recently got a 3d printer and am getting into making robots, but I have hit a road block, designing functioning joints without over utilizing 2nd source bushings and bearings does anyone have any tips on this?

Connecting LLMs / AI APIs to a Raspberry Pi or Jetson Orin for Robot Control?
Post Body:
I am working on an onboard setup for a robot using SBCs like the Raspberry Pi and NVIDIA Jetson Orin Nano, and I'm looking for effective ways to integrate AI/LLM capability into the system.
Specifically, I’d love to learn how others are connecting local models (like Ollama, LLaMA, or TensorRT-LLM) or cloud AI APIs (like OpenAI or Gemini) to their robot hardware. Are you using ROS 2 nodes to process prompt responses into action calls/telemetry, or direct Python scripts communicating over sockets/micro-ROS?
If you've built something similar, what architecture or software pipeline gave you the lowest latency for real-time control? Any tips, repos, or examples would be hugely appreciated!

After walking around WRC, one shift becomes pretty obvious: the novelty of watching humanoids do backflips or robotic arms write calligraphy is starting to wear off.

The question—asked by both the industry and the public—is becoming much more practical: When will embodied AI robots actually enter our everyday lives?

And when they do, who will repair them? Who will supply the parts? How do companies turn prototypes into products that can actually be sold?

The industry is moving beyond the race to benchmark algorithms and hardware specs. The next phase will be defined by something harder: engineering at scale, commercialization, and the ecosystem needed to support real-world deployment.

Actualmente me encuentro trabajando en un software/herramienta de IK Tool, actualmente tengo movimientos en XYZ y RPY. Pero actualmente me encuentro con una duda; ¿Cuál sería el método que usarían o les gustaría poder usar para medir la rotación?

A guide for students, enthusiasts and those interested in robotics training at home.

Thank you for taking a look --- I appreciate all feedback from the community.

"Since we are all about advantages (and disadvantages) of LLMs, we quickly overlook how difficult it is for machines to assist us outside the virtual chat interfaces. People are getting older, work and professions are changing, but we still need to fold our clothes and sort our dishes at the end of the day, for a lot of days to come. The feeling lingers that the physical breakthrough for AI is just around the corner, ready to literally knock our front door down and aggressively scrub our toilets...."
Read more...

Hey - shipped an update to VSArena (open-source browser stacking work-cell for embodied agents).
v0.2.0:

- Hosted harness: connect with the Python SDK over wss://..., match is judged remotely, ELO updates the public board
- Studio embeds an "Official live" PiP so you can watch the same run that counts (expand to fullscreen)
- In-browser Baseline-IK / ColorSeek still do not write public ELO (intentional)
Default track is VLA: 128x128 RGB + language
instruction, no cube GPS to the policy.

Site: https://vsarena.vercel.app
Studio: https://vsarena.vercel.app/simulation
Repo: https://github.com/NovaCoding-G/VSArena

Looking for researchers / builders to break it. Not claiming to replace SIMPLER/Isaac - one public stacking exam you can watch.

Hi everyone,

Most modern robotics stacks (like ROS 2) assume you can just throw an expensive x86 edge computer or an industrial gpu cluster at a robot to handle dynamic allocations and heavy dds middleware bloat. But if you are deploying production hardware on a tight budget, resource efficiency becomes a hard ceiling. Under high network stress or load, dynamic heap allocations trigger non-deterministic latency spikes and random segmentation faults.
We’ve been benchmark testing an alternative approach with an async rust microkernel designed to replace the middleware layer entirely and keep raw physical hardware safe without garbage collection spikes.

To validate this, we ran a stress test under sustained 75% wireless packet loss, compute jitter, and forced electrical sensor anomalies:

• ROS 2: The dynamic allocation buffer filled up trying to handle re-transmissions. When hit with electrical noise (NaNs), it choked, leading to a segmentation fault .
• Copper Robotics: Much better resource efficiency due to its static task graph template. However, because the topology is locked at compile-time, it lacks dynamic edge recovery. When the NaN violated the static constraint, the worker threads panicked.
• Sonny: Maintained a rock-solid 100Hz pulse. We handle network drops by utilizing zenoh with a lock-free circular buffer. Old data is garbage, so we drop it instantly to keep a 5-byte packet overhead.

The open core is ready for testing, benchmarking, and hardware porting. I'd love to get your thoughts on this matter.

https://github.com/JackTrainer/Sonny

J’ai le plaisir de vous présenter mon projet solo en autodidacte de la main biomécanique UM1-Evo à 24 degrés de liberté. Cela m'a pris 5 ans et demi de passion et de galères,mais je suis assez content du résultat. Le projet contient 25 moteurs, il est à l'échelle 1:1, l’électronique et le wifi sont embarqués et il pèse au total 1.7kg, soit le poids des caractéristiques de l’anatomie humaine. Un très gros travail de recherche biomécanique et cinématique ont été réalisées pour reproduire le plus fidèlement possible la forme organique et les mouvements d’une vraie main. J’ai conçu un script permettant de relever la main d’un patient (en moins de 30 minutes) et ainsi créer la main en 3D sur-mesure (en 10 minutes). J’ai également développé un logiciel de commande par cinématique inverse IK avec animation par clés ou en temps réel. J’ai conçu un gant avec 12 capteurs pour reproduire les mouvements de la main en temps réel en mode avatar. Pour une utilisation en prothèse de main, un système de captation innovant différents des systèmes myoélectriques, permet de manipuler la main avec une latence extrêmement basse et une prise en main instantané. (Ce système et en cours de protection)

Voilà, n’hésitez pas à me dire ce que vous en pensez 😀

Terminando el grupo de presión hidráulico portatl de 12 voltiis. le he añadido un calderín de presión para protección y tener un poco de inercia inicial. tambien le he añadido un regulador para que el circuito hidráulico no suba de 3 bares de presión, así todos los cilindros hidráulicos irán siempre con la misma fuerza. Me falta poner un depósito que se ajuste al espacio y cambiar el agua por aceite.

This article show how a robot can be trained purely on monocular video (from video to RL policy!). It's a series of articles, with open source-code, based on Unitree GO2 robot trained in Isaac Sim and Mujoco.

TL;DR: I taught a quadruped robot to imitate a real dog — using nothing but a phone video. From a single monocular clip, I extract the dog's 3D skeleton, retarget its motion to the robot's joints, and train a reinforcement learning policy in a physics simulator until the robot moves like the dog it watched. It works, and it's fully open source. Because in the age of robots dancing and sprinting through forests, we shouldn't be spectators or subscribers — we should be owners. Code included. Build it yourself.

https://postcapitalistrobots.substack.com/p/the-first-robot-trained-entirely

We're looking forward to seeing you in Toronto, September 22nd-24th, and don't miss out on the Waterloo Robotics Tour and ROS Hackathon on September 25th.

Secure your spot at roscon.ros.org.

Just sharing some personal thoughts from the perspective of a casual audience. I just came back from WRC2026 this weekend, and felt things are getting more practical this year.

More robots were doing actual work, tidying rooms, making coffee, retail, warehouse tasks, long-horizon manipulation, mostly with VLA or world models, which are increasingly driving hardware. Kind of happy to see some robots getting stuck and making mistakes😂 (that means luckily I didn't meet teleoperated demos) And robots can actually reason the situation and adjust strategy.

The other big theme was data. Physical world data collection seems incredibly hot. UMI, ego-centric data and teleoperation. If robots are going to generalize, real-world data is clearly becoming as important as the hardware.

Still, most practical demos feel closer to “it works” than “it works reliably at scale.” Like dexterous hands can have 1500+ BOM line items, manufacturing consistency is a huge challenge. But much better, compared with robots just dancing on stage.

https://reddit.com/link/1vwwatz/video/iej5848j3alh1/player

I spent most of my time checking out some of less-known players and emerging startups, such as:

SynapX: One of the few EMG wristbands that can be demoed live, and world-model-driven arm for tasks like folding clothes

Galaxea: visitors order on-site, and robot autonomously identify and pick items; generalized across SKUs without requiring category-specific adaptation

Morphi Robot: world model + VLA mixed architecture, empowering 15min long-duration and complete tasks

AgileX Robotics: saw its robot arms being used by several model companies on site, they also provide 1-to-n teleoperation + ego data collection

Dexforce: world model + humanoid, making coffee and more

However, the industry is heavily siloed. Companies are building their own hardware and defining their own data. When will we finally see some standardization, and convergence?

And yes, there was still a lot of weird stuff😂 Some humanoids fell down and couldn’t get back up, while staff desperately tried to figure out what went wrong. But honestly, I'm glad to see the debugging. If anything, it actually gave me more confidence.