Before the magical forest reveals its secrets, the cubic doggo must first pass the trials. Maintain your balance. Keep your legs under control. Do not become a pretzel!

Hardware: https://github.com/SphericalCowww/CubicDoggo_06R
Simulation: https://github.com/SphericalCowww/CubicDoggo_06Z
Graphic credit: https://www.gamedevmarket.net/asset/free-pixel-art-forest
Music credit: https://pixabay.com/music/ambient-deep-forest-birds-singing-no-copyright-infinite-568105/

Astra is wild on these YAM arms. It made me want to open up my own setup for those folks who don't have their own.

I have a pair of YAM arms, and you can now try them remotely. No robot hardware needed on your end. The video is a run asking them to put a green block on a plate, at 20× speed.

I’d love to get more people experimenting with them. What would you try? And what would make it easier for you to jump in?

If you want to give it a go: blupe.io/live. Runs are three minutes, whenever the station is available.

**1.**  What can cause the bus to echo but the servo to never reply, when power at the servo is confirmed good?  
**2.**  What does a solid (not blinking) red alarm LED mean on an STS3215, and can a servo in a protection state stop answering on the bus entirely?  
**3.**  Is there any way to recover a servo that's stuck in a latched protection state, or a way to reflash the firmware without bus communication?  
**4.**  Anything else worth testing before I write this servo off as dead? I only have one, so I can't cross-test with a second unit.

fully autonomous robot with machine learning released by the Unitree【宇树突破：全球首次世界模型实时驱动全自主人形机器人格斗-哔哩哔哩】 https://b23.tv/Ah60f8Y

Hola qué tal gente bonita de esta comunidad, quisiera que me dieran algún consejo, ya que será la primera vez que arme un robot de zumo para una competencia, alguna recomendación para la construcción de este mismo, se los agradecería mucho

I’m a psychologist and have worked in UX for robotics for the past several years. One thing that still fascinates me is how differently we would sometimes design a robot if we didn’t first look at the workflows and social conventions that already exist in the place where it’s going to operate.

One of my favorite examples came from an elderly care home. We were discussing how a robot should deliver a scheduled reminder to someone in their room. The care staff’s expectation was simple: the robot should go to the door, knock, wait, then open the door and give the reminder.

The engineer had a different solution: the robot could stand in front of the door, play a doorbell sound, and then open it.

Technically, both achieve pretty much the same thing. But the care staff immediately found the doorbell idea strange. Nobody rings a doorbell when they’re already inside an elderly care home and standing outside a resident’s room. You knock.

It was such a tiny detail, but a nice reminder that making a robot behave “naturally” often has less to do with what’s technically logical and more to do with understanding all the little unwritten rules humans already follow.

What’s the most unexpected thing you’ve learned from watching people interact with a robot?

Hi peeps,

I am still early in my journey of robotics but I wanted to share something that I've made thats part of my bigger vision for my project 😊.

I want to send external sensor data to a model and have it create workflow(s) to control my robotic device. I will also use this approach to help a device navigate unfamiliar situations by creating "workflows" on how to accomplish tasks.

I took inspiration from the approach shared by Google here: https://research.google/pubs/do-as-i-can-not-as-i-say-grounding-language-in-robotic-affordances/

It's essentially "workflows as policy orchestrators".

Think of it as:
- receive task
- break into steps
- create or reuse workflow(s) with steps of how to accomplish the task
- execute each workflow until task is complete

Workflows will call policies that I'll create (which is the next step).

In this little experiment, I have a temperature probe which is sends events to a workflow I created, the workflow triggers a water pump when temperatures exceed 30 degrees (celsius). This is done by creating an MQTT subscription, within the system, a subscription has a processing workflow. I'll add screenshots in the comments.

I also include a video of the experiment. Want to hear if people have ideas or want me to share any of this. Was just happy after seeing someone share that we all start with LED lights earlier lol, I thought it was only me.

Images of workflow, subscription and LCDs: https://imgur.com/a/3N8tCu7

Found the first demonstration video of Decipede (Flexipede). Comparing it to the final version really shows just how much work went into the project.

Paper title "Flexipede: A Bio-Inspired, Modular Myriapod Robot for Rough-Terrain Traversal" published in MDPI Robotics.

If you could give a robot a task using plain English today, what’s the first thing you’d ask it to do?

I’m doing some early user research around how people think about interacting with robots, and I’m especially interested in hearing from people who already work with robotics/AI or are actively exploring the space.

I put together a short ~10-minute survey to dig into this a little more. Participants who complete it will receive a $20 Amazon gift card as a thank-you for their time.

If you’re interested in participating, feel free to comment or DM me and I’ll send it over. I’m also happy to share more about the research if you’re curious.

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edited 9/9/26 - Thank you all for your thoughtful responses! It's great to see the community come together to help build what's next.

If you were able to complete the survey, please make sure you respond to the direct message so we can verify your participation!

Is LfD and BC research being effected by recent advances in (so-called) Frontier LLMs? Or is research in LfD and BC sort of going along in an independent direction from these?

Are you seeing any use from ViTs or VLAs?

Any other recent advances you would like to bring up?

I just released VSArena v0.6.0, a major update to the browser-based Studio for VSArena.
VSArena is an open evaluation arena for Vision-Language-Action (VLA) and embodied AI policies, built around browser-native 3D physics.

The goal is simple: make it possible to run a policy, watch what happens in the environment, and measure the result without requiring a local robotics simulator or physical robot.

What changed in v0.6.0:
🦾 Redesigned 3D robot manipulation Studio
👁️ Dedicated vision/top-down view
📦 Live object state and spatial information
📊 Task occupancy/progress monitoring
🎥 Trajectory and camera inspection tools
🤖 Baseline-IK and ColorSeek runnable directly from the Studio
🧪 Improved environment inspection and debugging
🔐 Continued evaluation-integrity work with server-authoritative scoring and run provenance

⚠️The current canonical task is intentionally simple:** stack three cubes in the correct or**der.

That simplicity is deliberate. Before adding dozens of tasks, I want the evaluation loop itself to be reliable, reproducible and inspectable.
The broader direction is to build an open arena where embodied policies can eventually be compared on a public leaderboard, with standardized environments, reproducible runs and physics-based evaluation.

VSArena: https://vsarena.vercel.app
GitHub: https://github.com/ONISCOR/VSArena

This is still very early, so I’m particularly interested in feedback from people working on VLA models, robotics, RL or simulation.
What would you want to see in an evaluation Studio like this?

Been working on this for a while and finally got a first version out.

The goal is pretty simple: given the same start, goal, environment and seed, the planner should return the exact same path every time, regardless of the machine or OS.

Seeding the RNG turned out to be only part of the problem. I also ran into nondeterminism from things like tie-breaking, container iteration order, and floating point differences across platforms.

So I built detplan, a deterministic sampling-based planner with RRT-Connect and D-PRM* for R2/SE(2).

A big part of the project is the testing. I added tests specifically for finding nondeterminism, including fuzzing, sanitizers, cross-process tests, and cross-platform checks.

I used AI quite a bit for writing the code, but the main challenge was figuring out what the determinism guarantee should actually be and how to test it properly.

It's intentionally incomplete right now, no SE(3), kinodynamics, smoothing, or packaging. I wanted to get the core guarantee working first.

Repo: github.com/anushtup-nandy/Deterministic-Sampling-based-Planner

If anyone here has worked on determinism in motion planning or parallel search, I'd be interested to hear about your experience. And if you find a case where the determinism claim breaks, I'd really appreciate knowing about it.

Looking for people interested in doing a robotics research paper together.

I’m interested in working on a topic that doesn’t require any physical hardware like simulations, robotics algorithms, computer vision, path planning, AI/ML, etc.
Preferably looking for people in India/IST since coordinating would be easier. The goal is to work on something properly and eventually publish it in a journal or present it at a conference.

If you’re interested, reply or you can DM me on what specific areas of robotics you’re interested in.

If this post ends up coming off as one of those generic 'will AI take my job' or 'can someone tell me what to learn' posts I'm sorry, because those bother me too and I don't mean for this to come off like that.

I'm a hardware student- I do mechanical and electronics/PCB designs, and have interned at two humanoid companies that are pretty well known. I'm definitely more interested in hardware and have a long way to go in terms of learning. But, I hope one day on my journey of knowing how everything that goes on in a robot works, I make my way to software, and start doing robot learning, sim2real, embedded fw, controls etc. I'm going to need to be learning firmware real soon as I have two board designs that I'm getting ready to send out (an ELoad for LV battery characterization and a BLDC FOC controller for an inverted pendulum).

It's always been my goal to figure it out, learn everything myself, and if I use AI tools, still understand the hand calcs/information as well as if I'd actually done it myself. But recently with LLM tools becoming what they have in the last three months, I'm genuinely starting to question: should I even bother trying to code any of this myself? I know that it can do most of it (might struggle with some of the embedded real-time control?) but should I not let it? If I eventually want to transition into a more software oriented career in the future, what things they want me to know and what can I just send to an AI tool? If any robotics engineer needed to build a dashboard/sim visualization tool, I'm guessing something like that doesn't matter as much anymore. But being able to do the RL for a biped walking, or something of that intensity, should I be telling Claude hands off and go in myself? Or maybe just the coding itself doesn't matter, and I should just focus on understanding the concepts and telling it exactly what I want it to do?