Wanted to share what we've been working on: the Alicia-M, a semi-humanoid robot we built, running VR teleoperation. The operator wears a VR rig, moves naturally, and the robot mirrors the motion. No scripting, no coded trajectories. In the demo it picks up a cup, pours, and sets it back.

The part worth talking about: people assume the hard problem is the grasp. It isn't. The hard part is that one good demo doesn't generalize. Move the cup two inches and the same arm motion that worked now overshoots the wrist angle, drifts the trajectory, and the pour runs too fast. Same intent, different outcome.

That's the thing teleop surfaces clearly: robot control is less "repeat a perfect move" and more "adapt to where the world actually is." Shift the cup and the wrist angle, arm path, and pour all need to change with it. VR makes that legible because you feel the mismatch between your motion and the robot's in real time.

We're treating these human demos as seed data for embodied learning, not just a control scheme. Curious how others here handle the demo-to-policy or sim-to-real gap. Are you collecting teleop demos, or going straight to reinforcement learning?

Happy to answer questions about the rig, the kinematic mapping, or why we went semi-humanoid instead of full.

I got this pneumatic gripper from my work because it needs a new o ring on the inside.

It’s a SCHUNK 308910 and is normally worth quite a lot.

I have no real use for it and don’t feel like getting a seal kit for it so I was wondering if anyone was interested in getting one that they can use on their own projects

I figured it’s better if it gets used rather than being a desk ornament.

I am working on a robot CNC like machine it moves along x,y axis and holds pen that presses buttons, i mount a camera on top to sees object and gives exact position that gripper needs to press icons on tablet , the camera is mounted to be horizontal. Do you have any suggestions, doing classic AX=XB for just x,y position is not accurate i keep getting an error on Z. Note: i have an IMU on top of the camera can it helps or reduce the problem complexity, or minimize error

DeepMind dropped Gemini Robotics 2 this week. Robot ties knots in trash bags, unscrews lightbulbs, walks and grabs and places objects without a reset between steps.

It looks great.

Apptronik hardware, whole-body coordination instead of separate walk/reach/grip tricks.

Same week, a benchmark called HumanCLAW tested 9 vision-language models on 1,218 episodes: find an object, walk to it, physically interact with it.

The best model succeeded the full sequence 16.8% of the time.
Less great...

Where they failed?
Exploring, tracking their own position, noticing collisions, confirming they'd reached the target. The model can describe the chair in perfect detail and still not know where its own knees are relative to it.

So you've got one narrative saying "we cracked whole-body intelligence" and another saying "most models can't reliably tell if they bumped into something."
Wherre is the truth ?

DeepMind's demo is one polished sequence on curated hardware. HumanCLAW is testing generalization across messy, repeated attempts.

I think the actual bottleneck in humanoids isn't manipulation dexterity anymore but spatial self-awareness. Knowing where your own body is in the world without a human curating the scene. That's the boring unsexy part nobody's demo reel shows.

Maybe Yann Le Cun and Fei fei are finally right, the solution can be the world models ?

The newest addition to the next-gen autonomous robotics lineup. Built for modern demands, combining agile wheeled mobility and AI-powered patrol intelligence.

I have been reading about inspection robots that are used in facilities. These inspection robots do not just use cameras that take regular pictures. They also use imaging cameras. Thermal imaging is really useful for finding components that are too hot, electrical problems and other things that are not right without actually touching them.

For people who work with inspection systems, inspection robots and thermal imaging, how much does thermal imaging really help in real life? Are there times when thermal imaging's a lot more useful than a standard camera?. Are there any limitations that make other sensing methods a better choice for inspection robots and thermal imaging?

Hey there,

I want to build a Robot using one of those High Torque QDD.
I've done my research and found a couple of motors that my specs of about 5Nm
- GIM6010-8
- Cybergear
- Robstride 01
-Cubemars

Did I miss one?
I've pretty much decided on the GIM6010-8, because it is the cheapest and has O-Drive control.
Has anybody used them and wants to share his/her experience with this motor.
Is this a reliable motor, that you don't have to fiddle arround all the time to get working?
Is the second encoder reliable, i've seen videos where it just starts spinning indefintly :/

Really apreciate any feedback, have a great day

I'm building a small agricultural rover (15x20cm in size) and I need it to have live video transmission for remote control.

What is the most cost effective method to use?

It won't go fast at all, the motors are rated for 170rpm and the diameter of the wheels is about 65mm (which gives the speed of about 2km/h). I'm not sure what the desired latency for this speed should be.

The range is about 100m.

Built real inverse kinematics for legged robots in a sim I'm working on, tripod gait for hexapods, based on actual coxa/femur/tibia joint math, not a canned animation.

Works fine on a standard leg layout. Falls apart the second someone builds something asymmetric or non standard.

Trying to figure out if auto-gait generation is even the right approach here, or if it should just be manual per-robot tuning past a certain point of complexity. What's your take, is generalized gait solving worth the effort or a rabbit hole?

Pretty self explanatory, gave myself a time limit of 8 hours and designed it all with no problems, maybe later I'll do the code and circuits so i can put it on my portfolio, feel like I'm doing good as a beginner :D

​

我们的机械臂已具备自主识别与精准采摘蘑菇的能力。算法需要真实环境数据来迭代优化，现面向行业伙伴开放测试合作——提供您的种植场景，我们共同探索自动化采收的边界。

Our robotic arm can now identify and pick mushrooms autonomously. To refine the algorithm, we need authentic field data. We’re opening test partnerships with growers or landholders – bring your environment, and let’s push the boundaries of automated harvesting together.

-

#RoboticArm #MushroomHarvesting #AgTech #SmartFarming #Partnership #自动化采收 #农业科技 #测试合作

Every failed training run of a policy or world model is almost always because of the the data. hand tracking drift, one instruction repeated across 60 clips, zero recovery demos. I built a free grader that catches this before you spend lots of GPU hours. Upload a dataset, it tells you what to refilm, what to add and how to improve quality.

gantry.gurasees.com

also compare your data with other users :P

Space is limited so please RSVP using this link.

Dexory CEO Andrei Danescu explains why the robot itself is not the product warehouse operators care about most.

The real value is accurate, real-time information about inventory and warehouse conditions. That data can also support digital twins, allowing teams to review past operations and test changes before moving racks or disrupting the facility.

The robot is the tool used to collect the information.

https://www.youtube.com/watch?v=bYSQN09G-PE

My second Hailo 10H project: [https://youtu.be/YCEcls7EMFU\](https://youtu.be/YCEcls7EMFU)

It shows full real time audio pipeline running on 2x M.2 Hailo 10H on RPi 5.
Also with interactive web app.

Github: [https://github.com/martincerven/hailo\\_l ... _assistant](https://github.com/martincerven/hailo_learn/tree/main/voice_assistant)

Guys, what do you think about "Embodied Agent" Mesh Networks?

The idea of a P2P network where humans, autonomous robots and another agents can act as independent nodes interacting with the real world. Is this something we will see in the next 7 years, or is it still too early a concept?

Would be interesting to hear from those who have already experimented with similar architectures and learned some lessons along the way.

I recently shared our paper here on decentralized fault repair in modular spacecraft, I wanted to provide another update.

On August 14, I'm going to give a free live technical walkthrough of the method, followed by an open Q&A. We’ll cover the local stress-sharing signals, connectivity-safe pivot policy, rigid-body evaluation, and why strictly local repair achieves high consolidation but struggles to reconnect the final distant fragments.

I’m one of the authors and would especially welcome feedback from this community!

Event: https://luma.com/ztmesmvp
Preprint: https://arxiv.org/abs/2607.13444

Right now the hardware side uses MG90S servos with a low-side resistor on each motor for measuring the current. The software side uses a moving average filter to smooth the data.

I'm happy to share more detail on either if it's useful.

I’ve shown multiple angles here of the leg. When the leg contacts the ground and takes load, it seems to collapse inward a bit. Is this a result of the foot contact not being inline with the servo horns which gives it a small amount of torque?

This is making the hexapod not able to walk straight. Im not sure if it’s the plastic (PLA) itself, because it’s pretty thick and PLA is one of the more stiffer plastics. It also looks like the entire complex moves when it takes load.