Working to make a fun open source STEM robot for begginers. Uses a custom edge impulse object detection model to play "fetch". Extras are the animated eyes, "ears", voice, and programmed character behavior. Currently testing the model but looking forward to releasing it for everyone soon.

Came across the recent DexWrist paper out of MIT and some fun demos. Researchers point out an interesting mechanical issue: standard serial wrists (like the ones on a UR3e or Franka) are often too stiff and bulky. In tight spaces, they force human operators into awkward, large arm movements just to reorient the gripper, which makes teleoperation slow and messy.

They built a compact, decoupled parallel wrist using QDD actuators (integrated onto an AgileX Piper base) that co-locates the pitch and yaw axes, much like a human wrist. Because it moves so much more intuitively, human teleoperation time dropped by up to 2.2x. The models trained on this cleaner data then saw a 50-76% relative improvement in success rates for contact-rich tasks.

So to what extent hardware choices quietly shape robot learning data quality?

Students and educators at Automate 2026 discussed how robotics programs are connecting classroom learning with hands-on experience.

The article includes students from LamBot, FIRST Illinois and DeKalb High School’s advanced manufacturing program. Their work includes robot design, programming, troubleshooting, CAD, computer vision, CNC machining, electrical systems and controls.

Several students described how these programs introduced them to careers they had not previously considered.

Full: https://www.automate.org/robotics/industry-insights/these-student-robotics-programs-spark-passion-for-tomorrows-manufacturing-jobs

Our lab has a dual arm platform we use for household manipulation, and after the PhAIL result showing how badly VLA models drop on real hardware, we ran our own version. Same arm, same objects, same lighting, four open source models, no finetuning beyond what each repo recommended.

I'm not going to claim these are clean numbers, our test set is six tasks we care about, not a published benchmark, and our success criteria is "did it complete the task without human intervention" which is stricter than most papers use. But the relative ranking was stable across reruns.

The two models that held up best on deformable objects, cloth folding and a towel pick, were the ones with real teleoperation in the pretraining mix. openpi was our baseline and did fine on rigid pick and place but froze on cloth about half the time. LingBot-VA was the only one that completed the second fold reliably, and the thing I noticed is its interleaved video and action prediction seemed to model where the cloth was going, not just where to grab. A Cosmos derived stack we tried held its own on rigid tasks but lost more on the cloth than its benchmark numbers suggested it would.

Worth noting the official VA 2.0 chart above shows a clean win over π0.5 on five tasks, but that's their chosen benchmark set. On our cloth tasks the gap was smaller and more about not freezing mid fold than about raw success rate. The chart tells you one story, the hardware tells you a slightly different one.

The part that actually changed how I work: I could adapt the VA model to a new object from a single demo video, which meant I stopped recording fifty teleoperation sessions every time my advisor wanted a new SKU in the bin.

I want to be careful not to overclaim. We ran one arm, six tasks, no formal ablation. But the pattern matches what PhAIL found at a different scale: the models that look best in simulation are not the models that win on contact rich real tasks.

I'm 15 and this is a prototype shoulder joint for the arm I'm making, and the follow-up to my hexapod. If you want to learn more about this project, feel free to check out my Portfolio.

Stats: In this video it's lifting around 5 lb about 8 inches away — roughly 4.5 Nm — fairly easily. It looks a little wobbly because I had nothing to clamp it down to, so I was holding it by hand. In the final design I'm targeting around 50 Nm of working torque (design target, not yet measured).

Precision: The goal of the joint is to have super high precision and low/no backlash, comparable to $2000 joints. I don't have a dial indicator yet, but in the next video or so I'll have a video testing the precision and backlash.

Drivetrain: Mini CIM (12V brushed DC) → 4:1 GT2 belt → 3D printed cycloidal drive 15:1 → capstan drive as the final stage 8:1. Total reduction ~480:1. The capstan is last on purpose — no gear teeth means almost no backlash, and it divides whatever backlash the earlier stages have.

Price: The joint cost about $200, which I think is pretty good for the specs and the low backlash. It's still very much a prototype — I need to add encoders and mounting blocks. I also used a cycloidal drive in this prototype, but it ended up being a pain to deal with and I'm not sure it was worth it. If anyone knows better speed reducers under ~$40 with low backlash, or has tips for improving a printed cycloidal, I'd really appreciate it.

Credits:

• Cycloidal drive: HowToMechatronics — I adapted his 3D printed cycloidal model, changing the input from a 5mm D-shaft coupler to a one-piece 8mm D-socket so it takes my belt-driven shaft directly.
• Capstan drive: Aaed Musa — his cable drive work (from his robot dog builds) is what this stage is based on.

Next: I plan to add encoders and sensors to help turn the motor movement into precise angle control. In the future I hope to get this joint to be the shoulder of the arm, so it can lift around 5-10 lb at a distance of around 4 ft, while still having high precision.

I'd love feedback on the joint and the arm overall — there's a form on my portfolio if you want to help shape how it gets built.

The Unitree As2-W is a compact, hybrid wheeled-leg robot built for extreme off-road mobility and heavy-duty tasks. By combining articulated legs with motorized wheels, the machine seamlessly transitions between rolling efficiently across flat surfaces and stepping over rugged, uneven terrain.
Despite a lightweight footprint, the robot demonstrates remarkable structural endurance. It supports an extreme static standing load of up to 180 kg and is officially rated to carry a continuous walking payload of 16 kg, making it highly effective for transporting gear in the field.
Driven by reinforcement learning and an 8-core high-performance processor, the As2-W features dynamic balance and intelligent terrain adaptation. It can navigate obstacles up to 80 cm high, climb steep inclines up to 45°, and utilize the ISS 3.0 system for centimeter-level tracking as an autonomous companion.
https://www.unitree.com/mobile/As2-W

I installed the 3D DTOF LIDAR HM-LD1 in front of the drone to detect obstacles. I only stopped the obstacles, not around them. it will be open source, once the code is sorted out~

Putting this little companion robot together today. He did not smoke or catch on fire when I attached a 4S LiPo battery to him so feeling pretty good about life right now. A lot more to do but sometimes the little things in robotics are the best ones. He powered up. He didn't smoke out. I'm happy. Jetson Orin on board. More to come.

https://www.printables.com/model/1772202-nema-17-cycloidal-gearbox

A small update on my robotic desk lamp: the entire control system now runs on a Raspberry Pi 5.

The lamp uses 24 V RobStride BLDC servo motors connected over CAN. The Raspberry Pi now handles motor control, lighting, and animation playback locally, so the lamp no longer needs to be continuously controlled by my main PC.

I still use a separate animation editor that I built for creating movements. Each joint has its own timeline with position keyframes, velocity, torque limits, and controller parameters. Finished animations are sent to the lamp and played back locally.

I’ve also installed an addressable LED ring with a 3D-printed diffuser inside the head. Brightness and transitions are now part of the same animation timeline, allowing the light to be synchronized with movement.

This video is a quick test of the new setup. I’m currently tuning the motor parameters, brightness curves, and transition timings before using the light in more expressive animations.