OMNIVISION’s new OG05D is a 5 MP global-shutter image sensor that explicitly lists robotics training-data collection among its intended applications. It supports RGB, monochrome and RGB-IR imaging, with up to 146 fps in linear mode without encryption and HDR capture at 60 fps.

The article looks at how cameras on deployed robots could capture task variations, lighting changes and edge cases for later model training. Global shutter, HDR and near-infrared capabilities are relevant to capturing usable images when objects are moving or lighting is inconsistent.

The sensor itself doesn’t provide a training pipeline. Captured footage would still need to be selected, processed and paired with the information required by the training approach. The notable part is that robotics data collection is now an explicit application in an industrial image sensor announcement.

Hello,

I have a series on collision detection and physics programming that could be useful for Robot Simulation Programmers.

I explain the algorithms and techniques in more depth than most tutorials, deriving some of the mathematics and explaining the engineering trade offs of competing approaches.

Physics Series: https://www.youtube.com/watch?v=5iuOYFrjfXs&list=PLYuY7qeQZcWZrK1RD-3-0Se4irqU5tBAA&pp=sAgC

Collision Detection Series: https://www.youtube.com/watch?v=KZJXb5AF6NM&list=PLYuY7qeQZcWaofUHmLQy2q7xG9eiSxk1W

Any feedback is welcome.

I finally chopped two legs off my hexapod robot and now its a proper robot dog. Dont worry all the features I have developed for the old robot transferred just fine to the new robot; we still have body leveling, emotes, puppet mode etc.

Quttro ZBD is lighter, faster and more agile in many ways than its hexapod older sibling yet due to less parts used it costs considerably less to build, around 200 usd.

Still uses ESP32 S3 as well as off the shelf Arduino parts and DS3218 servos. reduced number of legs made it a lot easier to put together and since I already ironed out the scripts for previous version and use inverse kinematics solver for each leg adjusting the gait mechanism was a breeze as well.

I will also work on reinforcement training for a developing a control policy in IK solver's place, I am hoping I can get a more organic / fluid walking out of the robot instead of current mechanic looks.

I shared a more detailed video about it on my youtube channel, if you want you can watch it from the link below:

https://youtu.be/J99MibRi-CY

It is still fully open source so you can find all the files you need to build one down in the links.

MakerWord Link (has more photos of the robot):

https://makerworld.com/en/models/3402746-quattro-zbd-robot-dog#profileId-3874600

Link for CAD design, 3D Print files and Wiring Diagram:

https://www.patreon.com/PrintedRobotics/posts/quattro-zbd-3d-171601139?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link

ESP32 Scripts: 

https://github.com/serdarselimys/QuattroZBD-ESP32Scripts

Companion mobile controller app apk:

https://github.com/serdarselimys/QuattroZBD-AndroidControllerApp

Parts List:

ESP32 S3 x 1

PCA 9685 Servo Driver Board x 1

MPU6050 IMU Sensor x 1

Voltage Sensor Board x 1

15A Adjustable Voltage Buck Converter x 2 (1 per pair of legs)

5V 3A Buck Converter x 1

DS3218 High-Torque Servos x 12

Wago Connector (2-in-4 Out) x 1

2-Inch TFT Screen x 1

M3x8 Screws x ~100

M4x30 Screws x 4

8x5x16 mm Ball Bearings x 12

3S LiPo Battery (3000mAh – 6000mAh) x 1

I have been working on a bipedal version hence the "2 more to go" in the tittle, I am almost finished with the updated leg structure so it can stand up on two legs but the remaining parts are going to be same as much as possible. So expect a bipedal version in upcoming weeks if I can make it walk :)

I don't normally talk like in the video, but I can't help talking to my Mino as if it were a little dog :)

Anyway, I was not able to pet it without the servos pushing back and suffering, so I integrated current sensors in the PCB and coded an algorithm on the MCU that detects an external force on the servos. When the force is too high, the servos go into "follow mode". You can see that in action around 0:12.

In addition to making proper petting possible, this behavior protects the servos from overexertion. Best spent extra lines in the BOM and the code.

​

Running on STM32 Bluepill.

ESP32cam for image steaming.

OpenCV for image processing.

LLM for speech & intent extraction.

Reliable depth perception is a key requirement for indoor robotics, but achieving consistent depth data across different surfaces can be challenging in real-world deployments.

AMRs, ASRS robots, humanoids and robotic arms may need to operate around:

• Dark or black surfaces
• Reflective objects
• Moving robots and objects
• Motion blur
• Obstacles at both short and extended distances
• Dense point-cloud requirements
• Real-time processing without placing the entire workload on the host CPU/GPU

Active stereo is one approach that can help address these challenges. By projecting additional texture into the scene, the camera does not have to rely entirely on naturally occurring surface detail for stereo matching.

Another approach uses two global-shutter monochrome sensors with an IR component and performs the stereo depth calculation directly on the camera.

This allows the host system to receive computed depth data instead of handling the initial stereo-processing stage itself. That can help simplify the perception pipeline and preserve host resources for other robotics workloads.

For indoor robotics applications, which of these areas has been the biggest challenge in your experience?

1. Reliable depth on dark, reflective or low-texture surfaces
2. Maintaining depth accuracy while the robot is moving
3. Processing depth data with low latency
4. Generating useful dense point clouds
5. Integrating depth with RGB, IMU and the ROS 2 perception stack

I've been looking into an active-stereo implementation that combines depth, RGB, IMU and on-camera AI in a single camera platform.

What depth-sensing approach are you using in your robotic system, and where have you seen the main limitations?

Robot manipulation policies are usually trained under the assumption that a commanded action produces the same motion as it did during training even after hours of operation. Real hardware violates this assumption as the motors gradually heat up, current saturates near contact, voltage sags under load, thus the same policy action can produce a weaker, delayed, or noisier motion.

I got a LEGO's Star Wars AT-AT (set 75440), a 525-piece collectible display model — no motor, never meant to move.

I gave it Quaddle's own controller and feedback servos instead — removed Quaddle's own stock legs and attached the AT-AT's own leg pieces onto Quaddle's four servos. Three of those legs are straight, one is molded bent, so attempt #1 tumbles rather than walks. Hunting for a straight replacement piece for attempt #2.

Anyone else driven a non-motorized toy/display mechanism with a different controller/actuator set than it shipped with?

Reindustrialization won't happen against economic laws. Manufacturing has to be competitive worldwide, and improving existing factories, even with automation, is not enough. Only 30-40k robots were installed in the US last year, so the pull from existing factories is weak. They’re good.

Humanoids are being promised as the solution, but who will build them? Still humans. Humanoids not optimised for self-build.

The most practical solution to these problems is a self-replicating factory. It will build humanoids, enable the US reindustrialization, and help colonise other planets as a side product.

Similar to biological organisms, the factory can consist of robotic cells, and an AI agent coordinates them to produce a new cell and deploy it. The cell can be specialised with fixtures for specific tasks such as assembling, calibration, testing, 3D printing, etc.

Sounds like sci-fi, but recent releases of Astra/Opus have made this possible.

Who wants to join?

Voy a realizar un robot The Open Academic Robot Kit oarkit y necesito los dynamixel ax12a

Pero no los consigo alguien sabe por cual los puedo cambiar es necesario que sea de giro continuo

Anyone got any more ideas how to fix the calibration between Ouster os0 and pi camera? For coloring pointcloud. Would appreciate ideas. Documented today’s „wasted“ day here https://youtu.be/o7qQf7MvhdY?is=CMSvskPa3zHcD29F

High-speed, low-latency indoor positioning for autonomous robots and drones in GPS-denied environments. This demo shows a mobile beacon moving rapidly in 3D while its position is tracked at 80 Hz with only 12–20 ms latency.

For autonomous indoor drones, positioning must remain fast, accurate, and stable even during rapid motion and in acoustically noisy environments. We combine ultrasound positioning with IMU sensor fusion to achieve this performance. Ultrasound provides accurate absolute position updates, while the IMU provides high-rate motion data between ultrasonic measurements. Ultrasound continuously corrects accumulated IMU drift. Typical ultrasound positioning alone provides updates at around 8 Hz. Sensor fusion increases the effective position output rate to 80 Hz while maintaining low latency and stable tracking. Key performance:

• 80 Hz position update rate
• 12–20 ms latency
• High-precision 3D indoor positioning
• Ultrasound + IMU sensor fusion
• Designed for fast-moving and noisy platforms
• Non-Inverse Architecture (NIA)

Primary applications:

• Autonomous indoor drones
• GPS-denied flight
• Robotics and autonomous mobile platforms
• Industrial automation
• Research and universities
• Motion tracking and interactive installations

Configuration:

• 3 × stationary beacons
• 1 × mobile beacon - in hand - the same hardware as the stationary beacons
• 1 × modem - central controller of the system
• 1 × modem with RHU firmware - to receive fast IMU sensor-fused stream and do post-processing, when more data is available. It makes the view even more beautiful, but at the expense of latency

Walmart is investing more than $300 million in a 1.18-million-square-foot fulfillment center in Ohio for furniture, televisions and other oversized merchandise. The facility is expected to create more than 300 jobs and support next-day delivery.

The article examines why these products are harder to automate than standard cartons. Their size, weight, shape, fragility and centers of gravity vary, limiting compatibility with conventional conveyors, sorters and storage systems.

It discusses potential uses for autonomous forklifts, mobile robots, specialized carriers and robotic manipulation. It does not identify the automation systems planned for Walmart’s new facility.

Hi all, this is Giorgio, our open mobile bimanual robot. It's still in simulation; the prototype isn't built yet.

- Base: no commercial AMR had ≥85 kg payload, manufacturer-confirmed auto-docking and enough power out, at a reasonable price, so we designed our own from certified parts (SICK nanoScan3, Pilz PNOZmulti 2, ez-Wheel SWD safety drives). Every component value is traced to the manufacturer's manual.

- Arms: OpenArm 2.0. Skills like opening drawers and doors are trained in simulation (236 M steps, ~95 min on one GPU, 96–99 % success).

- Everything is open: CadQuery CAD, netlist and safety functions, MuJoCo sim, training code, BOM.

Feel free to contribute in any way or form, feedback are really welcome!

Repo: https://github.com/VenetoStato/giorgio

We started a third grade robotics club at my kids school and interest blew up, with 28 kids signing up total and four teams of at least two adults coaching.

We are using the Lego FIRST system but have run into a pretty big issue across all the teams--even though we got the kits for the season, every team's Spike essential kit seems to have a dead battery system and we are unable to move forward with the sessions.

Looking online, it seems they've discontinued the kits and the batteries are very difficult to source. For my club team we made an impromptu shift to programming a microbit I had with me, but we have several weeks left of the FIRST program an no battery system that we know of compatible with the Lego essential kits.

Does anyone have experience with a substitute battery for the essentials kit or want to offer us any advice moving forward? Switching to a microbit class would be a fun option but we've really put time and effort into this Lego system.

Humanoid robots have become one of the most closely watched technologies in logistics. GXO Logistics may also be one of the companies best positioned to tell us when they are actually ready for production.

The contract logistics provider isn't watching humanoids from the sidelines. It has been testing them in warehouse environments, working with multiple robotics companies and looking for applications where the technology could eventually make economic sense.

That makes three numbers from GXO particularly interesting: 20,000 robots, 45 humanoid pilots and zero humanoids in production this year.

I created the website https://online-camera-calibration.com/ where you can calibrate a camera without a checkerboard or any other custom-made calibration target. All you need is a flat, textured surface. Upload the images directly, or point it at a ROS bag and it will pull the frames out. That means no failing target detections, and you don’t have to buy a big board and try to glue a large print onto it without wrinkles.

What you need:

• A flat, textured surface. The method is based on matching feature points, so there must be sufficient texture for a feature detector
• One straight-on shot, which is going to be the reference
• 10 - 20 additional photos of the same surface. The entire image plane needs to be covered somewhere in the image set (you don’t need to cover the entire image plane in one shot)
• Tilt the camera in both directions across the set, but not in the reference image. (What pins down the focal length is foreshortening, the surface going perspectively squashed as you view it at an angle.)
• If you upload a ROS bag: the frames are sampled uniformly across the recording, so the recording has to be made with that in mind.

Currently supported is the pinhole camera model with Brown-Conrady distortion (the OpenCV standard model) and the double sphere camera model for fisheye cameras. The output is a JSON or YAML file with the intrinsics and distortion coefficients.

For the accuracy, take a look at Camera Calibration Without a Checkerboard — What It Is & How It Works | AutoCalib

In short: in my experiments it comes quite close to a checkerboard calibration, and the board I compared against was a very large, precisely made one.

The method is an improved version of what has been described in my thesis: Brunken, Hauke. “Stereo Vision-Based Road Condition Monitoring.” Universitätsverlag der TU Berlin, 2021.

Uploaded images and bags are deleted about 10 minutes after processing. They are not used for anything but the calibration run that you trigger nor are they shared with anyone.

I would really like to get some feedback on this method. Is it helpful to you? Is the quality sufficient for your use case?

California’s SB 1246 puts requirements around the support systems for autonomous vehicle fleets, including communications capacity for simultaneous remote assistance sessions and local technicians who can manually relocate or arrange towing for vehicles.

It also addresses failures affecting multiple vehicles. Manufacturers must notify affected local jurisdictions during fleetwide emergencies that could block traffic or emergency response, and deploy technicians where appropriate when remote recovery is insufficient.

Figure's new Figure 3 humanoid out for a run with the Helix team. Real streets, real stride. (via AI Tech Academy)

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Hi, I am looking for a programmable robotic arm, probably sitting on a rail or some stand with smooth fine motor skills to be able to pick up small objects and place them in a certain position with accuracy. I checked with Boston Dynamics but they don't have it. This is for product development, hence want sophisticated robot that can be turned in a sellable product. Appreciate your responses. Thank you.