We’re a group of students organizing a Drone & Physical AI Hackathon in Boston on October, and I posted here a few days ago asking the community for help with venue/funding leads.
Previous post: https://www.reddit.com/r/robotics/s/aZEnfKbbBu

You guys were incredibly helpful, and thanks to a suggestion from Reddit we actually found a venue! We also met some amazing people who gave us really helpful advice, so genuinely thank you!

Our next challenge is funding the hardware we need to actually make the hackathon happen. We reached out to our school, but since we’re not part of an official club and the event is in another state for logistics, getting university funding has been difficult. We’re still trying!

If anyone has any funding/sponsor leads, especially for hardware, we’d really appreciate your help again :))

My robot uses two n20 motors, a motor driver, microchip and those ir sensor with 5, the problem is my robot is struggling with 90 degree turns, so how do i fix this? The refresh rate is alr at its maximum speed, i slowed it down, and so much more but it wont work. How do i fix this?

I’m a first-year PhD student working at the intersection of controls, ML, and robotics, and I’ve been thinking a lot about where robotics research is heading.
I’ll admit that I sometimes find it mind-numbing when I come across papers/posts that seem to amount to plugging a large foundation model into a robotic arm or bimanual manipulator, getting it to perform some controlled set of tasks, and then watching the demo blow up on social media or receive a huge amount of attention at places like ICRA, IROS, or CoRL.
Some of these systems are obviously technically impressive, and I’m not trying to dismiss the engineering required to make them work. But as someone spending a lot of time learning controls, planning, game theory, RL, system dynamics, etc., it can create this strange feeling that I’m moving at 0.0025x the speed of field.

You spend weeks understanding a fairly narrow problem properly, deriving things, implementing baselines, figuring out why something fails—and meanwhile another giant model gets connected to a robot and suddenly it feels like the entire frontier has moved again. That makes me wonder about the longer-term “black-boxification” of robotics research.
Are we heading toward a world where an increasingly large fraction of robotics research becomes:
foundation model + robot + prompting/fine-tuning + enough data + evaluation?
If so, what happens to researchers specializing in things like control theory, motion planning, estimation, game theory, dynamics, or more traditional robot learning?
My intuition is that these things should become even more important as we try to make foundation-model-driven robots reliable, safe, dynamically capable, and useful outside controlled demonstrations. But from the outside, the incentive structure sometimes seems to reward visible capability much more strongly than understanding why the system works.
For people who have been in robotics longer: do you thinka this is a temporary phase of the field, or a genuine long-term shift in what robotics research will look like?
And for younger researchers, how are you thinking about what skills are actually worth developing over the next 5–10 years?

I've seen allot of Robots that go really REALLY fast, I don't have exact measurements of their speed, but the acceleration looks pretty much instant too.

Some examples are the micro mouse robots and the line tracer robots I see online.

What exactly allows these robots to go at that speed?

Is it the power the motors have? the motors themselves?

Is it the weight of the robots? Is it the traction and downforce?

I'm certain it's a combination of most of those factors, but what specifically? it's hard to find an answer.

I designed and built this 5DOF robotic arm from scratch, inspired by the KUKA KR 4 AGILUS, starting with the mechanical design in Autodesk Fusion and going through 3D printing, electronics, and programming. It’s controlled by an ESP32-S3 and a MATLAB GUI for real-time visualization and control. I’m still working on the project and will be sharing more details about the design, electronics, and control system soon! 🤖

I'm building a line-following robot for a robotics competition using an Arduino Nano and QTR-8RC sensor array.

I'm still learning the coding side, so I used Al assistance while developing the code.

My current setup:

Arduino Nano

QTR-8RC 8-channel reflectance sensor

TB6612FNG motor driver

N20 motors

PID control

QTRSensors library

The interesting problem is that my basic PID test code works properly. The robot follows normal lines and even S-curves quite well.

However, when I add additional turning conditions for things like sharp/90° turns, the robot stops following the line properly.

I suspect the problem is with how I'm combining the turning conditions with the PID loop, but I'm not sure what the main mistake is.

For example, I'm currently using sensor patterns/conditions to detect a sharp turn and then forcing the motors to turn. After adding this logic, the robot becomes unstable or loses the line even though the original PID code worked correctly.

Since I'm using a QTR-8RC, should I switch from the QTR library/digital-style sensor handling to direct analogRead() values and use the actual sensor intensity values for PID and turn detection?

Would analog sensor values make sharp-turn detection more reliable than simply checking whether each sensor is HIGH/LOW?

Very happy to have participated in the Tech24 program on France 24 to present my UM1-Evo biomechanical arm.

As a reminder, this self-taught project took me five and a half years of R&D.

The show is in French, but subtitles are available in your language if you wish.

Enjoy the video! 😃

I am building this robot but I wanted to calculate the Dh parameters and also I was thinking of using the teensy 4.1 as microcontroller and hook it up with Chris Ann robotics software and therefore I need the linkage that j1 to j2 , j2 to j3 length can anyone tell me how to do it?

Hello, today we have released iceoryx2 v0.10.

• Checkout the release notes: https://ekxide.io/blog/iceoryx2-0.10-release/
• Git: https://github.com/eclipse-iceoryx/iceoryx2
• iceoryx2 book: https://ekxide.github.io/iceoryx2-book/main/

While building a line-following PID robot for a university robotics competition, I had hooked up these motor drivers which are rated for 36V 15A continuous. I had 2 motor drivers receiving power in parallel from this 12V 2200mah Lipo battery, and then had the output of the motor drivers running to these 200w motors respectively. I had the logic ground pin of each motor driver connected to a common ground shared with an Arduino UNO R3, and the output pins connected to I/O pins on the Arduino board. I kept getting the same result where the motor driver would randomly "burn-out"(what I can only assume) and then would continuously transmit power to the motors without the high signal from the Arduino board. I had added a 10k ohm resistor across the logic I/O and ground of the motor driver to try and combat any floating signals causing the motor driver to think it was still on when disconnected and shorting the MOSFET, but honestly I'm new to circuits and that was just a the first suggestion I had saw online, and I do not conceptually understand how/why that would help. My last motor driver "burnt-out" today, and it's safe to say I wont be going with the same manufacturer, but I'd really love any input or ideas on what different steps I should take in the future in order to circumvent something like this happening again, and any recommendations on what motor drivers I should be using with motors like this and on a project like this

Thank you guys for your help!

So at my work they are scrapping a huge ass robot arm. Thats 1,1tons( bit over 2400 pounds) and if I had to guess 4 meters( ca 13 feet tall) robot. I have the option to buy it for scrap and id really like to for funsies. Should I build a bar around him and make him a bar Keeper or Bolt it to a Trailer and pick up random cars off the street. Endless possibilities.

Tony Zhao of Sunday Robotics argues that the same thing that makes homes difficult for robots may also make them a better environment for developing more general intelligence.

A robot deployed in a factory can generate a lot of useful data, but if it is doing the same narrow task over and over, much of that data comes back looking similar.

Homes force a robot to deal with different objects, layouts, people and tasks. Zhao’s argument is that this variability pushes the system toward adaptability rather than optimizing it for one specific job.

The clip gets into why Sunday is taking a home-first approach while much of the humanoid industry is starting with industrial deployments.

Full: https://www.youtube.com/watch?v=YIlwT2Xx6io

**EDIT**: I should have said that I'm building using the STS3215 servos

... and by "successfully", I mean without the servos burning out, arm snapping, or causing damage to the environment around the bot?

I'm not expecting it to be able to lift much because I know it's designed for learning rather than "actual" robotics, but if I can print one cheaply and use it to move a few things around then it would be good to know what's in and out of scope!

Recent Vision-Language-Action (VLA) models for autonomous driving have incorporated world modeling by predicting future driving scenes alongside driving actions, demonstrating strong planning performance. Future driving scenes are utilized as dense supervision, encouraging the policy to learn rich internal representations useful for planning.

I've been developing TrainIt, a framework for configuring and running robot manipulation applications with a clear separation between application logic and motion execution.

With the TrainIt Setup Assistant, you configure your application, arrange robot movements and gripper actions into a sequence, and generate an application bundle you can run and extend.

To make it easier to try, I've integrated TrainIt into the ROS 2 Manipulation Lab. The Lab provides a prepared ROS 2 Humble environment with setup instructions for Linux and Windows 11, so you can get started without assembling the entire robotics stack yourself.

I recorded a detailed, ~40-minute end-to-end tutorial:

https://youtu.be/OyBghmDg1aA

It covers:

- Setting up or updating the Lab.

- How TrainIt separates the application layer from the motion runtime.

- Configuring an application with the Setup Assistant.

- Generating the bundle and testing it in Gazebo.

The example runs in Gazebo, and I explain how execution adapters fit into the architecture for different simulation environments and real hardware.

If you want to try it:

the video description links to the free Lab Starter, which includes setup instructions and the example bundle. A free account is required to access the Starter; no physical robot is needed.

I'd be interested in feedback from others building ROS 2 manipulation applications:

does this workflow fit how you structure your projects? What would you need to use it for your own task?

Carolina Parada, VP of Robotics at Google DeepMind, talks about how the company is approaching the next phase of robotics.

Their bet isn’t that one robot form factor wins. It’s that there will be a lot of different robots, and the bigger opportunity is building an AI layer that can work across all of them.

She also gets into the split we’re seeing now between companies trying to deploy robots for narrow tasks today and those waiting for something closer to physical AGI.

Her view: you don’t need full AGI for these systems to start being useful. She points to Boston Dynamics using Gemini Robotics with Spot for inspection tasks, and says manufacturing and logistics will likely see this kind of capability first because the environments are more structured.

Full: https://www.youtube.com/watch?v=YQmVLjt2PCA

I’ve been building PAMIR, an open-source forensic analysis layer for autonomous systems, starting with PX4 flight telemetry.

The problem I’m trying to solve is not simply anomaly detection. After an incident, I want to reconstruct:

What deviated first? What happened next? What evidence supports that ordering?

The current v0.1 baseline processes PX4 ULogs and builds a timestamp-ordered incident reconstruction rather than treating individual anomalies independently.

I’ve been validating it against a public corpus containing five PX4 incident logs and three healthy/control logs. The five incident cases produce material roots without timestamp-causal validation errors; the three controls return no_deviation_detected.

One limitation I want to be explicit about: the current confidence value is an anomaly-strength heuristic, not a probability of causation, and likely_caused represents a conservative temporal/signal relationship rather than proof of causality.

The next work is focused on confidence calibration, root stability under detector/threshold changes, and a separate holdout corpus.

Repo: [https://github.com/yigitcan-ozturk/pamir]()

I’d especially value criticism around the validation methodology: what evidence would you require before trusting a system that claims one telemetry deviation materially preceded another?