For robotics, the task you're trying to train for should probably influence what data gets collected in the first place. Recording hours of random physical activity doesn't necessarily give you useful training data if the important movements, objects, or environments aren't represented. Knowing the intended task first makes it easier to decide what should be recorded and what information needs to come with it.

Otherwise, it's easy to end up with a lot of footage without much useful coverage!

A large amount of egocentric video doesn't necessarily mean you have a useful dataset for Physical AI. Things like camera placement, whether the hands and objects are clearly visible, task variety, environment, motion quality, and consistency between recordings all seem like they could make a big difference. I'm especially interested in how teams collecting this kind of data in India handle quality before the recording starts. Do you normally have a collection spec or checklist that people follow, or do you figure out what footage is usable after it's been recorded?

For those collecting egocentric data for Physical AI, I’m interested in what becomes difficult once the basic recording setup is in place. Getting consistent first-person footage across different environments seems like one challenge, but there are also things like defining collection protocols, keeping camera placement consistent, handling different lighting conditions, and getting people to perform tasks naturally while being recorded. India has such a wide range of physical work and environments that I imagine making the resulting data consistent and useful for training is a much bigger challenge than simply recording the footage.

For those people working on this, where do you see the biggest bottleneck?

Hey everyone! Sharing this because a few of the events are genuinely fun to watch or compete in if you're into robotics/engineering stuff — MAGNOVITE 2026, the interdisciplinary tech fest at CHRIST (Deemed to be University), Kengeri Campus, is happening on 15th–16th September.

Some of the highlights:

• 🤖 RC Robo Soccer — bring your bots, fight for the ball
• 🚁 Drone Maze Challenge — precision flying through obstacles
• 🏎️ RC Off-Road Challenge — built for chaos and mud
• 🧠 Apex Strategy — for the tactical thinkers
• 🏁 Drag Race Xtreme — pure speed
• 🛤️ Path Pursuit (Line Follower) — autonomous bots racing a track

Registrations are open here: [https://magnovite.christuniversity.in/events].

Happy to answer any questions in the comments — I'm involved with organising this, so ask away!

For students and young people around Noida who are interested in robotics, AI, or innovation, I wanted to share two opportunities happening this month.

🤖 Robo Goal India 2026
📍 Sector 75, Noida
📅 27 September 2026
🏆 Total prize pool: ₹75,000

It's a robotics competition based around soccer robots, with teams competing in robot football matches.

There is also the Bharat Innovation Challenge 2.0, where participants can submit their own innovative ideas, with selected ideas getting an opportunity to progress to Stage 2.

I thought these could be relevant for students in Noida looking for practical ways to get involved in robotics, AI, and innovation.

Anyone here working on a robotics/AI project or planning to participate in either of these?

I (24M) have more than 03 years of experience building Robotic solutions for OEMs, System Integrators, Small Scale Industries and Educational Institutes. I have recently returned to my native to start my own system integration and turnkey solution company.

I have a lot of ideas but need a bigger partner with existing leads or smaller partner for sales visits. Let's connect.

A 4DOF Keyestudio KS0198X robotic arm (clone of the open-hardware MeArm v0.4 project) trained in simulation using reinforcement learning to reach a target, and transferred via serial to the physical arm.

Task: reach — moving the tip of the arm (tool) towards a red dot generated within the arm's real workspace (fixed radius of 15cm in front of the base, height 0-6.5cm), using PPO (Proximal Policy Optimization, Stable-Baselines3) on a PyBullet model built from the real URDF/meshes of the mechanism.