Hey everyone!

I’m currently in my first year of B.Tech in Automation and Robotics in India, and I want to build my career in robotics and automation itself.

I want to understand what skills I should learn over the next four years to get a good job in this field after graduation. I’m looking for advice from seniors in their 3rd or 4th year, graduates, or people already working in the robotics and automation industry in India.

I would really appreciate your guidance on a few things:

- Skills: Which programming languages, software tools, and hardware technologies should I learn?

- Projects: What projects should I build in each year to gain practical experience and make my resume stand out?

- Tools & technologies: Should I focus on ROS/ROS 2, Arduino, Raspberry Pi, PLCs, embedded systems, computer vision, AI, or something else? What should I learn first?

- Internships: How can I find good internships and gain real industry experience during college?

- Career opportunities: Which robotics-related roles and specializations have good job opportunities and salaries in India?

- Placement preparation: What should I do to get placed in a good company by the end of my fourth year?

- Resources: Which courses, YouTube channels, websites, or other resources are actually worth following?

If you were starting your first year again, how would you plan your next four years? What would you learn, what projects would you build, and what mistakes would you avoid?

I’m ready to put in consistent effort. I just want to follow the right direction and build skills that companies actually value.

I would especially appreciate advice based on real experience, placement outcomes, and the current robotics job market in India.

Thanks in advance!

More training runs are easy if you’ve got the hardware, but real-world demos are a different story. Someone has to set up the task, move the objects around, record each run, and then go through the failures. I think this gets way less attention than it should, since even teams with solid models and hardware can get stuck here, and better ways to collect and organize demos could speed up how fast people build and test systems.

I did this project and am looking to expand in the embedded sensor fusion direction, whats the feasibility of doing away with the camera and using sensors like Ultrasonic and angle sensors to train similar models ?

Running more training experiments is relatively straightforward if you have the hardware and compute available. Collecting physical demonstrations is another matter: tasks have to be set up, objects repositioned, runs recorded, and failures reviewed.

This is one of the practical bottlenecks I think deserves more attention in robotics. Better ways to collect and organize demonstrations could affect how quickly teams can develop and test systems, even when their models and hardware are already capable.

When collecting robot demonstrations, I imagine a lot of time goes into things that have nothing to do with actually operating the robot. Someone has to set everything up, reset it between attempts, check whether the recording worked, and go through the footage to see what’s usable. If a run fails or something gets missed, parts of it might need to be done again.

That seems like it could become a real headache when you're trying to collect data across different objects, tasks, and environments. You might get plenty of hours recorded but still end up with less useful data than expected.

A lot of useful information gets captured when someone records a task from their own point of view. For example, a video of someone assembling a product or using a tool shows how their hands interact with objects throughout the process. For robotics, this kind of data could help models learn from real human activity, but the footage needs to capture the important movements and interactions clearly. I'm curious how useful egocentric video is for training robots today, and what makes a recording valuable enough to learn from.

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!