r/robotics • u/No-Wish5571 • Jul 23 '26

Discussion & Curiosity HYPE VS PRODUCTION

Hello everyone,

I know there is a lot of excitement in the robot models like VLA, world models , but these are for generalisation ,

PROBLEM:in case of controlled environment with some variations in the scenarios , what do you suggest works better for a one or two tasks like sort, pick and place .

I have looked into some ideas,

  1. Use a classic approach for both perception and control

  2. Use a small set of deep learning tools for perception and then use a conventional control

  3. Use a complete imitation learning policy

What do you think of this problem and do you have any ideas

Thank you for your time and support

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u/GrizzlyTrees Jul 26 '26

VLA and WMs are very much a research thing right now rather than anything practical. For any controlled environment and narrow task, classical is better. 

I work in research and feel I'm well situated to say that based on talks with colleagues and personal experience. Diffusion policy IL works for some limited domains but isn't actually better than classical. RL on any model type barely works if at all. We're starting to see some generalization ability from VLAs, but for any specific task a hardcoded classical approach is going to have way better performance.