[ghm2199]

Here is thought, this is a fixed 3d environment and you lack training data or at least an algorithm to train. Why not use RL to learn good trajectories? Like build a 3d environment of your home/room and generate images and trajectories in a game engine to generate image data to pretrain/train it, then for each run hand label only promising trajectories i.e. where the robot actually did better cleaning. That might make it a good RL exercise. You could also place some physical flags in the room that when the camera gets close enough it gets rewarded to automate these trajectory rewards.

I would begin in one room to practice this.

[indraneelpatil]

Wow okay there is a lot here, just so that I understand this correctly:

1. Make a replica of my home/ room in a game engine or a simulator 2. Generate trajectories with RL where the reward is hand specified by me 3. Automate trajectory rewards using some proximity flags

Some stupid questions: 1. How do I build a replica of my home? Is there an SFM algorithm I could use to do this just from camera images? 2. Would this still work even if things/ furniture move around the house? 3. This data collection strategy will have a distribution shift compared to real data so it might struggle with different lighting conditions and stuff?

[ghm2199]

To get an idea of the thing. I would find something similar to https://docs.nvidia.com/learning/physical-ai/getting-started...

Caveat here is you may not be able to use their environments or you may or may not have their kind or robots to train your roomba. But at-least you could get an idea of how RL training is done for robots like yours.