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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 bu
by ghm2199 6mo ago
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 6mo agoWow 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 6mo agoTo get an idea of the thing. I would find something similar to https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/an-introduction-to-robot-learning-and-isaac-lab/03-available-robots-and-environments/02-available-environments.html 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.