I stumbled on nicrusso7's github page as I was doing my own initial research. I initially, naively, thought I could easily expand on his work and reinforcement learn my way to a working walking gait pretty quickly. However once I got a feel for the hardware and it's performance limits and the play in the system, I realized differences between the simulated model and real life would be significant, and would make debugging the translation from simulation to real life difficult. Also I'm teaching myself software development and reinforcement learning was another whole thing to learn.
So instead I took a different approach and implemented a more "conventional" gait, with inspiration and examples from other similar projects.
I've abandoned reinforcement learning for now, as I'm more interested in implementing mapping and motion planning going forward. But one day I'd like to revisit it and see how the pipeline for ML to real implementation works for something like this low level control.
As you, I was totally new to RL when I started (almost 1 year ago) - I’m a DevOps engineer during the day :)
The goal of this project for me is learning ML more than build a product - that’s why the limited focus on hw implementation. Anyway, there are examples on the web on how to run the policies on the real world robot (even using ROS) - maybe in the future I’ll digging in this topic as well!
Googling around I find some recent stuff. It seems to have started being on people’s minds in the past five years only. A lot of complicated articulated arms and inputs. I am only more convinced that there is arbitrage here... a very simple articulation mechanism, something akin to what hello robot did vs willow garage, would be totally new. And you don’t have to replicate perfectly the performance of humans because it’s a sliding scale, you could use the robot to downsize at first, following it with a small human team.
Bespoke harvesters for things like potatoes and almonds obviously destroy humans in the productivity department. If we’re still picking it that’s a good signal that automation will be tricky.
We probably have the technology to do most of it now, but you may wind up with a half million dollar robot.
This is how it is already. It just takes time to amortize, build, and operate facilities.
One thing, why use a machine learning algorithm for path finding in the maze instead of just using an actual path finding algo? Did I miss something?
If you have a quarter acre, you can have a nice garden, and it's kind of labor intensive but hopefully enjoyable, and you can get some nice food out of it.
With five to ten acres, food security is in reach, and some left over for market. But it's really rather a lot of work, and the tools farmers use to make that work easy are massively oversized for the job, and require you to lay out the land according to the logic of a much larger farm.
A pig-sized arachnid robot, with a vision module and a few replaceable tools for 'mandibles', a replaceable lithium pack for an abdomen: that could bring the amount of work back down to a part-time job.
This is a company, not a project. But backed with open-source software so that the machine learning and supplemental tools can be improved by users, I think it could be quite a successful one, and make the world more robust and anti-fragile.
With half decent radios, the brains of the bot could be stashed back at HQ, which would really improve the amount of computation available.
The physical manipulation part would take some work for sure, and the robot itself would be fairly expensive to make, but it's all pretty achievable. You need it to do more than a BobCat while costing less.
It's not "Uber for $sector" but it can be done.
Re the maze, I've got just the terrain so far - I'm working on the gym environment.. I was thinking to write 2 different versions of it, one in which the robot has a map of the maze and navigate it using a path finding algo (probably A*) and another version without any map (I'll probably need a lidar for this).
Are you hiring?
(Obviously this isn't very serious. I do however think this project is great)