Covariant.ai and applying deep learning to robotics
indexventures.com
indexventures.com
1. "Most robots these days make use of some form of Deep Learning." This is not obvious. What is the basis for it?
2. "Robots themselves have been around forever, but, with a few exceptions, have been disappointments." In historical context, this is hardly true. Look at assembly lines, at automation, just to start.
The purpose of the CV part might vary, but for mobile robots (delivery, drone etc) it is usually to enhance localization & mapping (SLAM)by detecting features in the environment. For static robots (arms, P&P) usually object detection for pick, and segmentation+classification for placing/stacking it correctly.
Covariant.ai, Osaro and other CV in robotics start ups are simply trying to sell this as a module to existing arms, to make then slightly more flexible.
Manipulation is another task that appear deceptively simple, but is actually very complex for machines, similar to autonomous driving. Personally, any solution involving manipulation with fingers cannot be viable. Thankfully their approach appear to use a simple gripper. Most of their publication is around general RL (https://covariant.ai/our-approach). And again similar to AVs, the sim to real gap is pretty big here too.
One good thing is that warehouses is a more constrained environment and can be further structured around specific robots. And Amazon has internal robotics teams and have deployed robotic arms in limited settings. It works there because the entire warehouse is structured around robots, that's what it takes.
Thus companies such as this is a pump and flip play. There's a direct comp that's Amazon's internal division, everyone is trying to copy Amazon's supply chain efficiency these days, so the best outcome is strategic investment from Walmart and such, and then followed by an acquisition.
You saw similar companies come out in the nascent days of the deep learning hype, socher from Stanford comes into mind. His company MetaMind was sold to Salesforce for xx million amount, all the engineers got a fine pay day, they didn't really release a product. But they certainly published some nice papers along the way.
[1] https://openreview.net/attachment?id=ByeWogStDS&name=origina...
Just some food for thought, I suppose.
Good luck to both teams!
What does irk me a bit is when programmers abuse math words. Like implementing correlation and calling it convolution. Or using Tensor to describe a nested array of numbers. Sort of like nerdrage over an inauthentic movie portrayal of a comic.
But Tensorflow otherwise is just kinda descriptive. An iteration of the "dataflow graph" idea that operates on "tensors".
When you have two finger grippers, you have to consider how wide they can open (if it is too narrow, usability suffer, too wide the mechanical complexity increases), how many joints you have (too many make them heavy/low durability/expensive), and the torque/force you can put on the fingers (too high it crush the object, too low it slips out. Also more force you want, more expensive the gripper becomes).
On top of that, on the software side: if you use a suction pad you only need to estimate _one point_ of the object to be picked, and can also ignore the "grasping" problem all together. For other grippers you have to estimate several points (two for a simple gripper) and this increase the complexity for both detection (labeling of data, sensitive to center of mass in the object, etc) and grasping
There's some cool looking combinations as well, Righthand Robotics makes grippers that retracts a suction cup into a set of fingers. It looks a bit like Xenomorph.
I’d be happy to be proven wrong though if you have examples.
It's only recently that that you could use this with a high success rate in 'the wild'
This is a very impressive step on the road, but this kind of hyperbole always sets off my Segway early-warning-system.