Grasping Robots Compete to Rule Amazon’s Warehouses
wired.com
wired.com
It's not even just picking something up, it's picking one specific item out of an arbitrarily messy or overstuffed bin without dislodging or dropping anything else. Items in a bin can be wedged anywhere, in any position, in nondescript boxes only identifiable by the ASIN, or a vague natural language description. It's a hard visual logic, inference and manual dexterity problem made harder by unpredictability and harder still by the need to meet quotas, and every job has its own mess of them.
And on top of that, employees in Amazon warehouses are often cross-trained to save costs, which means you either need one robot that can unload, decant, move juice carts, pick, stow, pallete-stow, etc, at a moment's notice or else multiple dedicated robots for each task.
It's something humans do so easily that Amazon can justify paying almost nothing for the work, but still way beyond what AI and robotics can achieve. The Kiva pods seem to be the current state of the art for Amazon warehouses, and all they seem to do is move bins around (and can't even get that right sometimes.)
The problems start when things are tightly packed. One object might be in the way of your gripper so you can't take the thing you want. What happens if you drop the object? Does your robot know how to move things aside to grab the item you want?
And on top of this you have the whole issue of detecting objects in a cluttered environment: this requires state of the art semantic segmentation, robust 3D vision that will work on non-cooperative targets and a classifier that can work with occlusions and huge numbers of objects reliably.
I think it's reasonable that some items can be scanned upon entry into the warehouse, e.g. on a laser triangulation turntable that also grabs a video as it rotates. With this sort of task you want to exploit as many priors as you can find. If you look at papers (and factories), you see a lot of tricks: objects on flat surfaces that make segmentation easier, small numbers of known objects, etc.
If it could be done quickly enough with enough volume, maybe, but the constraint is that items have to be loaded off the truck, scanned and stowed into a bin (which makes them available for purchase on the site) as quickly as possible.
Simple algorithm for that : Annoy (pun intended) towers :-)
I wonder how they pick up books using this method?
Books are difficult because they appear to have hard surfaces on the side, which turn out to be not so hard and not suitable for gripping.
Also books, unless they're shrink wrapped, have a tendency to open which I imagine would be a pain.
Mimicking that mechanically is a major challenge, it has nothing to do with AI per se.
It's not a trivial challenge in any form really.
Also, there is an interesting theory that humans hands not only evolved to grab things, human hands evolved to form "efficient" fists for punching.
http://www.latimes.com/science/sciencenow/la-sci-sn-human-fi...
For what it's worth I for example only learned to tie my shoes when I was already 9 years old, while I had learned to read almost all by myself at 5 (my dad had taught me the letters of the alphabet).
Reliable force feedback data at our hand's level of detail would provide a wealth of data to those making the control software and that better software could benefit from real time data. The two problems are strongly interconnected.
Thing is that each of us accumulate prior data that we can use as a basis for future action, things like how much pressure is enough for grasping a raw egg without breaking it.
I would like to point that with pair of tongs you will still have force feedback. Even though is will be reduced in precision it will still be better than any artificial system I am aware.
http://www.takktile.com/main:tech
There are other methods of figuring out how much force a hand is exerting too.
EDIT: Pun retroactively intended.
This makes me wonder. Why did people compete for the Netflix prize, [1]? Were those IP terms better?
They would have probably been really surprised that we'd have a superhuman Go player before we had a robot that was just average at picking up arbitrary items.
Interestingly the myelin sheath around your peripheral nerves thins as you grow. I have always assumed this is to reduce capacitance so that signal propagation time between (say) your fingertips and brain remains roughly constant as your arm gets longer. I don't know if anyone has ever studied this though.
Myelination continues Into adulthood btw -- you can see a change in the distribution of white matter due to learning a new language for example.
I've never worked with machine learning in robotics, but my understanding is that physics simulators are still insufficient environments for training state-of-the-art robotic agents. Especially when using reinforcement learning or a similar algorithm, many models are trained with several real robots picking up real objects.
It really seemed unfair that he had had the fun of building the robots and than said I shouldn't do it. It took years to realize what he was really saying (a statement I could make about many things he said).
They are getting better these days, but let's not get too breathless about it. That headline is very misleading.
I'm increasingly convinced that amazon has opened themselves up to competition on simultaneous price and performance fronts, even with relatively few robots and a unionized and well paid work force. A few key structural decisions made up front by intelligent and informed people will make all the difference.
That's interesting! What are these decisions? I'm guessing there is a natural increase due to Amazon's growth but are they making any structural changes to make the system more unwieldy?
2) Warehouse growth. The underlying assumption is that if you're closer to the customer, you save more money on transportation. That's true to some extent, but they fuck it up in other ways: fragmenting inventory resulting in split shipments (a way bigger deal than most people realize, I wouldn't be surprised if this alone overwhelmed the savings from shorter linehauls), increasing safety stocks, truck endpoint combinatorial explosions, truck scheduling issues, inventory balancing problems, reduced truckload utilization factors from reduced shipping volumes per warehouse and destiation. Larger warehouses have their problems, but compared to the issues that more warehouses cause, they are very solvable in comparison.
3) Random stow processes and path-based picking are hard wired into everything about their operations. From their software to their warehouse design to their metrics. It works phenomenally for books, but not so well for things that aren't physically shaped like books or have sales distributions like books. Inbound costs with random stow are orders of magnitude higher than case-level stow for case-packed items, experience exponential growth in costs when inventory shelves are at high levels of utilization, and random stow combined with path-based picking means your pick rates decrease almost linearly with inventory turns (more inventory per unit sold means walking farther between each pick which means more labor cost per pick, and prioritized placement to fix the pick cost problem just shifts costs towards a much more complex and costly stow process).
4) Refusal to reevaluate decisions made in the past that no longer apply. Two big examples are random stow processes, and inventory selection.
- Random stow vs other stow methods were evaluated back when Amazon was a bookstore, and they were an astounding win. Now that Amazon sells everything, there are plenty of methods that are vastly superior, and even the possibility of different methods for different products, but they've built out several hundred warehouses under the assumption of random stow and would require monumental efforts to retool them and write the software to support it.
- Inventory selection is seen as a universal good and is never going to stop growing. That's fine for a business growing into new sectors and categories, but when you have 3000 different brands and models of staplers, you've passed the point of marginal benefits exceeding marginal costs. The effect on inventory turns, warehouse growth, and warehouse operations alone is staggering.
5) Their Kiva robots work beautifully in small warehouses with low shipping volumes, but have severe scaling issues in larger warehouses due to inherent design problems. They have centralized path planning which is an NP-complete problem that can't keep up with the real time requirement at large warehouses. They require whole-warehouse shutdowns every time a single piece of inventory falls off a shelf. They experience physical deadlocks due to inventory being tied up in multiple conflicting processes at the same time. Sure, they eliminate picking labor, but they replace it with decreases in stow and packing productivity, and unseen to it all is that the fact that dependence on Kiva means small warehouses which means you further explode the logistics problem by having more warehouses and more fragmented inventory.
6) They introduced and scaled FBA without item-level inventory accounting. Now, due to co-mingling, fraud and piracy are a systemic problem. And it's not entirely solvable without item level accounting...which would require physical and technical changes to every step of the process all the way from the shipper to the warehouse(s) to the customer.
If I had to summarize Amazon's core problem, it is this[0]: they optimize the things that are easy to measure, because they don't know how to think about the things that are hard to measure. Warehouse labor is easy to measure...the effect of growing their warehouse network on logistics and inventory management is not. So they grow their warehouse network, see their costs increase, shrug their shoulders, and then double down on labor costs because that's the only thing they can understand.
I'm currently working in an 800,000 foot Amazon warehouse that uses Kivas, where inventory falls out of the bins constantly, and they don't even shut the whole thing down if the pods crash into each other. They just shut off a section of the pods and send someone in to clean the mess up.
I'm not saying there aren't problems - I could go on a rant about the issues I've seen with their internal software and UI design, not to mention bin design and the way it seems pods are allocated - but it doesn't seem to be that broken.
Amazon wants your first thought on the question "I want XYZ, how do I get it" to be "Amazon".