3 ms·
I don't find this particularly impressive as well. Anything except a pod machine would likely present the robot with tasks it can't handle like e.g. handling ac
by fabian2k 3y ago
I don't find this particularly impressive as well. Anything except a pod machine would likely present the robot with tasks it can't handle like e.g. handling actual beans or ground coffee and filling water. And most household tasks would often involve this kind of task that is not entirely straightforward as your house and its contents aren't designed with limited robots in mind.
- steveBK123 3y agoThis Keurig queued up perfectly use case is fundamentally unimpressive because I could expect my 5 year old nephew to pass it with 1 minute of verbal direction or watching me do it once, not 10 hours of deep learning. Now expand the Keurig use case to the real world. Present it with a machine: * filled / not filled with water * used pod bin empty / used pod bin filled * pods available / pods in a box on counter / pods in a box in the cabinet * cup on counter / cup in cabinet / cup in dishwasher / cup in sink We now have ~50 permutations of even the simple Keurig use case and let me know how many years of training to accomplish even this. All of this to make fairly bad coffee. We haven't even gotten into your example of actually making real coffee.
- xbar 3y agoI am more impressed than you. A general purpose robot can learn new tasks in a modest amount of time. As you argue, fluidly chaining sequences of learned tasks together is tricky, but current AI technology is capable of solving nearly all those problems within a handful of years. With centralized learning labs, over-the-air updates, and a 2x or 3x improvement in execution speed of precision tasks, I think this type of solution is going to be ubiquitous within the decade.