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209 points alexcos | 2 comments | | HN request time: 0.484s | source
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dchftcs ◴[] No.44419191[source]
Pure vision will never be enough because it does not contain information about the physical feedback like pressure and touch, or the strength required to perform a task.

For example, so that you don't crush a human when doing massage (but still need to press hard), or apply the right amount of force (and finesse?) to skin a fish fillet without cutting the skin itself.

Practically in the near term, it's hard to sample from failure examples with videos on Youtube, such as when food spills out of the pot accidentally. Studying simple tasks through the happy path makes it hard to get the robot to figure out how to do something until it succeeds, which can appear even in relatively simple jobs like shuffling garbage.

With that said, I suppose a robot can be made to practice in real life after learning something from vision.

replies(4): >>44419561 #>>44419692 #>>44420011 #>>44426961 #
1. namibj ◴[] No.44419561[source]
If the robot already knows "how to" the happy path, the training difficulty falls severely at least if it can continue after a recovery.
replies(1): >>44419906 #
2. dchftcs ◴[] No.44419906[source]
The tasks you do to recover from the failure is often different from the happy path. For example, the happy path of dumping garbage is carrying a garbage bag to a collection bin. The non-happy path is that the bin is overflowing and you have to put the bag on the ground, or if the bag leaks and you need to move to a new bag, or if the bag breaks entirely and you have to pick up the trash again.

But yeah, I think a better way to put it is that sampling the happy path would indeed make the failure case easier, but sampling just happy paths is far from sufficient from completing even some of the simplest human tasks with failure.