4 ms·

Teaching Robots to Understand Semantic Concepts

137 points by rey12rey 9y ago

QAPereo 9y ago
To reference an earlier article on HN, this reads like a future of, "Robot, observe these field workers picking fruit for a week. Now practice in this field for a day. OK, now the job is yours. What I can't tell at all from this article is whether that day is years or decades away.
- deleted 9y ago
  [deleted]
- buttly 9y ago
  How can we even really differentiate years from decades when there is potential for building on top of research and radical acceleration at any time.
- Swizec 9y ago
  > whether that day is years or decades away Yes, it's both. Depends on the task. Afaik we can already program industrial robots by showing them what to do. Robot records movement in its actuators, then keeps replaying over and over. And we already have machine learning agents that can observe your behavior and learn which news stories are "good" news stories and which aren't. (algo newsfeeds). You could use them to observe a magazine editor and after a few issues, you'd have a robo editor.
  - deleted 9y ago
    [deleted]
  - webmaven 9y ago
    That's not "showing them what to do" it is more along the lines of "hand-holding".
- gallerdude 9y ago
  Yeah, I think “easily programmable humanoid” will be up there with the smartphone, maybe even the computer in terms of impact. Imagine walking into a restaurant, telling the app what food you want, and having a robot walk it over to you. Imagine going to a retail store, giving a voice query of what you’re looking for, and have a robot bring back the best candidates. Imagine owning an Etsy shop, but having a robot automate the entire creation process. Imagine...
- ragebol 9y ago
  https://xkcd.com/1425/ https://xkcd.com/1425/ It reall, really depends on the job. And in a job, maybe some subtasks can be automated. Perhaps the picking cannot be automated, but the transportation can. Or instead of computer vision, have a person click the fruit on a screen so the robot know where to pick.
  - briga 9y ago
    Crazy to think that something that was considered "virtually impossible" (image recognition) a few years ago is relatively easy today.
- joshvm 9y ago
  This is unlikely to be how the fruit pickers of the future work (though I get your point). Using a robotic arm like this is an extremely expensive way of solving the problem. That's ignoring the technical challenges like accurate localisation of fruit in small clusters, not damaging crop, dealing with occlusions from leaves, etc. My personal opinion, having worked a little on this problem, is that it's very much like autonomous driving. Getting 90% of the job done is fairly straightforward, but getting that final 10% to make a system commercially viable will take years. Commercial growers don't (yet) have any pressure on labour - it's too readily available and too cheap. http://www.agrobot.com/ http://www.agrobot.com/ solves this in quite a neat way. Rather than grasping the fruit, they just scoop up each berry in a cup with a blade on one side which severs the stem. This means you don't care about the precise shape of the berry either.
skywhopper 9y ago
The headline seems to misuse the word "semantic" (not to mention "understand"). Does the door-opening robot now understand how to open all hinged doors with a similar opening mechanism? Or was it just trained to imitate a sequence of changes in a 2D image from a fixed angle? Can the same software and robot also be taught to open windows? Boxes? We are talking about "semantics" explicitly here. Does it understand "open" versus "closed" for these different types of closures/portals? I don't want to discount the value of this research. It's absolutely necessary to do this sort of basic proof-of-concept testing of these ideas. But the claim being made implicitly here is way beyond what's actually going on. The software understands nothing, and the "semantics" extend to simple image-matching of objects, but there's no deeper meaning associated with the labels, so I think calling that "semantics" is a major stretch. This approach is not going to teach a robot how to pick fruit, or serve food, or clean floors anytime soon. In the best case where this is even a workable approach, research like this is just the first of millions more tiny steps along the path. Anyway I think it's naive to assume that a good way to approach automation is to write software to let robots learn by watching humans do the desired task. As cool as that sounds, chances are that approach would ultimately be a massively inefficient way to solve the problem. It'd be like trying to invent the automobile by building a steam-powered horse robot that can tow carriages. The critical purpose is being overlooked in favor of a cool-looking but totally impractical toy demo.
- suyash 9y ago
  That seems to be what they are aiming towards but true AI is not the same as Machine Learning. Google is still using what we could call a very rudimentary form of AI as they describe "Unsupervised learning on very small datasets is one of the most challenging scenarios in machine learning. To make this feasible, we use deep visual features from a large network trained for image recognition on ImageNet".
  - Vivian99 9y ago
    Since I started with my online business I earn $95 every 15 minutes. It sounds unbelievable but you wont forgive yourself if you don't check it out. ----------------->>http://www.joinmate2.Com http://www.joinmate2.Com
richard___ 9y ago
Summary-- Semantics part: Seems like the idea is we can "transfer" knowledge from prior labeled samples so that we don't need to do as much new work labeling sample images with semantic labels. Grasping part: "Emulating human movements with self-supervision and imitation." High-level imitation based on visual frame differences avoids needing to manually control actuators. Not sure how this works exactly Two-stream model: ventral network asks What class, dorsal network asks Is this how we should grasp this object. The benefit is that we can make use of all the automatically generated (robot-generated) grasping data without having a human supervise all that automated grasping, e.g. "This process is a successful way to pickup this object, and also this object is an apple." The ventral network ties back this the grasping data (without object labels) to object labels, which allows for semantic control of the trained robot e.g. "Pickup that apple".

137 points by rey12rey 9y ago

To reference an earlier article on HN, this reads like a future of, "Robot, observe these field workers picking fruit for a week. Now practice in this field for a day. OK, now the job is yours. What I can't tell at all from this article is whether that day is years or decades away.

deleted 9y ago
[deleted]
buttly 9y ago
How can we even really differentiate years from decades when there is potential for building on top of research and radical acceleration at any time.
Swizec 9y ago
> whether that day is years or decades away Yes, it's both. Depends on the task. Afaik we can already program industrial robots by showing them what to do. Robot records movement in its actuators, then keeps replaying over and over. And we already have machine learning agents that can observe your behavior and learn which news stories are "good" news stories and which aren't. (algo newsfeeds). You could use them to observe a magazine editor and after a few issues, you'd have a robo editor.
- deleted 9y ago
  [deleted]
- webmaven 9y ago
  That's not "showing them what to do" it is more along the lines of "hand-holding".
gallerdude 9y ago
Yeah, I think “easily programmable humanoid” will be up there with the smartphone, maybe even the computer in terms of impact. Imagine walking into a restaurant, telling the app what food you want, and having a robot walk it over to you. Imagine going to a retail store, giving a voice query of what you’re looking for, and have a robot bring back the best candidates. Imagine owning an Etsy shop, but having a robot automate the entire creation process. Imagine...
ragebol 9y ago
https://xkcd.com/1425/ https://xkcd.com/1425/ It reall, really depends on the job. And in a job, maybe some subtasks can be automated. Perhaps the picking cannot be automated, but the transportation can. Or instead of computer vision, have a person click the fruit on a screen so the robot know where to pick.
- briga 9y ago
  Crazy to think that something that was considered "virtually impossible" (image recognition) a few years ago is relatively easy today.
joshvm 9y ago
This is unlikely to be how the fruit pickers of the future work (though I get your point). Using a robotic arm like this is an extremely expensive way of solving the problem. That's ignoring the technical challenges like accurate localisation of fruit in small clusters, not damaging crop, dealing with occlusions from leaves, etc. My personal opinion, having worked a little on this problem, is that it's very much like autonomous driving. Getting 90% of the job done is fairly straightforward, but getting that final 10% to make a system commercially viable will take years. Commercial growers don't (yet) have any pressure on labour - it's too readily available and too cheap. http://www.agrobot.com/ http://www.agrobot.com/ solves this in quite a neat way. Rather than grasping the fruit, they just scoop up each berry in a cup with a blade on one side which severs the stem. This means you don't care about the precise shape of the berry either.

skywhopper 9y ago

The headline seems to misuse the word "semantic" (not to mention "understand"). Does the door-opening robot now understand how to open all hinged doors with a similar opening mechanism? Or was it just trained to imitate a sequence of changes in a 2D image from a fixed angle? Can the same software and robot also be taught to open windows? Boxes? We are talking about "semantics" explicitly here. Does it understand "open" versus "closed" for these different types of closures/portals? I don't want to discount the value of this research. It's absolutely necessary to do this sort of basic proof-of-concept testing of these ideas. But the claim being made implicitly here is way beyond what's actually going on. The software understands nothing, and the "semantics" extend to simple image-matching of objects, but there's no deeper meaning associated with the labels, so I think calling that "semantics" is a major stretch. This approach is not going to teach a robot how to pick fruit, or serve food, or clean floors anytime soon. In the best case where this is even a workable approach, research like this is just the first of millions more tiny steps along the path. Anyway I think it's naive to assume that a good way to approach automation is to write software to let robots learn by watching humans do the desired task. As cool as that sounds, chances are that approach would ultimately be a massively inefficient way to solve the problem. It'd be like trying to invent the automobile by building a steam-powered horse robot that can tow carriages. The critical purpose is being overlooked in favor of a cool-looking but totally impractical toy demo.

suyash 9y ago
That seems to be what they are aiming towards but true AI is not the same as Machine Learning. Google is still using what we could call a very rudimentary form of AI as they describe "Unsupervised learning on very small datasets is one of the most challenging scenarios in machine learning. To make this feasible, we use deep visual features from a large network trained for image recognition on ImageNet".
- Vivian99 9y ago
  Since I started with my online business I earn $95 every 15 minutes. It sounds unbelievable but you wont forgive yourself if you don't check it out. ----------------->>http://www.joinmate2.Com http://www.joinmate2.Com

richard___ 9y ago

Summary-- Semantics part: Seems like the idea is we can "transfer" knowledge from prior labeled samples so that we don't need to do as much new work labeling sample images with semantic labels. Grasping part: "Emulating human movements with self-supervision and imitation." High-level imitation based on visual frame differences avoids needing to manually control actuators. Not sure how this works exactly Two-stream model: ventral network asks What class, dorsal network asks Is this how we should grasp this object. The benefit is that we can make use of all the automatically generated (robot-generated) grasping data without having a human supervise all that automated grasping, e.g. "This process is a successful way to pickup this object, and also this object is an apple." The ventral network ties back this the grasping data (without object labels) to object labels, which allows for semantic control of the trained robot e.g. "Pickup that apple".