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"Conversely to your comment, I find that -if this paper really represents the state-of-the-art- then the state-of-the-art is extremely limited." The state of t

"Conversely to your comment, I find that -if this paper really represents the state-of-the-art- then the state-of-the-art is extremely limited." The state of the art in robotic manipulation is extremely limited (the state of the art in robotic locomotion is also extremely limited, but somewhat easier to paper over - simplified special cases get you farther in locomotion than in manipulation). IMHO, the layer of robotic control systems above servo control, but below spatial reasoning is an enormous hole in the field's research portfolio. We've got pretty decent machine vision at this point -- still not up to human standards, but improving rapidly. We've got excellent servo control laws. We've got good planning and scheduling techniques. What we don't have is techniques for robustly and rapidly performing dexterous motion planning, particularly where the contact forces with the environment are an important part of the problem. I was at Robotics-Science and Systems this year and heard a talk by Sergey Levine, one of the handful of researchers addressing this topic. He put up a picture of IBM Deep Blue playing Garry Kasparov. He pointed out that there was no robot in the picture -- there was a human moving Deep Blue's pieces around on the board. The point was that we are now in a strange place in robotics: the hardest part of playing a grandmaster--level chess match isn't figuring out the moves. It's reaching out and picking up the pieces.

YeGoblynQueenne 8y ago
Thank you for the useful insight. I'm not an expert in robotics, far from. Like you say, machine vision has progressed, but it's still very far from human vision. For one thing, most of the progress is in object classification, so any problem that can't be reformulated as a classification problem is basically out of the question. Then, there's the issue of poor generalisation between classes- for any class of object that we want a system to recognise, we must train the system anew, at great cost- except for pre-training of course (which basically means someone else already did the hard work).
- GlenTheMachine 8y ago
  ...and evenif we completely solved the machine vision problem, the motion planning under cintact problem would remain. At some point the world will wake up and realize that this problem is approximately as hard as the machine vision problem, and will devote resources to solving it. But that awaeness hasn't happened yet.

YeGoblynQueenne 8y ago

Thank you for the useful insight. I'm not an expert in robotics, far from. Like you say, machine vision has progressed, but it's still very far from human vision. For one thing, most of the progress is in object classification, so any problem that can't be reformulated as a classification problem is basically out of the question. Then, there's the issue of poor generalisation between classes- for any class of object that we want a system to recognise, we must train the system anew, at great cost- except for pre-training of course (which basically means someone else already did the hard work).

GlenTheMachine 8y ago
...and evenif we completely solved the machine vision problem, the motion planning under cintact problem would remain. At some point the world will wake up and realize that this problem is approximately as hard as the machine vision problem, and will devote resources to solving it. But that awaeness hasn't happened yet.