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Open Problems in Robotics

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Re: Open Problems in Robotics

#71

Post by redis_mlc »

burntoutfire wrote: post #12
fluffy87 wrote: post #7

Earlier quoted context omitted.

This is the scariest part of using machine learning as an engineer on any practical application as well. Without an error bound, ML can’t be in charge of anything that could put human lives at risk. This is also why I don’t understand all the hype about FSD / L5 autonomous driving. We don’t even know yet if such error bounds even exist, so we don’t even know if machine learning is even the right tool for FSD yet. All…

What about "practical error bounds", i.e. testing the system through millions/billions of miles driven?

> testing the system through millions/billions of miles driven?

Makes sense doesn't it?

Well, actually no. The Google cars drive the same route every day in Mountain View with no deviation, so those millions of miles are really the same 10 miles over and over. Even seen 3 in a row behind each other.

Fools the regulators, and apparently you, though.

Re: Open Problems in Robotics

#72

Post by danbmil99 »

TL; DR: Robotics is hard. Nature is impressive.

(Source: 2 years working on a humanoid bipedal robot. Now, I am constantly amazed that people can balance all that weight on two spindly little legs. Running is a miracle)

Re: Open Problems in Robotics

#73

Post by monkeydust »

mvn9 wrote: post #17
monkeydust wrote: post #5

Honestly, late yesterday evening after work I was looking at floor full of toys that my little kids were playing with in yet another lockdown day thinking I wish there was a robot that I could build or buy to tidy this up. Did some research found this research project, promising but from 2018 and looks like it didn't go anywhere. https://youtu.be/geub-Nuu-Vw So now I'm thinking what about the build option.

Build it. There is a global market of parents who will buy it. But on the other hand, why not accept the toys on the floor? You are fighting entropy for no reason. You sleep at night, you will work tomorrow during the day, and when you look again, the toys are in an equally dispersed state. Why not let them stay in that state for days until you need to hover?

Isn't life just one big fight against entropy?

Re: Open Problems in Robotics

#74

Post by zippy5 »

cpgxiii wrote: post #53
fxtentacle wrote: post #44

Earlier quoted context omitted.

I strongly disagree! I have seen fantastic results with stereo cameras, colored lights, and self-calibration. For most use cases, it's no problem if your robot will stop for a few seconds, rotate the camera axis around a bit, and then continue. But that appears to be good enough to calibrate the features for tracking things like a reflective and transparent glass jar. As for the precision, I agree that 1cm at 10m dis…

If you have a vision system that "just works" and produces high-quality pointclouds in an actual kitchen environment, with reflective appliances, silverware, shiny countertops, shiny ceramic dishware, and glasses, we would absolutely use it. We have in-house research work on both learned monocular depth (so-so for robotics tasks) and learned stereo disparity (much more promising), so progress here isn't impossible, b…

Hypothetically, it seems that your problem has to do surfaces being reflective in the visible light spectrum. Do you have any luck with ultrasonic, ultraviolet, or IR sensors?

Also I was also thinking about how light source estimation might be viable work around for reflective surfaces. http://www.thomaswhelan.ie/Whelan16ijrr.pdf

Re: Open Problems in Robotics

#75

Post by redis_mlc »

heyitsguay wrote: post #3

I know that there's a lot of successful work in specific controlled environments (company X's factory floor), and that a lot of environment understanding/SLAM is broadly unsolved in arbitrary uncontrolled environments, but what about specific uncontrolled environments? What if i want a beer serving robot to learn a consistent, high-accuracy model of just my house, the way it is, and I'm willing to put in some technic…

Willow Garage sold a robot that did that. Only $440,000 each.

It was a vanity project of the Google founders/early employees, but they contributed to ROS (the Robot Operating System.)

https://en.wikipedia.org/wiki/Willow_Garage

https://en.wikipedia.org/wiki/Robot_Operating_System

Re: Open Problems in Robotics

#76

Post by vsskanth »

iamgopal wrote: post #50
modeless wrote: post #13

The objections to neural nets as the solution to all the problems on his list are the same as the old objections to neural nets as the solution to computer vision, speech recognition, translation, playing Go, etc. The objections will fall in the face of overwhelming evidence that neural nets simply work better than other approaches to these types of problems. For a long time software was the reason robots didn't work…

Any research/potential example of such actuators ?

Probably electro active polymers

Re: Open Problems in Robotics

#77

Post by deepGem »

Animats wrote: post #66

- Motion planning: already discussed. - Multiaxis singularities: much less of a problem than it used to be. We don't need closed-form solutions any more; we have enough CPU power at the robot to deal with this. You need some additional constraint, like "minimize jerk" when you have too many degrees of freedom. - Simultaneous Location and Mapping. SLAM for short: Getting much better. Things which explore and return a…

"The real problem: solve any of these problems, make very little money" - Just curious why have you come to this conclusion ?

Object manipulation has potential products in dishwashing and vegetable chopping - sufficiently large markets, potential billion $ outcomes for a startup which takes the early mover lead. Two robotic hands that can work in co-ordination just as human hands do. Extremely difficult to solve, but money is there.

Re: Open Problems in Robotics

#78

Post by NalNezumi »

I love the ever prevalent pessimism/cautious optimism in the field of robotics (industry and academics alike). It's a fresh breath of air from the ever over-hyping ML/AI field.

On a related note, an open problem I see in practicality is also: How do you manage an robotics company effectively? iRobot seems to be succeeding in this well, and so does some industrial robotics arm companies but the latter is more about industrial automation than the more general "robotics" company out there.

Some companies that go very broad general solution seems to be struggling, the application based robotics seems also fail more often than succeed.

There have emerged a lot of management methods and theories around software development (Agile etc), but what's the efficient management method for robotics?

Having been working at a few robotics company as a junior, It have always been either: 1. Someone with extensive research/engineering experience in a subfield of robotics in management, that can't manage the other subfields 2. Some one with too general knowledge and can't balance between each robotics-subfields needs including production & reliability + cost.

and both seems to do pretty bad, while the second one slightly favourable.

Re: Open Problems in Robotics

#79

Post by gspr »

tuatoru wrote: post #25
burntoutfire wrote: post #12

Earlier quoted context omitted.

What about "practical error bounds", i.e. testing the system through millions/billions of miles driven?

Miles driven is a useless metric. Stick your vehicle on a treadmill and have it drive a billion miles. What does that tell you? Edit to add: a better metric would be something like "billions of decisions made where human life was at stake".

It's pretty clear the they meant "distance driven in ordinary conditions where humans normally drive cars".

Re: Open Problems in Robotics

#80

Post by ragebol »

helltone wrote: post #4

One problem I encountered while working on robotics is that many commonly used algorithms yield approximate solutions with no error bounds. They work 99.99‰ of the time. This is fine from a computer science or math point of view, but very scary from an engineering perspective, specifically when there are humans nearby. A big part of me struggles to accept the suitability of algorithms coming from gaming engines or ma…

Many localisation algorithms are probabilistic, eg. 'you are here within R of X'. A couple of times I've had a manager who could just not accept the probabilistic nature: "It's just right there!"

So I to explain my manager that we just cannot do better and know for sure that the robot is really in position X, especially with the limited sensing the project would afford. Sure, you can do the classic AGV thing and add magnetic markers everywhere. Or use more sensors to get higher accuracy, but none of those were popular options.

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