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Comment by gabrielgoh | Hacker News Reader

I've wanted to break into robotics from machine learning for a long time, but I haven't found a good entry point for the problem. It seems like a rather large vertical cliff I have no way of scaling.

One of the handholds I'd need is a physics engine which which models a robotic arm down to the finest levels of motor control and feedback. I am not a mechanical engineer, and I do not know where to begin with a problem like that. I don't imagine this is hard to build using existing physics engines, but it wouldn't work out the box. It requires some deep domain knowledge of things like friction, tensile strength, and so on. A system like this would spur progress in robotics immensely.

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Have you looked into ROS and Gazebo?

I am an undergraduate leading an open source autonomous vehicle project [0] and we're currently almost done setting up simulation testing. Gazebo and ROS are very poorly documented but they serve their purpose really well when it comes to creating pub sub and physics simulation for robotics systems.

[0] https://github.com/gtagency/buzzmobile

It doesn't have to be an arm; for example Kiva, the robotics company Amazon bought for $775 million in 2012, just found a better way to move parts around in a warehouse; pretty basic idea and system too.

If you or for that matter anyone want a cofounder to apply to YC for AI, happy to help.

Would be helpful to have your email here or in your profile

Thanks, I only do outbound contact; if anyone wants me to contacts them, simply let me know and I'll be in touch with 24-hours.

contact me, I am interested

My email is in my profile, pls reach out

Why do you think you need a physics engine (outside of simulation/testing)? Don't humans get by pretty well with only an intuitive one?

it is for simulation, as you say - but this is a critical for employing reinforcement learning. The internal (inverse) model will of course, be based on a simplified system, like a DNN

Maybe or maybe you could just model what the workers already do, predict what they should do, and dynamically provide feedback, simulation, etc.

im not quite sure what you're suggesting. there needs to be a source of ground truth to inform the robot if what is being done is right. And actually having a physical robot arm for this would be way too slow

Right, the ground truth is the existing human labor - and yes, run sims at scale makes sense, but if you don't build and model using the real world you'll rapidly get a solution that only shows the sims errors in modeling the world. All the best robots I've seen run sims, but very narrowly and with a well defined problem to refine via a well designed model.

Offer stands to help you, but given the deadline for the next batch is days away, you would need to let me know as soon as possible via a comment, then I'd contact you via email.

shoot me an email with your background etc. I'm not aiming for this batch actively but i wouldn't mind a chat. Google my name to get my website an email. it should be the first result

Human motor planning involves, at least, forward and inverse models for performing stochastic optimal control (at least, that's the standard formulation of the problem). An intuitive physics engine may be built into our heads, but whether or not it is, just running it forward doesn't seem to suffice for motor control.

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