this post was submitted on 23 Nov 2023
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Machine Learning

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I've spent the past few months diving into recent publications from conferences like CoRL, ICRA, IROS, RAUL, CVPR, and some preprints of note. It seems like we're getting pretty close to robotic assistants that can perform a limited range of tasks and be deployed in people's homes. Where do we think we are with this tech? What do you think is the biggest bottleneck we need to overcome? Do you think the "internet scale" behavioural cloning techniques from Google will lead the way? Or something more RL-VL oriented?

I'm at the start of an AI PhD with a focus on robotics so I'd love to hear people's thoughts on this!

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[–] ItsJustMeJerk@alien.top 1 points 11 months ago (1 children)

I'm not exactly in expert, but what I'm seeing is that

  1. Software moves faster than mechanics. Robots (especially humanoid ones) are limited to stiff movements and expensive to manufacture.
  2. While big advances in perception and control have been made in the past couple years with VLMs, most still aren't reliable enough to be deployed. It's now possible to ask a robot to fetch you a beer successfully 70% of the time, but 70% isn't enough.
  3. It takes time for firms to bring a product to market. Robots being deployed now use techniques that are no longer SOTA.
[–] harrisonfield@alien.top 1 points 11 months ago