lang-segment-anything VS fury-benchmarks

User fishbotics already answers a lot of these questions downstream, but just confirming it here as an author of the project/paper:

> - How does it know what objects are? Does it use some sort of realtime object classifier neural net? What limitations are there here?

We use Lang-SAM (https://github.com/luca-medeiros/lang-segment-anything) to do most of this, with CLIP embeddings (https://openai.com/research/clip) doing most of the heavy lifting of connecting image and text. One of the nice properties of using CLIP-like models is that you don't have to specify the classes you may want to query later, you can just come up with them during runtime.

> - Does the robot know when it can't perform a request? I.e. if you ask it to move a large box or very heavy kettlebell?

Nope! As it is right now, the models are very simple and they don't try to do anything fancy. However, that's why we open up our code! So the community can build smarter robots on top of this project that can use even more visual cues about the environment.

> - How well does it do if the object is hidden or obscured? Does it go looking for it? What if it must move another object to get access to the requested one?

It fails when the object is hidden or obscured in the initial scan, but once again we think it could be a great starting point for further research :) One of the nice things, however, is that we take full 3D information in consideration, and so even if some object is visible from only some of the angles, the robot has a chance to find it.

1) Fury is 41.6x faster than jackson for Struct serialization 2) Fury is 65.6x faster than jackson for Struct deserialization 3) Fury is 9.4x faster than jackson for MediaContent serialization 4) Fury is 9.6x faster than jackson for MediaContent deserialization

see https://github.com/chaokunyang/fury-benchmarks for detailed benchmark code.