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LLMs have terrible spatial awareness. This probably comes from ONLY being trained on text. I wonder if it would help a LLM running a robot to have a separate c

LLMs have terrible spatial awareness. This probably comes from ONLY being trained on text. I wonder if it would help a LLM running a robot to have a separate controller calculating it's position and what is around it, and feed that into the LLM constantly. It would be like when a video game has a radar display or map to let you know where you are in relation to other things.

moffkalast 3y ago
I actually tried that a while back, giving 3.5-turbo a multishot prompt that consisted of distance readings for ahead, left, right and back in an array, as extracted from lidar data, then giving it movement instructions. It performed rather terribly. You've very much correct that their spatial awareness is terrible. Something as simple as drive forward, then back, turn left, etc. works just fine and they can generally translate it to a specified message format reasonably reliably, but give them something more complex to execute, like drive a robot in a square pattern (an example answer would be go forward, turn right, go forward, turn right, etc.) they start to generate nonsense. I also tested it with the 30B WizardLM at the time which performed almost as well in terms of message format but had even worse awareness. Part of the problem is that the training data contains next to no examples that would teach it how 3D space works. I considered making a dataset of driving a robot around with human movement commands and then logging the aggregated sensor data and commands for fine tuning so the prompt format would be pre-learned, but I'm not entirely sure how much it would help.
- adeelzaman 3y ago
  Hi @moffkalast, I am interested in doing something similar (building a larger dataset of robot movements) and fine-tuning an LLM with it. Would love to have a quick call and chat further. You can reach me at "hi [at] adeelzaman [dot] me".
StackOverlord 3y ago
LLMs have a firm grip on common sense. It's because it allows them to deal with the utterly unexpected they are deemed useful in robotics. Not to perform delicate movements, but stop doing so when police enters the room.

moffkalast 3y ago

I actually tried that a while back, giving 3.5-turbo a multishot prompt that consisted of distance readings for ahead, left, right and back in an array, as extracted from lidar data, then giving it movement instructions. It performed rather terribly. You've very much correct that their spatial awareness is terrible. Something as simple as drive forward, then back, turn left, etc. works just fine and they can generally translate it to a specified message format reasonably reliably, but give them something more complex to execute, like drive a robot in a square pattern (an example answer would be go forward, turn right, go forward, turn right, etc.) they start to generate nonsense. I also tested it with the 30B WizardLM at the time which performed almost as well in terms of message format but had even worse awareness. Part of the problem is that the training data contains next to no examples that would teach it how 3D space works. I considered making a dataset of driving a robot around with human movement commands and then logging the aggregated sensor data and commands for fine tuning so the prompt format would be pre-learned, but I'm not entirely sure how much it would help.

adeelzaman 3y ago
Hi @moffkalast, I am interested in doing something similar (building a larger dataset of robot movements) and fine-tuning an LLM with it. Would love to have a quick call and chat further. You can reach me at "hi [at] adeelzaman [dot] me".

StackOverlord 3y ago

LLMs have a firm grip on common sense. It's because it allows them to deal with the utterly unexpected they are deemed useful in robotics. Not to perform delicate movements, but stop doing so when police enters the room.