Earlier quoted context omitted.
It seems robotics has advanced more in the last 3 years than the previous 20.
the torrent of funding helps here
ML helps here and the progress Nvidia made with their robotics platform.
π0.5: A VLA with open-world generalization
41–47 of 47 posts
Re: π0.5: A VLA with open-world generalization
#42Earlier quoted context omitted.
That's not the reason, Pi0 was basically predicting at 10hz and predicting a temporal chunk up to 50 points so it could go up to 500Hz. It's slow because the original telop is slow, and the learned controllers through imitation learning is always a bit slower. Source : i work on this (not at PI)
Another practical reason is that it's dangerous. Pi0 uses ARX robot arm which weights 3-4kg per arm. It can easily break things or harm people if you allow it to move fast.
Not really if you clamp the torque aggressively.
But yeah in general the physical world is more dangerous than we tend to think
Re: π0.5: A VLA with open-world generalization
#43Re: π0.5: A VLA with open-world generalization
#44Re: π0.5: A VLA with open-world generalization
#45Earlier quoted context omitted.
Great, would you happen to know what's used in this video?
Here are some of the suppliers for things seen in the videos: https://arx-x.com/ https://x.com/GalaxeaDynamics https://www.youtube.com/@HEXMOVEHexmove_Robotic https://www.trossenrobotics.com/
So you are saying I can buy some robot, GPU and have this robot fold my laundry?
How much? :D
Re: π0.5: A VLA with open-world generalization
#46Amazing! On a fun note, I believe if a human kid were cleaning up the spill and threw the sponge into the sink like that, the kid would be in trouble. XD
cleaning a spill consists mostly of spreading it over the whole counter
Re: π0.5: A VLA with open-world generalization
#47Does the general laws of demos apply here? Than any automation shown is the extent of capabilities not the start?
One thing I notice is that they specify that the robot has never seen the homes before, but certain objects, like the laundry baskets, are identical. Doing your demo is significantly easier if you've already programmed/trained the robot to recognize the specific objects it has to interact with, even if those items are in different locations.
isn't object recognition essentially solved? AI models were beating humans at image classification (in terms of error rate) back in 2016. even if this particular model isn't the best at it, they can always call out to an API or have a secondary on-device VLM that has stronger object recognition capabilities