With the physique of an ordinary person, running at a world champion’s speed!Leg length: 0.4+0.4=0.8m, body weight: approx. 62kg!H1: “Give me one more chance...
In à set a planned condition you don’t have the impact of random events. A perfectly fine program can broke easily because input data don’t match expected input. I join the other guy sating that not a huge feat, that a feat but on the same scale of solid state battery or the folding cloth robot. Pretty but not real life usable yet so not so usefull.
I get that you like the final idea. I am myself wetting myself about the LG robot which was folding t-shirts at CES2026 but I fold on my bed and I am convinced he can’t handle well a tilted surface which is not as hard as a table.
You absolutely do have the impact of random events when you’re doing anything in the physical world. You have wind, uneven ground, variations in weight distribution, and so on. That’s what makes this sort of stuff so difficult in practice. All the tiny little errors quickly add up, so you can’t just match expected input. You have to have a dynamic system that can adjust on the fly to the sensory data. Dealing with stuff like an uneven bed or a tilted surface is a completely separate problem of having a good enough world model internally.
I disagree, but I’m just a mechanical engineer.
And what specifically is it that you disagree with, but I’m just a software engineer.
In à set a planned condition you don’t have the impact of random events. A perfectly fine program can broke easily because input data don’t match expected input. I join the other guy sating that not a huge feat, that a feat but on the same scale of solid state battery or the folding cloth robot. Pretty but not real life usable yet so not so usefull. I get that you like the final idea. I am myself wetting myself about the LG robot which was folding t-shirts at CES2026 but I fold on my bed and I am convinced he can’t handle well a tilted surface which is not as hard as a table.
You absolutely do have the impact of random events when you’re doing anything in the physical world. You have wind, uneven ground, variations in weight distribution, and so on. That’s what makes this sort of stuff so difficult in practice. All the tiny little errors quickly add up, so you can’t just match expected input. You have to have a dynamic system that can adjust on the fly to the sensory data. Dealing with stuff like an uneven bed or a tilted surface is a completely separate problem of having a good enough world model internally.