𝗔𝗳𝘁𝗲𝗿 𝟭𝟬+ 𝘆𝗲𝗮𝗿𝘀 𝗶𝗻 𝗿𝗼𝗯𝗼𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, from my PhD at Imperial to Berkeley to building the Dyson Robot Learning Lab, one frustration kept hitting me:
𝗪𝗵𝘆 𝗱𝗼 𝗜 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗿𝗲𝗯𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗼𝘃𝗲𝗿 𝗮𝗻𝗱
Rhoda AI checked whether scaling web video pretraining actually helps real robots, using a real customer task. Unpacking 10kg boxes of bearings and sorting the packaging.
Turns out yes. Bigger models do better, more pretraining compute does better, and the compute gap is widest
At Rhoda, we care deeply about the science of pre-training for robotics.
In one of the most rigorous studies of its kind, over thousands of trials and hundreds of hours of robot evaluations, we show how scaling web-video pre-training leads to better real-world robot performance.
A robot's camera predicts the future to decide what to do next. The problem is that prediction takes time, and time is the one thing a robot moving in the real world doesn't have to spare.
That's the tension a team from @BAAIBeijing and collaborators went after with World Action
Try holding your arm perfectly still for two minutes while someone else moves it into a sleeve. Now imagine doing that if holding still wasn't something your body could easily do in the first place.
That's the gap in a lot of robot-assisted dressing research, which usually
Most of the robots sitting on factory floors right now are more capable than the tasks they're being used for.
I've spent over a decade in robot learning, PhD at Imperial, postdoc at Berkeley, building the Dyson Robot Learning Lab, and the pattern hasn't changed. Every major