Humanoid robots often fail when conditions change.
A heavy backpack. A soft floor. A steep slope. Suddenly, the same controller may not work.
Meet FADA.
🦾
It adapts a humanoid to new conditions using only its own experience, keeping what the robot wants to do and changing only how it does it.
About two minutes. No rewards. No demonstrations. No simulator retuning.
🧵
🤖Humanoid robots, to be truly useful, must handle dynamics they never saw during training: payloads, slopes, soft terrain, and more..
But these are exactly the settings where the familiar sim-to-real pain also shows up, and every humanoid researcher has probably at least once said:
“but it worked perfectly in simulation!!”
For precise whole-body control, the challenge is not just avoiding failure. Small execution errors can quickly cascade into large deviations.
In FADA, we study how humanoids can adapt to these unseen dynamics. Our key observation is simple: the robot’s intent often transfers, but the execution does not.
1/N🧵
Couldn't have done this without this incredible team. Huge thanks to @NikhilSoban353, Ishayu Shikhare, Alan Wang, my advisors @max_simchowitz and @GuanyaShi, and special thanks to @yxyang1995, whose insight shaped a key part of this work. 🙏
Humanoid robots often fail when conditions change.
A heavy backpack. A soft floor. A steep slope. Suddenly, the same controller may not work.
Meet FADA.
🦾
It adapts a humanoid to new conditions using only its own experience, keeping what the robot wants to do and changing only how it does it.
About two minutes. No rewards. No demonstrations. No simulator retuning.
🧵