GEN-1 仅用 1 小时机器人数据即可玩猜杯游戏，并能泛化到未见物体（如车钥匙）。该任务要求视觉记忆与高速率状态追踪，GEN-1 还展现出“主动感知”能力，手部动作会跟随杯子以辅助注意力。
GEN-1 plays the 🐚 shell game, trained on just 1 hr of robot data. It also generalizes to unseen objects, like @BerkayAntmen 's car keys.
Physical AI models should be capable of benchmark tasks like this one. It's interesting for the all the reasons @RhodaAI calls out -- requires visual memory, and the model must track the cups from the very start, at high frame rates.
Interestingly, GEN-1 appears to exhibit a degree of "active perception." It's subtle; the hands can sometimes appear to "follow" the cups, using its own movements to help attend to where it thinks the object should be.
Read more about GEN-1 in our blog post in the comments below ↓
Here’s something we’ve never seen done before. Real-world tasks are long and ambiguous. Solving them requires visual memory and state tracking. Most robot policies only see the last few frames. Ours doesn't. We put our DVA, FutureVision, to the perfect testbed: the shell game 🐚. The DVA nails it.在 X 查看被引用的帖子
来源：Generalist · x.com