GEN-1.5 的成功秘诀在于利用人类数据中的自然重复动作。这些重复主要来自对称模式，如 IKEA 组装手册中的螺栓配对，以及人类的恢复动作。关键在于保留失败的尝试，而非过度清理数据。另一个关键因素是 UMI（直接人类数据收集），它比传统的遥操作（teleop）更能保留人类的物理直觉。有了足够数据，许多行为可以实现零样本学习，无需微调。
GEN-1.5 的成功秘诀在于利用人类数据中的自然重复动作。这些重复主要来自对称模式，如 IKEA 组装手册中的螺栓配对，以及人类的恢复动作。关键在于保留失败的尝试，而非过度清理数据。另一个关键因素是 UMI（直接人类数据收集），它比传统的遥操作（teleop）更能保留人类的物理直觉。有了足够数据，许多行为可以实现零样本学习，无需微调。
Seeing a hype wave around GEN-1.5, and rightfully so. Lots of respect to Pete & Andy for executing s...
Seeing a hype wave around GEN-1.5, and rightfully so. Lots of respect to Pete & Andy for executing so well. The secret is in the naturally repetitive motions in human-collected data. There're 2 main sources for such repetitions: (1) Symmetric patterns. Sorting, tidying, and assembling almost never finish in one motion. Open any assembly manual from IKEA, and you find most objects symmetrical. You drive one bolt, then its twin, then the next pair. Every {bolt A, bolt B} pair is a natural continuation in context, and the second instance is a free training signal that imitates the first ("prompt"). (2) Recovery. Humans drop things all the time, but we pick them up so fast, we don’t even notice. That reflex to fix is half of our physical competence. The key insight is to keep the failed first half instead of trimming it away. If the model consumes the full arc, fumble, catch, continue, then recovery shows up organically at test time. It's funny that in-context improvement results from *NOT* over-sanitizing your data. The other critical ingredient is UMI. I've been saying for a while that teleop will not last, and GEN-1.5 is driving the final nail in the coffin. UMI is essentially a human wearing the robot gripper to collect data directly (human → data). Teleop inserts a layer of separation: human → VR/skeletal device → robot → data, which bleeds out all the human "physical intuition". The subtle sleight of hand we perform constantly with objects, the micro-adjustments, the feel of a part snapping into place, is nearly impossible to capture when you can't feel the environment directly. Once you have enough data, many behaviors can actually be zero-shot. For example, you don't even need finetuning to pick up a novel object. The model "just knows" what to do given a similar scene in the training distribution. Whether in-context learning truly works or not also depends on how far away the test is from training. Currently, the demos are still a bit too simple to conclude. I'm cautiously optimistic. Still, it's a great day in robotics. 💬 11 🔄 10 ❤️ 144 👀 8240 📊 32 ⚡