狐𝒏𝒊𝒄𝒌🦊

We validated the Axis data pipeline through model training in two ways: - ACT/DP small-model benchmark: trained from scratch and evaluated on individual Axis tasks, showing that Axis-rendered data supports reliable single-task policy learning. - Pi0.5 foundation-model training: pretrained on 82 Axis tasks and finetuned with LoRA in MuJoCo, showing strong generalization across target tasks. This is a key step in our closed-loop data loop: using model performance to verify data quality and guide further optimization of the upstream data pipeline. Details and demos below. ⬇️

卧槽！快跑！！！ 聚币JU关提现了！

我是知行合一的人！ 你们猜昨天晚上我分析后开的这个仓位目前盈利多少？ 猜对了有奖哟，当大家都在看跌的时候市场不见得会跌，至少不见得会立马跌，关键就在于明天美股开盘后的走势，当前BTC下跌可以认为趋势反弹结束，也可以认为是趋势反弹中的调整…… 因此，不要盲目看多或者空，涨跌很快，做好交易计划和仓位管理很重要，一旦开单一定要理智再理智，不理智就会被市场重锤一击！

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In our Task Package breakdown, we highlighted Dimension 2: Atomic Skills. But what exactly are they, and why are they critical? While recent trends in robotic learning rely heavily on end-to-end models—trying to map raw camera pixels straight into complex movements in one giant leap—this method can be extremely data-hungry and brittle in complex, real-world environments. The solution is architectural: deconstructing complex, long-horizon tasks into indivisible, foundational physical actions—like Grasp, Place, Push, or Pivot. These are atomic skills. They successfully decouple high-level cognitive reasoning ("what to do") from low-level motor control ("how to move"). As demonstrated by research from Google DeepMind, scaling robotic intelligence fundamentally relies on dynamically composing a robust library of these base atomic skills. It establishes a shared, scalable conceptual structure for autonomous agents. Training models on atomic skill sequences unlocks true generalization: - Capability Reuse: A robot that already knows how to "Push" and "Grasp" doesn't relearn basic physics for a new task; it simply learns a new sequence. - Spatial Generalization: Skills adapt to local geometry, working flawlessly no matter where an object sits in the workspace. - Error Recovery: If a grasp fails, the system doesn't freeze. It recognizes the failure and triggers a recovery skill. Raw, unsegmented teleoperation video suffers from a low signal-to-noise ratio. At Axis, our Dynamic Data Engine structures human intelligence into these exact atomic sequences, delivering the high-value building blocks foundation models need to achieve robust generalization.