AFCE完成100k训练后按200观测heldout composite loss选择72500（0.1550050788745284）；100000为0.15692223992198706。heldout用于选点，不是独立测试或真机成功率。π0.5 baseline为已有实机经验的7500权重，训练预算不同。 可在GitHub ARTIFACTS.json中取不可变revision，然后为hf download添加--revision ；或使用仓库的python scripts/downloadartifacts.py --baseline --afce-fullft，自动固定revision并保持评测脚本目录结构。 - 三路当前RGB（主视角/左右腕）、原生16D实测state（双臂各7关节＋两个夹爪，输入补到32D）与subtask prompt。 - Baseline预测32×16动作；AFCE先预测E空间30×256，再由联合训练decoder输出30×16动作；30Hz是客户端执行频率，与chunk长度是不同概念。 - AFCE推理不需要DINOv3和E编码器；decoder初始化/normalization/manifest与normstats已附带。训练后decoder已在checkpoint参数内。 - Orbax/JAX完整推理params，不是Transformers格式或仅LoRA adapter；不含optimizer。 - 仍使用subtask指令/外部阶段切换；这是原子能力对比权重，不是三整任务40k的新实验。…
Independent publisher
Kaiyuan Yang
yky666
AI、Robotics、Embodied AI
Models in Library1
Datasets in Library0
Models on Hugging Face1
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Published tasks: Robotics.
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