X Square Robot WALL-B模型分拣万包裹实录
10,000 parcels. 5 hours, 14 minutes. One embodied AI model running the whole cha…
具身智能落地的重要里程碑，实测数据详实且挑战性强，值得机器人领域从业者关注其长周期稳定性表现。
10,000 parcels. 5 hours, 14 minutes. One embodied AI model running the whole challenge.
10,000个包裹。5小时14分钟。一个具身AI模型完成了整个挑战。
X Square Robot's WALL-B model sustained 1,911 parcels/hour, or about 1.88 seconds/parcel, while sorting 10,000 packages.
X Square Robot的WALL-B模型在处理10,000个包裹时，保持了每小时1,911个包裹的速度，即每个包裹约1.88秒。
The task is deceptively physical. The arm has to identify package orientation, flip each parcel label-side up, then slide it onto the conveyor. Odd objects such as soft toys are routed separately.
这项任务看似简单，实则充满物理挑战。机械臂必须识别包裹的方向，将每个包裹翻转至标签朝上，然后将其滑入传送带。像毛绒玩具这样的不规则物品会被单独分流。
For context, Figure has reported a 2.88-second parcel cycle time.
作为参考，Figure此前曾报告过2.88秒的包裹处理周期时间。
X Square Robot's WALL-B model decides how each parcel should be handled from the scene, while its six-axis arms execute the picks and corrections.
X Square Robot的WALL-B模型根据场景决定每个包裹的处理方式，而其六轴机械臂则执行抓取和校正动作。
Its previous public run averaged 1,816 parcels an hour with over 98% accuracy, so the new result claims both longer duration and higher throughput.
其之前的公开运行平均每小时处理1,816个包裹，准确率超过98%，因此新结果声称实现了更长的持续时间和更高的吞吐量。
The apparent simplicity of picking a parcel hides a repeated closed-loop computation spanning 3D perception, grasp reasoning, motion planning, force control, and online correction.
看似简单的抓取包裹动作背后，隐藏着涵盖3D感知、抓取推理、运动规划、力控和在线校正的重复闭环计算。
The difficulty comes from combining fast visual perception, 3D geometry, grasp selection, collision-aware motion planning, feedback control, and failure recovery under a scene that changes after every action.
难点在于结合快速视觉感知、3D几何、抓取选择、碰撞感知的运动规划、反馈控制以及失败恢复，且每次动作后场景都会发生变化。
Sustaining the full loop across 10,000 parcels tests whether the robotics stack remains stable when thousands of small physical uncertainties accumulate.
在10,000个包裹中维持完整的闭环流程，旨在测试当数千个微小的物理不确定性累积时，机器人技术栈是否仍能保持稳定。
Another point of difficulty comes from the endurance, because this is as difficult as the speed.
另一个难点来自耐力，因为这与速度一样具有挑战性。
Every additional hour gives perception errors, tiny calibration errors, grasp failures, awkward package geometries, and small control mistakes more opportunities to compound. A system that looks great for 50 parcels can behave very differently after several thousand.
每增加一小时，感知误差、微小的校准误差、抓取失败、不寻常的包裹几何形状以及小的控制失误就有更多机会叠加。一个在处理50个包裹时表现出色的系统，在处理几千个包裹后可能会表现出截然不同的行为。
The footage also shows the policy doing more than repetitive pick-and-place. Packages are flipped label-side up, moved onto the conveyor, and unusual items such as soft toys are sent to another lane.
视频还显示，该策略不仅执行重复的抓取和放置动作。包裹被翻转至标签朝上，移至传送带上，而像毛绒玩具这样的特殊物品则被送往另一条通道。
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