时间无关预测：预测可预测的视频帧
Time-Agnostic Prediction: Predicting Predictable Video Frames
摘要：
预测可以说是智能系统最基本的功能之一。一般来说，预测未来或两个航点之间的事件是非常困难的。然而，大多数现象自然会通过相对可预测的瓶颈-虽然我们无法预测机器人手臂在静止和举起物体之间的精确轨迹，但我们可以肯定它一定会拿起物体。为了利用这一点，我们将视觉预测从严格的时间概念中分离出来。虽然常规方法以规则间隔的时间间隔预测帧，但我们的时间不可知预测器（TAP）不与特定时间绑定，使得它们可以替代地发现可预测的“瓶颈”帧，而不管它们何时发生。我们评估我们的方法，未来和中间帧预测在三个机器人操作任务。我们的预测不仅具有更高的视觉质量，而且还对应于时间扩展任务中的连贯语义子目标。
英文摘要：
Prediction is arguably one of the most basic functions of an intelligent system. In general, the problem of predicting events in the future or between two waypoints is exceedingly difficult. However, most phenomena naturally pass through relatively predictable bottlenecks---while we cannot predict the precise trajectory of a robot arm between being at rest and holding an object up, we can be certain that it must have picked the object up. To exploit this, we decouple visual prediction from a rigid notion of time. While conventional approaches predict frames at regularly spaced temporal intervals, our time-agnostic predictors (TAP) are not tied to specific times so that they may instead discover predictable "bottleneck" frames no matter when they occur. We evaluate our approach for future and intermediate frame prediction across three robotic manipulation tasks. Our predictions are not only of higher visual quality, but also correspond to coherent semantic subgoals in temporally extended tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
