Robotic interaction in fast-paced environments presents a substantial challenge, particularly in tasks requiring the prediction of dynamic, non-stationary objects for timely and accurate responses. An example of such a task is ping-pong, where the physical limitations of a robot may prevent it from reaching its goal in the time it takes the ball to cross the table. The scene of a ping-pong match contains rich visual information of a player's movement that can allow future game state prediction, with varying degrees of uncertainty. To this aim, we present a visual modeling, prediction, and control system to inform a ping-pong playing robot utilizing visual model uncertainty to allow earlier motion of the robot throughout the game. We present demonstrations and metrics in simulation to show the benefit of incorporating model uncertainty, the limitations of current standard model uncertainty estimators, and the need for more verifiable model uncertainty estimation. Our code is publicly available.

翻译：在快节奏环境下的机器人交互面临重大挑战，特别是在需要预测动态非平稳物体以实现及时准确响应的任务中。乒乓球运动即为典型案例，由于机器人物理性能限制，可能无法在乒乓球飞越球台的时限内完成击球动作。乒乓球比赛场景蕴含运动员动作的丰富视觉信息，这些信息可在不同不确定性程度下预测未来比赛状态。为此，我们提出一套视觉建模、预测与控制系统，通过利用视觉模型不确定性实现乒乓球机器人在比赛中的早期动作响应。通过仿真演示与量化指标，我们证明了引入模型不确定性的优势、当前标准模型不确定性估计器的局限性，以及建立更高可靠性模型不确定性估计方法的必要性。本系统代码已开源。