让机器人在人群中导航时，能动态预测人的不确定性并安全规划路径。
Interaction-aware Conformal Prediction for Crowd Navigation
- 交替优化机器人路径与人类轨迹不确定性预测，考虑双方相互影响。
- 在不同人群密度下保持高效导航、社交合理与误差量化平衡。
- 适合需要实时安全决策的机器人应用，如服务机器人或自动驾驶。
在人群导航中，机器人运动规划需考虑人类行为的不确定性，而该不确定性又依赖于机器人的运动规划。本文提出交互感知的置信预测（ICP），通过交替进行不确定性感知的机器人路径规划与依赖决策的人类行为不确定性量化。ICP 包含轨迹预测器、模型预测控制器（添加置信区间半径以保障概率安全）、人类模拟器（基于规划路径生成校准数据集）以及置信预测模块（在决策依赖的数据集上量化轨迹预测误差）。仿真结果表明，ICP 在导航效率、社会合理性与不确定性量化之间取得良好平衡，且在不同人群密度下具有良好泛化能力。其快速运行时间和高效内存使用使其适用于真实场景。代码已公开于 https://github.com/tedhuang96/icp。
原文摘要 · Abstract (English)
During crowd navigation, robot motion plan needs to consider human motion uncertainty, and the human motion uncertainty is dependent on the robot motion plan. We introduce Interaction-aware Conformal Prediction (ICP) to alternate uncertainty-aware robot motion planning and decision-dependent human motion uncertainty quantification. ICP is composed of a trajectory predictor to predict human trajectories, a model predictive controller to plan robot motion with confidence interval radii added for probabilistic safety, a human simulator to collect human trajectory calibration dataset conditioned on the planned robot motion, and a conformal prediction module to quantify trajectory prediction error on the decision-dependent calibration dataset. Crowd navigation simulation experiments show that ICP strikes a good balance of performance among navigation efficiency, social awareness, and uncertainty quantification compared to previous works. ICP generalizes well to navigation tasks under various crowd densities. The fast runtime and efficient memory usage make ICP practical for real-world applications. Code is available at https://github.com/tedhuang96/icp.
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