异构人群中最优视角社交机器人导航

在密集动态人群中导航社交机器人具有挑战性，这源于环境不确定性和复杂的人机交互。虽然模型预测控制（MPC）在实际时性能力方面表现优异，但其依赖固定预测视角限制了对变化环境和社交动态的适应性。此外，大多数MPC方法将行人视为均质障碍物，忽略了社交异质性以及合作或对抗性交互，这常导致部分可观测真实环境中的冻结机器人问题。在本文中，我们将规划视角识别为受社交背景条件制约的决策变量，而非固定的设计选择。基于这一洞见，我们提出了一种最优视角社交导航框架，通过推断推断的社交背景优化MPC的前瞻视角。一种时空Transformer从局部轨迹观察中推断行人合作属性，这些属性作为强化学习策略选择预测视角的社交先验。 resulting horizon-aware MPC incorporates socially conditioned safety constraints to balance navigation efficiency and interaction safety. Extensive simulations and real-world robot experiments demonstrate that optimal foresight selection is critical for robust social navigation in partially observable crowds. Compared to state-of-the-art baselines, the proposed approach achieves a 6.8% improvement in success rate, reduces collisions by 50%, and shortens navigation time by 19%, with a low timeout rate of 0.8%, validating the necessity of socially optimal planning horizons for efficient and safe robot navigation in crowded environments. Code and videos are available at Under Review.