Motion generation in cluttered, dense, and dynamic environments is a central topic in robotics, rendered as a multi-objective decision-making problem. Current approaches trade-off between safety and performance. On the one hand, reactive policies guarantee fast response to environmental changes at the risk of suboptimal behavior. On the other hand, planning-based motion generation provides feasible trajectories, but the high computational cost may limit the control frequency and thus safety. To combine the benefits of reactive policies and planning, we propose a hierarchical motion generation method. Moreover, we adopt probabilistic inference methods to formalize the hierarchical model and stochastic optimization. We realize this approach as a weighted product of stochastic, reactive expert policies, where planning is used to adaptively compute the optimal weights over the task horizon. This stochastic optimization avoids local optima and proposes feasible reactive plans that find paths in cluttered and dense environments. Our extensive experimental study in planar navigation and 6DoF manipulation shows that our proposed hierarchical motion generation method outperforms both myopic reactive controllers and online re-planning methods.

翻译：在杂乱、密集且动态的环境中生成运动是机器人学的核心课题，被建模为多目标决策问题。当前方法在安全性与性能之间进行权衡：一方面，反应式策略能保证对环境变化的快速响应，但可能产生次优行为；另一方面，基于规划的运动生成可提供可行轨迹，但高计算代价可能限制控制频率从而影响安全性。为结合反应式策略与规划的优势，我们提出一种分层运动生成方法。此外，采用概率推理方法对分层模型及随机优化进行形式化建模。该方法通过随机反应式专家策略的加权乘积实现，其中规划被用于自适应计算任务时域上的最优权重。这种随机优化避免了局部最优，并能在杂乱密集环境中生成找到可行路径的反应式规划方案。我们在平面导航和六自由度操作任务上的大量实验表明，所提出的分层运动生成方法优于短视反应式控制器和在线重规划方法。