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、稠密和动态环境中的动力生成是机器人的一个中心议题,作为一个多客观的决策问题。当前安全与性能之间的权衡方法。一方面,反应性政策保证了对环境变化的快速反应,而这种反应性政策有亚于最佳行为的风险。另一方面,基于规划的动作生成提供了可行的轨道,但高计算成本可能会限制控制频率,从而限制安全。为了将反应性政策和规划的好处结合起来,我们建议了等级性运动生成方法。此外,我们采用了概率性推论方法来正式确定等级模型和随机优化。我们把这一方法作为随机性、反应性专家政策的加权产品,用于在任务范围上适应性地计算最佳重量。这种随机性优化避免了本地的节能,并提出可行的反应性计划,在布局导航和6DoF操纵中找到路径。我们在规划性导航和6DoF操作方面的广泛实验研究显示,我们拟议的等级运动生成方法超越了我的被动反应控制器和在线再规划方法。