Planning with Learned Subgoals Selected by Temporal Information

Abstract-- Path planning in a changing environment is a challenging task in robotics, as moving objects impose timedependent constraints. Recent planning methods primarily focus on the spatial aspects, lacking the capability to directly incorporate time constraints. In this paper, we propose a method that leverages a generative model to decompose a complex planning problem into small manageable ones by incrementally generating subgoals given the current planning context. Then, we take into account the temporal information and use learned time estimators based on different statistic distributions to examine and select the generated subgoal candidates. Experiments show that planning from the current robot state to the selected subgoal can satisfy the given timedependent constraints while being goal-oriented. Modern robotic applications aim to place the robots into a collaborative environment rather than in a confined workstation, Figure 1: Planning with learned subgoals: with the estimated e.g.