Model Predictive Networkを用いたマニピュレータによるフォールディングドアの開オペレーション

抄録

Due to labor shortages, the number of service robots being installed is steadily increasing. The main functions of service robots include mobility and grasping. However, doors installed between rooms and in storage spaces become obstacles, limiting the environments where service robots can be introduced. Therefore, to relax this constraint, it is necessary to equip service robots with the functionality to perform opening operations. The operation of opening doors with manipulators has traditionally focused on swing and sliding doors whose handle’ trajectories are relatively simple when the doors open. This study proposes a Model Predictive Reinforcement Learning (MPRL) for the operation of opening folding doors, which have not been conventionally addressed.