Efficient Task/Motion Planning for a Dual-arm Robot from Language Instructions and Cooking Images

Kota Takata, Takuya Kiyokawa, Ixchel Georgina Ramirez-Alpizar, Natsuki Yamanobe, Weiwei Wan, Kensuke Harada

Abstract: When generating robot motions based on instructions such as cooking recipes, ambiguity of the instructions and lack of necessary information are problematic for the robot. To solve this problem, we propose an efficient motion planning approach for a dual-arm robot by constructing a graph repre-senting a motion sequence based on a recipe consisting of verbal instructions and cooking images. A functional unit is generated based on the linguistic instructions in the recipe. Since most recipes lack the necessary information for executing the motion, we first consider extracting the information about the cooking motion like cutting from the food images of the recipe and supplementing it. In addition, to supplement the actions that humans perform unconsciously, we generate functional units for actions not explicitly mentioned in the recipe based on the current situation of the cooking process, and then connect them to the functional units generated from the recipe. Moreover, during the connection we consider the motion of the robot's arms in parallel for an efficient execution of the recipe, similar to those of a human. Through experiments, we demonstrate that for a given recipe, the proposed method can be used to generate a cooking sequence with the supplementary information needed, and executed by a dual-arm robot. The results show that the proposed method is effective and can simplify robot teaching in cooking tasks.