Practical Task and Motion Planning for Robotic Food Preparation

To fully integrate robots into household settings, they must be capable of autonomously planning and executing diverse tasks. However, task and motion planning for multi- step manipulation tasks remains an open challenge in robotics, especially for long-horizon tasks in dynamic environments. This study presents an integrated task and motion planning (TAMP) robotic framework for real-world cooking tasks using a dual- arm robotic system. Our framework combines PDDLStream, an existing TAMP framework, with the MoveIt Task Constructor, a multi-stage manipulation planner, to improve multi-step motion planning for long-horizon tasks. We enhance our framework with various cooking-related skills, including object fixturing, force-based tip detection, and slicing using Reinforcement Learning (RL). As a motivating case study, we address the long- horizon task of preparing a simple cucumber salad, involving slicing and serving it on a plate. We showcase our framework through both simulation and real robot demonstration.