Non-Prehensile Tool-Object Manipulation by Integrating LLM-Based Planning and Manoeuvrability-Driven Controls

Being able to use tools is a widely recognised indicator of intelligence across species. Humans, for instance, have demonstrated mastery of tool use for over two million years. The ability to use tools is invaluable as it extends an organism's reach and enhances its capacity to interact with objects and the environment. Being able to understand the geometric-mechanical relations between the tools-objects-environments allows certain species (e.g., apes and crows) to reach food in narrow constrained spaces. The same principles of physical augmentation and its associated non-prehensile manipulation capabilities also apply to robotic systems. For example, by instrumenting them with different types of end-effectors, robots can (in principle) dexterously interact (e.g., push and flip) with objects of various shapes and masses akin to its biological counterpart. However, developing this type of manipulation skill is still an open research problem. Furthermore, the complexity of planning tool-object manipulation tasks, particularly in coordinating the actions of dual-arm robots, presents significant challenges. To address these complexities, we propose integrating Large Language Models (LLMs) to assist in planning and executing these intricate manipulations, thereby enhancing the robot's ability to perform in diverse scenarios.