Recent advances in large language models (LLMs) have demonstrated exceptional reasoning capabilities in natural language processing, sparking interest in applying LLMs to task planning problems in robotics. Most studies focused on task planning for clear natural language commands that specify target objects and their locations. However, for more user-friendly task execution, it is crucial for robots to autonomously plan and carry out tasks based on abstract natural language commands that may not explicitly mention target objects or locations, such as ‘Put the food ingredients in the same place.’ In this study, we propose an LLM-based autonomous task planning framework that generates task plans for abstract natural language commands. This framework consists of two phases: an environment recognition phase and a task planning phase. In the environment recognition phase, a large vision-language model generates a hierarchical scene graph that captures the relationships between objects and spaces in the environment surrounding a robot agent. During the task planning phase, an LLM uses the scene graph and the abstract user command to formulate a plan for the given task. We validate the effectiveness of the proposed framework in the AI2THOR simulation environment, demonstrating its superior performance in task execution when handling abstract commands.

This paper presents a novel method for state estimation of rigid body attitude system evolving on the manifold S3, which is crucial in robotics and drone applications. We introduce a particle filter with stable embedding that extends the system into Euclidean space while ensuring stability of the manifold. Our particle filter with stable embedding enables accurate state estimation by maintaining estimated state values in close proximity to the manifold, while requiring significantly fewer computational resources than the standard exponential-map-based method that keeps state estimates on the manifold. Furthermore, our method facilitates the application of usual techniques designed for particle filters in Euclidean spaces, to the manifold system, as is, without any modification. The accuracy and the efficiency of our particle filter are confirmed both by simulation and by real drone experiments.