Integrating Disambiguation and User Preferences into Large Language Models for Robot Motion Planning

This paper presents a framework that can interpret humans' navigation commands containing temporal elements and directly translate their natural language instructions into robot motion planning. Central to our framework is utilizing Large Language Models (LLMs). To enhance the reliability of LLMs in the framework and improve user experience, we propose methods to resolve the ambiguity in natural language instructions and capture user preferences. The process begins with an ambiguity classifier, identifying potential uncertainties in the instructions. Ambiguous statements trigger a GPT-4-based mechanism that generates clarifying questions, incorporating user responses for disambiguation. Also, the framework assesses and records user preferences for non-ambiguous instructions, enhancing future interactions. The last part of this process is the translation of disambiguated instructions into a robot motion plan using Linear Temporal Logic. This paper details the development of this framework and the evaluation of its performance in various test scenarios.

Distributed Multi-Robot Multi-Target Tracking Using Heterogeneous Limited-Range Sensors

This paper presents a cooperative multi-robot multi-target tracking framework aimed at enhancing the efficiency of the heterogeneous sensor network and, consequently, improving overall target tracking accuracy. The concept of normalized unused sensing capacity is introduced to quantify the information a sensor is currently gathering relative to its theoretical maximum. This measurement can be computed using entirely local information and is applicable to various sensor models, distinguishing it from previous literature on the subject. It is then utilized to develop a distributed coverage control strategy for a heterogeneous sensor network, adaptively balancing the workload based on each sensor's current unused capacity. The algorithm is validated through a series of ROS and MATLAB simulations, demonstrating superior results compared to standard approaches that do not account for heterogeneity or current usage rates.