ABSTRACT
As natural language processing advances in the field of robotics, enabling seamless human-robot interaction, it becomes imperative to identify the most effective approach for conditioning complex robotics tasks using natural language commands. This article reviews various state-of-the-art methods for natural language-conditioned planning, with a particular focus on mobile manipulation. The authors explore and review different architectures and techniques to comprehend, interpret, and execute natural language commands. Challenges are identified along the way, and conceptual architecture is proposed to tackle them in an efficient manner.
