Multimodal Robotic Manipulation Learning

Abstract

The primary issue addressed in this paper pertains to semantic misunderstandings or errors in textual input, leading to inaccurate correspondences between text and images. Such discrepancies hinder the effective utilization of natural language for instructing robotic actions. To mitigate the problem, a framework that retrieval augmented generation semantic analysis with object recognition is proposed to establish accurate mappings between images and textual descriptions. Specifically, the capabilities of transformer models are leveraged to comprehend the semantics of natural language inputs. Subsequently, through object recognition techniques, the alignment of visual cues with corresponding textual representations is facilitated. In the development of a comprehensive framework that seamlessly integrates natural language understanding, object recognition, and sequence-to-sequence modeling for robotic arm planning. By addressing semantic ambiguities and ensuring precise correspondence between textual instructions and visual inputs, our approach enhances the efficacy of human-robot interaction and facilitates more intuitive and accurate robotic task execution.