ASMR: Augmenting Life Scenario using Large Generative Models for Robotic Action Reflection

TL;DR

This paper presents a data augmentation framework using large generative models to improve multimodal classification for robotic assistance, significantly enhancing action prediction accuracy with limited data.

Contribution

The novel framework combines large language models and diffusion-based image generation to augment training data for robotic action reflection tasks.

Findings

Achieved state-of-the-art performance on real-world datasets.

Enhanced multimodal model accuracy with limited target data.

Demonstrated effective use of generative models for data augmentation.

Abstract

When designing robots to assist in everyday human activities, it is crucial to enhance user requests with visual cues from their surroundings for improved intent understanding. This process is defined as a multimodal classification task. However, gathering a large-scale dataset encompassing both visual and linguistic elements for model training is challenging and time-consuming. To address this issue, our paper introduces a novel framework focusing on data augmentation in robotic assistance scenarios, encompassing both dialogues and related environmental imagery. This approach involves leveraging a sophisticated large language model to simulate potential conversations and environmental contexts, followed by the use of a stable diffusion model to create images depicting these environments. The additionally generated data serves to refine the latest multimodal models, enabling them to more accurately determine appropriate actions in response to user interactions with the limited target data. Our experimental results, based on a dataset collected from real-world scenarios, demonstrate that our methodology significantly enhances the robot's action selection capabilities, achieving the state-of-the-art performance.