ASMR: Augmenting Life Scenario using Large Generative Models for Robotic Action Reflection
This addresses the problem of data scarcity in multimodal robotic assistance for everyday human activities, though it is incremental as it builds on existing generative models.
The paper tackles the challenge of limited multimodal data for robotic assistance by introducing a framework that uses large language and diffusion models to generate synthetic dialogues and images, which improves robot action selection to state-of-the-art levels.
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.