Chat with the Environment: Interactive Multimodal Perception Using Large Language Models

Programming robot behavior in a complex world faces challenges on multiple levels, from dextrous low-level skills to high-level planning and reasoning. Recent pre-trained Large Language Models (LLMs) have shown remarkable reasoning ability in few-shot robotic planning. However, it remains challenging to ground LLMs in multimodal sensory input and continuous action output, while enabling a robot to interact with its environment and acquire novel information as its policies unfold. We develop a robot interaction scenario with a partially observable state, which necessitates a robot to decide on a range of epistemic actions in order to sample sensory information among multiple modalities, before being able to execute the task correctly. An interactive perception framework is therefore proposed with an LLM as its backbone, whose ability is exploited to instruct epistemic actions and to reason over the resulting multimodal sensations (vision, sound, haptics, proprioception), as well as to plan an entire task execution based on the interactively acquired information. Our study demonstrates that LLMs can provide high-level planning and reasoning skills and control interactive robot behavior in a multimodal environment, while multimodal modules with the context of the environmental state help ground the LLMs and extend their processing ability. The project website can be found at https://matcha-model.github.io/.

Authors

• Xufeng Zhao 0002
• Mengdi Li 0006
• Cornelius Weber
• Muhammad Burhan Hafez
• Stefan Wermter

Keywords

• Training
• Computational modeling
• Memory management
• Process control
• Programming
• Robot sensing systems
• Planning
• Interactive
• Language Model
• Multimodal Perception
• Large Language Models
• Reasoning Ability
• Training Set
• Natural Language
• Sensor Data
• Modularity
• Tactile Sensor
• Sound Detection
• Multimodal Learning
• Epistemic Uncertainty
• Established Knowledge
• Robot Capabilities
• Weight Perception
• Metal Block
• Impressive Ability
• Perception Module
• Commonsense Knowledge
• Passive Manner