MultiPLY: A Multisensory Object-Centric Embodied Large Language Model in 3D World

TL;DR

MultiPLY is a novel multisensory embodied large language model that actively interacts with 3D environments, integrating visual, audio, tactile, and thermal data to improve performance on various embodied tasks.

Contribution

The paper introduces MultiPLY, a multisensory embodied LLM with a large-scale interaction dataset and a new encoding method for 3D scenes, enabling active environment interaction and multisensory data integration.

Findings

MultiPLY significantly outperforms baselines on embodied tasks.

The dataset contains 500k multisensory interaction data points.

Active interaction with multisensory data enhances task performance.

Abstract

Human beings possess the capability to multiply a melange of multisensory cues while actively exploring and interacting with the 3D world. Current multi-modal large language models, however, passively absorb sensory data as inputs, lacking the capacity to actively interact with the objects in the 3D environment and dynamically collect their multisensory information. To usher in the study of this area, we propose MultiPLY, a multisensory embodied large language model that could incorporate multisensory interactive data, including visual, audio, tactile, and thermal information into large language models, thereby establishing the correlation among words, actions, and percepts. To this end, we first collect Multisensory Universe, a large-scale multisensory interaction dataset comprising 500k data by deploying an LLM-powered embodied agent to engage with the 3D environment. To perform instruction tuning with pre-trained LLM on such generated data, we first encode the 3D scene as abstracted object-centric representations and then introduce action tokens denoting that the embodied agent takes certain actions within the environment, as well as state tokens that represent the multisensory state observations of the agent at each time step. In the inference time, MultiPLY could generate action tokens, instructing the agent to take the action in the environment and obtain the next multisensory state observation. The observation is then appended back to the LLM via state tokens to generate subsequent text or action tokens. We demonstrate that MultiPLY outperforms baselines by a large margin through a diverse set of embodied tasks involving object retrieval, tool use, multisensory captioning, and task decomposition.