CAVER: Curious Audiovisual Exploring Robot

TL;DR

CAVER is a robot that actively explores objects to learn rich audiovisual representations, enabling improved material classification and imitation of audio-only demonstrations through curiosity-driven interaction.

Contribution

The paper introduces a novel audiovisual exploration robot with a new end-effector, combined representation, and curiosity-based exploration algorithm, advancing multimodal robotic perception.

Findings

CAVER efficiently builds audiovisual representations with fewer interactions.

The learned representations improve material classification accuracy.

CAVER successfully imitates audio-only human demonstrations.

Abstract

Multimodal audiovisual perception can enable new avenues for robotic manipulation, from better material classification to the imitation of demonstrations for which only audio signals are available (e.g., playing a tune by ear). However, to unlock such multimodal potential, robots need to learn the correlations between an object's visual appearance and the sound it generates when they interact with it. Such an active sensorimotor experience requires new interaction capabilities, representations, and exploration methods to guide the robot in efficiently building increasingly rich audiovisual knowledge. In this work, we present CAVER, a novel robot that builds and utilizes rich audiovisual representations of objects. CAVER includes three novel contributions: 1) a novel 3D printed end-effector, attachable to parallel grippers, that excites objects' audio responses, 2) an audiovisual representation that combines local and global appearance information with sound features, and 3) an exploration algorithm that uses and builds the audiovisual representation in a curiosity-driven manner that prioritizes interacting with high uncertainty objects to obtain good coverage of surprising audio with fewer interactions. We demonstrate that CAVER builds rich representations in different scenarios more efficiently than several exploration baselines, and that the learned audiovisual representation leads to significant improvements in material classification and the imitation of audio-only human demonstrations. https://caver-bot.github.io/