The Talking Robot: Distortion-Robust Acoustic Models for Robot-Robot Communication

TL;DR

This paper introduces Artoo, an end-to-end neural communication system for robots that is robust to channel distortion, enabling efficient and accurate robot-robot communication without human speech constraints.

Contribution

The paper presents a lightweight, jointly trained neural system for robot communication that outperforms baselines in noisy environments and is suitable for resource-limited robots.

Findings

Achieves 8.3% CER at 0 dB SNR

Requires only 2.1M parameters and runs in under 13 ms

Outperforms traditional methods under channel noise

Abstract

We present Artoo, a learned acoustic communication system for robots that replaces hand-designed signal processing with end-to-end co-trained neural networks. Our system pairs a lightweight text-to-speech (TTS) transmitter (1.18M parameters) with a conformer-based automatic speech recognition (ASR) receiver (938K parameters), jointly optimized through a differentiable channel. Unlike human speech, robot-to-robot communication is paralinguistics-free: the system need not preserve timbre, prosody, or naturalness, only maximize decoding accuracy under channel distortion. Through a three-phase co-training curriculum, the TTS transmitter learns to produce distortion-robust acoustic encodings that surpass the baseline under noise, achieving 8.3% CER at 0 dB SNR. The entire system requires only 2.1M parameters (8.4 MB) and runs in under 13 ms end-to-end on a CPU, making it suitable for deployment on resource-constrained robotic platforms.