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.
Abstract (translated)
我们介绍了Artoo，这是一个学习型声学通信系统，专为机器人设计。该系统用端到端联合训练的神经网络替代了手动设计的信号处理过程。我们的系统将轻量级的文字转语音（TTS）发射器（1.18M参数）与基于卷积变压器（Conformer）的自动语音识别（ASR）接收器（938K参数）配对，通过可微分信道进行联合优化。与人类语言不同，机器人之间的通信无需考虑副语言信息：系统不需要保留音色、语调或自然度，只需在信道失真的情况下最大化解码准确性。 通过一个三阶段的协同训练课程，TTS发射器学会了生成抗干扰能力强的声音编码，在噪声环境下超过了基线表现，实现了0 dB信噪比下的CER（错误率）8.3%。整个系统仅需2.1M参数（8.4MB），并且在CPU上运行时端到端延迟不到13毫秒，非常适合部署于计算资源受限的机器人平台。
URL
https://arxiv.org/abs/2603.07072
