Title: SoundNeRirF: Receiver-to-Receiver Sound Neural Room Impulse Response Field. Abstract: We present SoundNeRirF, a framework that learns a continuous receiver-to-receiver neural room impulse response field~(r2r-RIR) to help robot efficiently predict the sound to be heard at novel locations. It represents a room acoustic scene as a continuous 6D function, whose input is a reference receiver's 3D position and a target receiver's 3D position, and whose outputs are an inverse room impulse response~(inverse-RIR) and a forward room impulse response~(forward-RIR) that jointly project the sound from the reference position to the target position. SoundNeRirF requires knowledge of neither sound source (e.g. location and number of sound sources) nor room acoustic properties~(e.g. room size, geometry, materials). Instead, it merely depends on a sparse set of sound receivers' positions, as well as the recorded sound at each position. We instantiate the continuous 6D function as multi-layer perceptrons~(MLP), so it is fully differentiable and continuous at any spatial position. SoundNeRirF is encouraged, during the training stage, to implicitly encode the interaction between sound sources, receivers and room acoustic properties by minimizing the discrepancy between the predicted sound and the truly heard sound at the target position. During inference, the sound at a novel position is predicted by giving a reference position and the corresponding reference sound. Extensive experiments on both synthetic and real-world datasets show SoundNeRirF is capable of predicting high-fidelity and audio-realistic sound that fully captures room reverberation characteristics, significantly outperforming existing methods in terms of accuracy and efficiency.