论文标题
影响使声音和声音产生影响：声音指南表示和探索
Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and Explorations
论文作者
论文摘要
声音是现实世界中最有用，最丰富的方式之一，同时可以在不廉价传感器的情况下感知到可以放置在移动设备上的无人接触。尽管深度学习能够从多个感官输入中提取信息，但几乎没有声音来控制和学习机器人动作。对于无监督的强化学习，预计代理人将积极地收集经验，并以一种自制的方式共同学习表征和政策。我们使用基于物理的声音模拟来构建逼真的机器人操作场景，并提出内在的好奇模块（ISCM）。 ISCM向加强学习者提供反馈，以学习强大的表示并奖励更有效的探索行为。我们在适应过程中对启用声音进行了实验，并在适应过程中进行了残疾，并表明，ISCM所学的表示形式优于仅视觉基线的基准和预训练的策略，当应用于下游任务时，可以加速学习过程。
Sound is one of the most informative and abundant modalities in the real world while being robust to sense without contacts by small and cheap sensors that can be placed on mobile devices. Although deep learning is capable of extracting information from multiple sensory inputs, there has been little use of sound for the control and learning of robotic actions. For unsupervised reinforcement learning, an agent is expected to actively collect experiences and jointly learn representations and policies in a self-supervised way. We build realistic robotic manipulation scenarios with physics-based sound simulation and propose the Intrinsic Sound Curiosity Module (ISCM). The ISCM provides feedback to a reinforcement learner to learn robust representations and to reward a more efficient exploration behavior. We perform experiments with sound enabled during pre-training and disabled during adaptation, and show that representations learned by ISCM outperform the ones by vision-only baselines and pre-trained policies can accelerate the learning process when applied to downstream tasks.
