STReSSD：随机动力学的声拟实
STReSSD: Sim-To-Real from Sound for Stochastic Dynamics
摘要：
声音是一种信息丰富的媒介，可以捕捉动态的物理事件。这项工作提出了STReSSD，一个框架，使用声音来弥合随机动力学的模拟到现实的差距，展示了弹跳球的典型情况。一个物理激励的噪声模型，以捕捉随机行为的球碰撞时与环境。无似然贝叶斯推理框架用于从音频观察推断噪声模型的参数以及称为恢复系数的材料属性。相同的推理框架和校准的随机模拟器，然后用于学习球动力学的概率模型。在两个机器人实验的动力学模型的预测能力进行了测试。首先，开环预测预测将球反弹到杯子中的概率成功。第二个实验将音频感知与机器人手臂集成，以实时跟踪和偏转弹跳球。我们设想，这项工作是一个步骤，为动态机器人任务集成基于音频的推理。实验结果可以在https://youtu.be/b7pOrgZrArk上查看。
英文摘要：
Sound is an information-rich medium that captures dynamic physical events. This work presents STReSSD, a framework that uses sound to bridge the simulation-to-reality gap for stochastic dynamics, demonstrated for the canonical case of a bouncing ball. A physically-motivated noise model is presented to capture stochastic behavior of the balls upon collision with the environment. A likelihood-free Bayesian inference framework is used to infer the parameters of the noise model, as well as a material property called the coefficient of restitution, from audio observations. The same inference framework and the calibrated stochastic simulator are then used to learn a probabilistic model of ball dynamics. The predictive capabilities of the dynamics model are tested in two robotic experiments. First, open-loop predictions anticipate probabilistic success of bouncing a ball into a cup. The second experiment integrates audio perception with a robotic arm to track and deflect a bouncing ball in real-time. We envision that this work is a step towards integrating audio-based inference for dynamic robotic tasks. Experimental results can be viewed at https://youtu.be/b7pOrgZrArk.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
