论文标题
压力：随机动力学的声音从SIMS到真实
STReSSD: Sim-To-Real from Sound for Stochastic Dynamics
论文作者
论文摘要
声音是一种捕获动态物理事件的信息丰富的媒介。这项工作提出了压力，这是一个框架，该框架使用声音来弥合随机动力学的模拟到现实差距，这是针对弹跳球的规范案例所证明的。提出了一个物理动机的噪声模型，以捕获与环境碰撞时球的随机行为。从音频观察中，使用无似然贝叶斯推理框架来推断噪声模型的参数以及一种称为恢复系数的材料属性。然后使用相同的推理框架和校准的随机模拟器来学习球动力学的概率模型。在两个机器人实验中测试了动力学模型的预测能力。首先，开环的预测可以预测将球弹入杯子的概率成功。第二个实验将音频感知与机器人臂相结合，以实时跟踪和偏转弹跳球。我们设想这项工作是迈向整合基于音频的动态机器人任务推断的一步。可以在https://youtu.be/b7porgzrark上查看实验结果。
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.
