A realistic simulation environment is an essential tool in every roboticist's toolkit, with uses ranging from planning and control to training policies with reinforcement learning. Despite the centrality of simulation in modern robotics, little work has been done to compare the performance of robotics simulators against real-world data, especially for scenarios involving dynamic motions with high speed impact events. Handling dynamic contact is the computational bottleneck for most simulations, and thus the modeling and algorithmic choices surrounding impacts and friction form the largest distinctions between popular tools. Here, we evaluate the ability of several simulators to reproduce real-world trajectories involving impacts. Using experimental data, we identify system-specific contact parameters of popular simulators Drake, MuJoCo, and Bullet, analyzing the effects of modeling choices around these parameters. For the simple example of a cube tossed onto a table, simulators capture inelastic impacts well while failing to capture elastic impacts. For the higher-dimensional case of a Cassie biped landing from a jump, the simulators capture the bulk motion well but the accuracy is limited by numerous model differences between the real robot and the simulators.

翻译：现实的模拟环境是每个机器人工具箱中的一个基本工具,其使用范围从规划和控制到强化学习的培训政策。 尽管模拟在现代机器人中具有核心作用,但是在将机器人模拟器的性能与现实世界数据进行比较方面,特别是在涉及高速撞击事件动态运动的情景方面,没有做多少工作。 处理动态接触是大多数模拟的计算瓶颈,因此围绕撞击和摩擦的模型和算法选择形成流行工具之间的最大区别。 这里, 我们评估了几个模拟器复制实际世界撞击轨迹的能力。 我们使用实验数据, 确定了流行模拟器Drake、 MuJoCo和Bullet的系统特有联系参数, 分析围绕这些参数进行模型选择的效果。 简单的例子是, 模拟器在无法捕捉到弹性影响的同时, 模拟器捕捉到大量弹性影响。 关于Acie 跳下来的更高维度案例, 模拟器捕捉到大体运动, 但精确度却受到许多模型的局限。