We propose a novel iterative approach for crossing the reality gap that utilises live robot rollouts and differentiable physics. Our method, RealityGrad, demonstrates for the first time, an efficient sim2real transfer in combination with a real2sim model optimisation for closing the reality gap. Differentiable physics has become an alluring alternative to classical rigid-body simulation due to the current culmination of automatic differentiation libraries, compute and non-linear optimisation libraries. Our method builds on this progress and employs differentiable physics for efficient trajectory optimisation. We demonstrate RealitGrad on a dynamic control task for a serial link robot manipulator and present results that show its efficiency and ability to quickly improve not just the robot's performance in real world tasks but also enhance the simulation model for future tasks. One iteration of RealityGrad takes less than 22 minutes on a desktop computer while reducing the error by 2/3, making it efficient compared to other sim2real methods in both compute and time. Our methodology and application of differentiable physics establishes a promising approach for crossing the reality gap and has great potential for scaling to complex environments.
翻译:我们提出了一种创新的迭代方法来克服现实差距,它利用了实实在在的机器人推出和不同的物理学。我们的方法,RealityGrad, 首次展示了一种高效的模拟转机,结合一种真实的2sim模型优化来缩小现实差距。由于目前自动分化图书馆、计算和非线性优化图书馆的顶峰,不同的物理学已经成为典型的僵硬模拟的诱导替代物。我们的方法以这种进步为基础,并使用不同的物理来高效的轨道优化。我们展示了RealitGrad 的序列链接机器人操纵器动态控制任务,并展示了它不仅在现实世界任务中快速改进机器人性能的效率和能力,而且还加强了未来任务的模拟模型。RealityGrad在台式计算机上的一个循环需要不到22分钟的时间,同时将错误减少到2/3,从而在计算时间和时间上与其它的模拟方法相比,效率更高。我们不同的物理方法和应用为跨越现实差距提供了很有希望的方法,并且具有向复杂环境扩展的巨大潜力。