We present a sampling-based control approach that can generate smooth actions for general nonlinear systems without external smoothing algorithms. Model Predictive Path Integral (MPPI) control has been utilized in numerous robotic applications due to its appealing characteristics to solve non-convex optimization problems. However, the stochastic nature of sampling-based methods can cause significant chattering in the resulting commands. Chattering becomes more prominent in cases where the environment changes rapidly, possibly even causing the MPPI to diverge. To address this issue, we propose a method that seamlessly combines MPPI with an input-lifting strategy. In addition, we introduce a new action cost to smooth control sequence during trajectory rollouts while preserving the information theoretic interpretation of MPPI, which was derived from non-affine dynamics. We validate our method in two nonlinear control tasks with neural network dynamics: a pendulum swing-up task and a challenging autonomous driving task. The experimental results demonstrate that our method outperforms the MPPI baselines with additionally applied smoothing algorithms.
翻译:我们提出了一个基于取样的控制方法,可以在没有外部平滑算法的情况下,为一般非线性系统产生顺利的行动。模型预测路径综合控制(MPPI)已经用于许多机器人应用中,因为模型预测路径综合控制(MPPI)具有解决非convex优化问题的吸引力特性。然而,基于取样方法的随机性可能会在由此产生的指令中引起重大的争吵。在环境迅速变化的情况下,聊天会变得更加突出,甚至可能导致移动电话伙伴关系倡议出现分歧。为了解决这一问题,我们建议了一种方法,即将移动电话综合控制与投入提升战略无缝地结合起来。此外,我们引入了一种新的行动成本,以在轨迹滚动期间平稳控制序列,同时保留从非节动动态中衍生出的信息对移动电话信息理论解释。我们在两种非线性控制任务中验证了我们的方法,即:笔式摇动任务和具有挑战性的自动驱动任务。实验结果表明,我们的方法比移动电话伙伴关系倡议基线的光滑动算法超出额外应用的平滑算法。