Non-prehensile manipulation in high-dimensional systems is challenging for a variety of reasons, one of the main reasons is the computationally long planning times that come with a large state space. Trajectory optimisation algorithms have proved their utility in a wide variety of tasks, but, like most methods struggle scaling to the high dimensional systems ubiquitous to non-prehensile manipulation in clutter as well as deformable object manipulation. We reason that, during manipulation, different degrees of freedom will become more or less important to the task over time as the system evolves. We leverage this idea to reduce the number of degrees of freedom considered in a trajectory optimisation problem, to reduce planning times. This idea is particularly relevant in the context of model predictive control (MPC) where the cost landscape of the optimisation problem is constantly evolving. We provide simulation results under asynchronous MPC and show our methods are capable of achieving better overall performance due to the decreased policy lag whilst still being able to optimise trajectories effectively.

翻译：高维系统中的非抓取式操作因多种原因而具有挑战性，主要原因之一是伴随大状态空间而来的计算规划时间过长。轨迹优化算法已在多种任务中证明了其实用性，但与大多数方法一样，在扩展到高维系统时面临困难，而这些系统在杂乱环境中的非抓取式操作以及可变形物体操作中普遍存在。我们认为，在操作过程中，随着系统演化，不同自由度对任务的重要性会随时间发生变化。我们利用这一观点，在轨迹优化问题中减少所考虑的自由度数量，以缩短规划时间。这一思路在模型预测控制（MPC）的背景下尤其相关，因为此时优化问题的代价函数地形在不断演化。我们在异步MPC下提供了仿真结果，并表明我们的方法由于降低了策略滞后，同时仍能有效优化轨迹，因而能够实现更好的整体性能。