Manipulation of articulated and deformable objects can be difficult due to their compliant and under-actuated nature. Unexpected disturbances can cause the object to deviate from a predicted state, making it necessary to use Model-Predictive Control (MPC) methods to plan motion. However, these methods need a short planning horizon to be practical. Thus, MPC is ill-suited for long-horizon manipulation tasks due to local minima. In this paper, we present a diffusion-based method that guides an MPC method to accomplish long-horizon manipulation tasks by dynamically specifying sequences of subgoals for the MPC to follow. Our method, called Subgoal Diffuser, generates subgoals in a coarse-to-fine manner, producing sparse subgoals when the task is easily accomplished by MPC and more dense subgoals when the MPC method needs more guidance. The density of subgoals is determined dynamically based on a learned estimate of reachability, and subgoals are distributed to focus on challenging parts of the task. We evaluate our method on two robot manipulation tasks and find it improves the planning performance of an MPC method, and also outperforms prior diffusion-based methods.

翻译：铰接式和可变形物体的操控因其柔顺性和欠驱动特性而颇具挑战。意外扰动会导致物体偏离预测状态，因此需要采用模型预测控制方法进行运动规划。然而，此类方法为保持实用性需采用较短的规划时域，故易陷入局部最优，难以适用于长时域操控任务。本文提出一种基于扩散的方法，通过动态生成供模型预测控制遵循的子目标序列，引导其完成长时域操控任务。该方法名为子目标扩散器，采用从粗到精的方式生成子目标：当任务易于被模型预测控制完成时生成稀疏子目标；当需要更多引导时则生成密集子目标。子目标密度基于可到达性的学习估计动态确定，且子目标分布聚焦于任务中的困难环节。我们在两个机器人操控任务上评估了该方法，结果表明其不仅能提升模型预测控制的规划性能，还优于现有的基于扩散的方法。