We present a learning-enabled Task and Motion Planning (TAMP) algorithm for solving mobile manipulation problems in environments with many articulated and movable obstacles. Our idea is to bias the search procedure of a traditional TAMP planner with a learned plan feasibility predictor. The core of our algorithm is PIGINet, a novel Transformer-based learning method that takes in a task plan, the goal, and the initial state, and predicts the probability of finding motion trajectories associated with the task plan. We integrate PIGINet within a TAMP planner that generates a diverse set of high-level task plans, sorts them by their predicted likelihood of feasibility, and refines them in that order. We evaluate the runtime of our TAMP algorithm on seven families of kitchen rearrangement problems, comparing its performance to that of non-learning baselines. Our experiments show that PIGINet substantially improves planning efficiency, cutting down runtime by 80% on problems with small state spaces and 10%-50% on larger ones, after being trained on only 150-600 problems. Finally, it also achieves zero-shot generalization to problems with unseen object categories thanks to its visual encoding of objects. Project page https://piginet.github.io/.

翻译：我们提出了一种基于学习的任务与运动规划（TAMP）算法，用于解决环境中存在大量可活动和可移动障碍物的移动操作问题。其核心思想是利用学习得到的计划可行性预测器来偏置传统TAMP规划器的搜索过程。该算法的核心是PIGINet——一种新颖的基于Transformer的学习方法，它以任务计划、目标和初始状态为输入，预测与该任务计划相关的运动轨迹的发现概率。我们将PIGINet集成到TAMP规划器中，该规划器生成一组多样化的高层任务计划，按预测的可行性概率排序，并依此顺序进行细化。我们在七个厨房重排问题系列上评估了TAMP算法的运行时间，并将其性能与非学习基线方法进行了比较。实验表明，PIGINet显著提升了规划效率：在仅经过150-600个问题的训练后，对于小规模状态空间的问题，运行时间削减了80%；对于更大规模的问题，削减幅度为10%-50%。最后，得益于其对物体的视觉编码，PIGINet还能实现对包含未见物体类别的问题的零样本泛化。项目页面：https://piginet.github.io/。