Developing intelligent robots for complex manipulation tasks in household and factory settings remains challenging due to long-horizon tasks, contact-rich manipulation, and the need to generalize across a wide variety of object shapes and scene layouts. While Task and Motion Planning (TAMP) offers a promising solution, its assumptions such as kinodynamic models limit applicability in novel contexts. Neural object descriptors (NODs) have shown promise in object and scene generalization but face limitations in addressing broader tasks. Our proposed TAMP-based framework, NOD-TAMP, extracts short manipulation trajectories from a handful of human demonstrations, adapts these trajectories using NOD features, and composes them to solve broad long-horizon tasks. Validated in a simulation environment, NOD-TAMP effectively tackles varied challenges and outperforms existing methods, establishing a cohesive framework for manipulation planning. For videos and other supplemental material, see the project website: https://sites.google.com/view/nod-tamp/.

通过提取人类示范的短时间操纵轨迹，使用神经目标描述符进行适应性处理，并将其组合以解决广泛的长期任务，我们的提出的基于TAMP的NOD-TAMP框架有效地解决了各种挑战，并在模拟环境中胜过现有方法，为操纵规划建立了一个有机的框架。

NOD-TAMP: 多步操作规划与神经对象描述符