  Solving complex manipulation tasks in household and factory settings remains
challenging due to long-horizon reasoning, fine-grained interactions, and broad
object and scene diversity. Learning skills from demonstrations can be an
effective strategy, but such methods often have limited generalizability beyond
training data and struggle to solve long-horizon tasks. To overcome this, we
propose to synergistically combine two paradigms: Neural Object Descriptors
(NODs) that produce generalizable object-centric features and Task and Motion
Planning (TAMP) frameworks that chain short-horizon skills to solve multi-step
tasks. We introduce NOD-TAMP, a TAMP-based framework that extracts short
manipulation trajectories from a handful of human demonstrations, adapts these
trajectories using NOD features, and composes them to solve broad long-horizon,
contact-rich tasks. NOD-TAMP solves existing manipulation benchmarks with a
handful of demonstrations and significantly outperforms prior NOD-based
approaches on new tabletop manipulation tasks that require diverse
generalization. Finally, we deploy NOD-TAMP on a number of real-world tasks,
including tool-use and high-precision insertion. For more details, please visit
https://nodtamp.github.io/.
