# DeCo: Task Decomposition and Skill Composition for Zero-Shot Generalization in Long-Horizon 3D Manipulation

arXiv: https://arxiv.org/abs/2505.00527 · HTML: https://mlsanity.com/paper/2505.00527/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 1 May 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Built on by (most cited first, 9 of 9)

- [ST-VLA: Enabling 4D-Aware Spatiotemporal Understanding for General Robot Manipulation](https://mlsanity.com/paper/2603.13788/) (2026)
- [Unleashing More Actions via Action Compositional Training for VLA Models](https://mlsanity.com/paper/2607.00351/) (2026)
- [From Code to Action: Hierarchical Learning of Diffusion-VLM Policies](https://mlsanity.com/paper/2509.24917/) (2025)
- [ManiLong-Shot: Interaction-Aware One-Shot Imitation Learning for Long-Horizon Manipulation](https://mlsanity.com/paper/2512.16302/) (2025)
- [RoTri-Diff: A Spatial Robot-Object Triadic Interaction-Guided Diffusion Model for Bimanual Manipulation](https://mlsanity.com/paper/2603.07165/) (2026)
- [Regression Test Selection for Updated Capability Modules in Compositional ML Systems via Atomic-Quality Probes](https://mlsanity.com/paper/2604.26689/) (2026)
- [GORDON: Graph-based Object-centric Rewards for Decomposition of Long-Horizon Manipulation](https://mlsanity.com/paper/2608.03753/) (2026)
- [OGPO: Offline Goal-conditioned Policy Optimization for Recoverable Vision-Language-Action Models](https://mlsanity.com/paper/2610.12914/) (2026)
- [EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents](https://mlsanity.com/paper/2610.13021/) (2026)

## Abstract (arXiv)

Generalizing language-conditioned multi-task imitation learning (IL) models to novel long-horizon 3D manipulation tasks is challenging. To address this, we propose DeCo (Task Decomposition and Skill Composition), a model-agnostic framework that enhances zero-shot generalization to compositional long-horizon manipulation tasks. DeCo decomposes IL demonstrations into modular atomic tasks based on gripper-object interactions, creating a dataset that enables models to learn reusable skills. At inference, DeCo uses a vision-language model (VLM) to parse high-level instructions, retrieve relevant skills, and dynamically schedule their execution. A spatially-aware skill-chaining module ensures smooth, collision-free transitions between skills. We introduce DeCoBench, a benchmark designed to evaluate compositional generalization in long-horizon manipulation tasks. DeCo improves the success rate of three IL models, RVT-2, 3DDA, and ARP, by 66.67%, 21.53%, and 57.92%, respectively, on 12 novel tasks. In real-world experiments, the DeCo-enhanced model, trained on only 6 atomic tasks, completes 9 novel tasks in zero-shot, with a 53.33% improvement over the baseline model. Project website: https://deco226.github.io.