RDD: Retrieval-Based Demonstration Decomposer for Planner Alignment in Long-Horizon Tasks

• URL: http://arxiv.org/abs/2510.14968v1
• Date: Thu, 16 Oct 2025 17:59:37 GMT
• Title: RDD: Retrieval-Based Demonstration Decomposer for Planner Alignment in Long-Horizon Tasks
• Authors: Mingxuan Yan, Yuping Wang, Zechun Liu, Jiachen Li,
• Abstract summary: We propose a Demonstration Decomposer that automatically decomposes demonstrations into sub-tasks.<n>Our method outperforms the state-of-the-art sub-task decomposer on both simulation and real-world tasks.
• Score: 21.341051218915535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To tackle long-horizon tasks, recent hierarchical vision-language-action (VLAs) frameworks employ vision-language model (VLM)-based planners to decompose complex manipulation tasks into simpler sub-tasks that low-level visuomotor policies can easily handle. Typically, the VLM planner is finetuned to learn to decompose a target task. This finetuning requires target task demonstrations segmented into sub-tasks by either human annotation or heuristic rules. However, the heuristic subtasks can deviate significantly from the training data of the visuomotor policy, which degrades task performance. To address these issues, we propose a Retrieval-based Demonstration Decomposer (RDD) that automatically decomposes demonstrations into sub-tasks by aligning the visual features of the decomposed sub-task intervals with those from the training data of the low-level visuomotor policies. Our method outperforms the state-of-the-art sub-task decomposer on both simulation and real-world tasks, demonstrating robustness across diverse settings. Code and more results are available at rdd-neurips.github.io.

Related papers

• From Code to Action: Hierarchical Learning of Diffusion-VLM Policies [8.0703783175731]
  Imitation learning for robotic manipulation often suffers from limited generalization and data scarcity.<n>In this work, we introduce a hierarchical framework that leverages code-generating vision-language models (VLMs)<n>We find that this design enables interpretable policy decomposition, improves generalization when compared to flat policies and enables separate evaluation of high-level planning and low-level control.
  arXiv  Detail & Related papers  (2025-09-29T15:22:18Z)
• Embodied Long Horizon Manipulation with Closed-loop Code Generation and Incremental Few-shot Adaptation [12.077740860502878]
  Embodied long-horizon manipulation requires robotic systems to process multimodal inputs-such as vision and natural language-and translate them into executable actions.<n>Recent methods have explored using large language models (LLMs) as high-level planners that decompose tasks into subtasks using natural language and guide pretrained low-level controllers.<n>Our framework achieves state-of-the-art performance on 30+ diverse seen and unseen long-horizon tasks across LoHoRavens, CALVIN, Franka Kitchen, and cluttered real-world settings.
  arXiv  Detail & Related papers  (2025-03-27T20:32:58Z)
• Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning [85.66907881270785]
  We propose a data curriculum method, namely Data-CUBE, that arranges the orders of all the multi-task data for training. In the task level, we aim to find the optimal task order to minimize the total cross-task interference risk. In the instance level, we measure the difficulty of all instances per task, then divide them into the easy-to-difficult mini-batches for training.
  arXiv  Detail & Related papers  (2024-01-07T18:12:20Z)
• TaskBench: Benchmarking Large Language Models for Task Automation [82.2932794189585]
  We introduce TaskBench, a framework to evaluate the capability of large language models (LLMs) in task automation. Specifically, task decomposition, tool selection, and parameter prediction are assessed. Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation.
  arXiv  Detail & Related papers  (2023-11-30T18:02:44Z)
• Universal Visual Decomposer: Long-Horizon Manipulation Made Easy [54.93745986073738]
  Real-world robotic tasks stretch over extended horizons and encompass multiple stages. Prior task decomposition methods require task-specific knowledge, are computationally intensive, and cannot readily be applied to new tasks. We propose Universal Visual Decomposer (UVD), an off-the-shelf task decomposition method for visual long horizon manipulation. We extensively evaluate UVD on both simulation and real-world tasks, and in all cases, UVD substantially outperforms baselines across imitation and reinforcement learning settings.
  arXiv  Detail & Related papers  (2023-10-12T17:59:41Z)
• Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents [99.17668730578586]
  Pre-trained large language models (LLMs) capture procedural knowledge about the world. Plan, Eliminate, and Track (PET) framework translates a task description into a list of high-level sub-tasks. PET framework leads to a significant 15% improvement over SOTA for generalization to human goal specifications.
  arXiv  Detail & Related papers  (2023-05-03T20:11:22Z)
• Task Compass: Scaling Multi-task Pre-training with Task Prefix [122.49242976184617]
  Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks. We propose a task prefix guided multi-task pre-training framework to explore the relationships among tasks. Our model can not only serve as the strong foundation backbone for a wide range of tasks but also be feasible as a probing tool for analyzing task relationships.
  arXiv  Detail & Related papers  (2022-10-12T15:02:04Z)
• Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization [101.72755769194677]
  We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph. Our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks. Our experiment results on 2D grid-world and complex web navigation domains show that the proposed method can learn and leverage the common underlying structure of the tasks for faster adaptation to the unseen tasks.
  arXiv  Detail & Related papers  (2022-05-25T10:44:25Z)
• Analysis and Prediction of NLP Models Via Task Embeddings [25.311690222754454]
  We propose MetaEval, a collection of $101$ NLP tasks. We fit a single transformer to all MetaEval tasks jointly while conditioning it on learned embeddings. The resulting task embeddings enable a novel analysis of the space of tasks.
  arXiv  Detail & Related papers  (2021-12-10T16:23:24Z)
• Learning Task Decomposition with Ordered Memory Policy Network [73.3813423684999]
  We propose Ordered Memory Policy Network (OMPN) to discover subtask hierarchy by learning from demonstration. OMPN can be applied to partially observable environments and still achieve higher task decomposition performance. Our visualization confirms that the subtask hierarchy can emerge in our model.
  arXiv  Detail & Related papers  (2021-03-19T18:13:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.