Generative Trajectory Stitching through Diffusion Composition

• URL: http://arxiv.org/abs/2503.05153v1
• Date: Fri, 07 Mar 2025 05:22:52 GMT
• Title: Generative Trajectory Stitching through Diffusion Composition
• Authors: Yunhao Luo, Utkarsh A. Mishra, Yilun Du, Danfei Xu,
• Abstract summary: CompDiffuser is a novel generative approach that can solve new tasks by learning to compositionally stitch together shorter trajectory chunks from previously seen tasks.<n>We conduct experiments on benchmark tasks of various difficulties, covering different environment sizes, agent state dimension, trajectory types, training data quality, and show that CompDiffuser significantly outperforms existing methods.
• Score: 29.997765496994457
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective trajectory stitching for long-horizon planning is a significant challenge in robotic decision-making. While diffusion models have shown promise in planning, they are limited to solving tasks similar to those seen in their training data. We propose CompDiffuser, a novel generative approach that can solve new tasks by learning to compositionally stitch together shorter trajectory chunks from previously seen tasks. Our key insight is modeling the trajectory distribution by subdividing it into overlapping chunks and learning their conditional relationships through a single bidirectional diffusion model. This allows information to propagate between segments during generation, ensuring physically consistent connections. We conduct experiments on benchmark tasks of various difficulties, covering different environment sizes, agent state dimension, trajectory types, training data quality, and show that CompDiffuser significantly outperforms existing methods.

Related papers

• Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent [74.02034188307857]
  Merging multiple expert models offers a promising approach for performing multi-task learning without accessing their original data.<n>We find existing methods inevitably discard task-specific information that, while causing conflicts, is crucial for performance.<n>Our approach consistently outperforms previous methods, achieving state-of-the-art results across diverse architectures and tasks in both vision and NLP domains.
  arXiv  Detail & Related papers  (2025-01-02T12:45:21Z)
• Do We Need to Design Specific Diffusion Models for Different Tasks? Try ONE-PIC [77.8851460746251]
  We propose a simple, efficient, and general approach to fine-tune diffusion models.<n> ONE-PIC enhances the inherited generative ability in the pretrained diffusion models without introducing additional modules.<n>Our method is simple and efficient which streamlines the adaptation process and achieves excellent performance with lower costs.
  arXiv  Detail & Related papers  (2024-12-07T11:19:32Z)
• Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging [33.23758947497205]
  Advanced embedding models are typically developed using large-scale multi-task data and joint training across multiple tasks. To overcome these challenges, we explore model merging-a technique that combines independently trained models to mitigate gradient conflicts and balance data distribution. We introduce a novel method, Self Positioning, which efficiently searches for optimal model combinations within the space of task vectors using gradient descent.
  arXiv  Detail & Related papers  (2024-10-19T08:39:21Z)
• TrACT: A Training Dynamics Aware Contrastive Learning Framework for Long-tail Trajectory Prediction [7.3292387742640415]
  We propose to incorporate richer training dynamics information into a prototypical contrastive learning framework. We conduct empirical evaluations of our approach using two large-scale naturalistic datasets.
  arXiv  Detail & Related papers  (2024-04-18T23:12:46Z)
• Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks [92.32670915472099]
  We propose an ensemble diversification framework exploiting the generation of synthetic counterfactuals using Diffusion Probabilistic Models (DPMs) We show that diffusion-guided diversification can lead models to avert attention from shortcut cues, achieving ensemble diversity performance comparable to previous methods requiring additional data collection.
  arXiv  Detail & Related papers  (2023-10-03T17:37:52Z)
• Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption [73.98706049140098]
  We propose a novel phasic content fusing few-shot diffusion model with directional distribution consistency loss. Specifically, we design a phasic training strategy with phasic content fusion to help our model learn content and style information when t is large. Finally, we propose a cross-domain structure guidance strategy that enhances structure consistency during domain adaptation.
  arXiv  Detail & Related papers  (2023-09-07T14:14:11Z)
• Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning [101.66860222415512]
  Multi-Task Diffusion Model (textscMTDiff) is a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis. For generative planning, we find textscMTDiff outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D.
  arXiv  Detail & Related papers  (2023-05-29T05:20:38Z)
• From Points to Functions: Infinite-dimensional Representations in Diffusion Models [23.916417852496608]
  Diffusion-based generative models learn to iteratively transfer unstructured noise to a complex target distribution. We show that a combination of information content from different time steps gives a strictly better representation for the downstream task.
  arXiv  Detail & Related papers  (2022-10-25T05:30:53Z)
• Interval Bound Interpolation for Few-shot Learning with Few Tasks [15.85259386116784]
  Few-shot learning aims to transfer the knowledge acquired from training on a diverse set of tasks to unseen tasks with a limited amount of labeled data. We introduce the notion of interval bounds from the provably robust training literature to few-shot learning. We then use a novel strategy to artificially form new tasks for training by interpolating between the available tasks and their respective interval bounds.
  arXiv  Detail & Related papers  (2022-04-07T15:29:27Z)
• Mixture of basis for interpretable continual learning with distribution shifts [1.6114012813668934]
  Continual learning in environments with shifting data distributions is a challenging problem with several real-world applications. We propose a novel approach called mixture of Basismodels (MoB) for addressing this problem setting.
  arXiv  Detail & Related papers  (2022-01-05T22:53:15Z)
• Dense Unsupervised Learning for Video Segmentation [49.46930315961636]
  We present a novel approach to unsupervised learning for video object segmentation (VOS) Unlike previous work, our formulation allows to learn dense feature representations directly in a fully convolutional regime. Our approach exceeds the segmentation accuracy of previous work despite using significantly less training data and compute power.
  arXiv  Detail & Related papers  (2021-11-11T15:15:11Z)

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.