xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing

• URL: http://arxiv.org/abs/2409.08687v2
• Date: Fri, 11 Oct 2024 17:15:39 GMT
• Title: xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing
• Authors: Haoyi Niu, Qimao Chen, Tenglong Liu, Jianxiong Li, Guyue Zhou, Yi Zhang, Jianming Hu, Xianyuan Zhan,
• Abstract summary: Cross-domain policy transfer methods mostly aim at learning domain correspondences or corrections to facilitate policy learning. We propose the Cross-Domain Trajectory EDiting framework that employs a specially designed diffusion model for cross-domain trajectory adaptation. Our proposed model architecture effectively captures the intricate dependencies among states, actions, and rewards, as well as the dynamics patterns within target data.
• Score: 21.37585797507323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reusing pre-collected data from different domains is an appealing solution for decision-making tasks that have insufficient data in the target domain but are relatively abundant in other related domains. Existing cross-domain policy transfer methods mostly aim at learning domain correspondences or corrections to facilitate policy learning, such as learning domain/task-specific discriminators, representations, or policies. This design philosophy often results in heavy model architectures or task/domain-specific modeling, lacking flexibility. This reality makes us wonder: can we directly bridge the domain gaps universally at the data level, instead of relying on complex downstream cross-domain policy transfer models? In this study, we propose the Cross-Domain Trajectory EDiting (xTED) framework that employs a specially designed diffusion model for cross-domain trajectory adaptation. Our proposed model architecture effectively captures the intricate dependencies among states, actions, and rewards, as well as the dynamics patterns within target data. By utilizing the pre-trained diffusion as a prior, source domain trajectories can be transformed to match with target domain properties while preserving original semantic information. This process implicitly corrects underlying domain gaps, enhancing state realism and dynamics reliability in the source data, and allowing flexible incorporation with various downstream policy learning methods. Despite its simplicity, xTED demonstrates superior performance in extensive simulation and real-robot experiments.

Related papers

• Cross-Domain Policy Adaptation by Capturing Representation Mismatch [53.087413751430255]
  It is vital to learn effective policies that can be transferred to different domains with dynamics discrepancies in reinforcement learning (RL) In this paper, we consider dynamics adaptation settings where there exists dynamics mismatch between the source domain and the target domain. We perform representation learning only in the target domain and measure the representation deviations on the transitions from the source domain.
  arXiv  Detail & Related papers  (2024-05-24T09:06:12Z)
• DIGIC: Domain Generalizable Imitation Learning by Causal Discovery [69.13526582209165]
  Causality has been combined with machine learning to produce robust representations for domain generalization. We make a different attempt by leveraging the demonstration data distribution to discover causal features for a domain generalizable policy. We design a novel framework, called DIGIC, to identify the causal features by finding the direct cause of the expert action from the demonstration data distribution.
  arXiv  Detail & Related papers  (2024-02-29T07:09:01Z)
• One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers [96.51828911883456]
  Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data. Traditional UDA often assumes that there are abundant unlabeled real-world data samples available during training for the adaptation. We explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization problem, where only one real-world data sample is available.
  arXiv  Detail & Related papers  (2022-12-14T15:54:15Z)
• Transfer RL via the Undo Maps Formalism [29.798971172941627]
  Transferring knowledge across domains is one of the most fundamental problems in machine learning. We propose TvD: transfer via distribution matching, a framework to transfer knowledge across interactive domains. We show this objective leads to a policy update scheme reminiscent of imitation learning, and derive an efficient algorithm to implement it.
  arXiv  Detail & Related papers  (2022-11-26T03:44:28Z)
• Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts [133.99270341855728]
  Real-world domain styles can vary substantially due to environment changes and sensor noises. Deep models only know the training domain style. We propose Normalization Perturbation to overcome this domain style overfitting problem.
  arXiv  Detail & Related papers  (2022-11-08T17:36:49Z)
• Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images [93.50240389540252]
  Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. We propose a novel stagewise domain adaptation model called RoadDA to address the domain shift (DS) issue in this field. Experiment results on two benchmarks demonstrate that RoadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-28T09:29:14Z)
• Multilevel Knowledge Transfer for Cross-Domain Object Detection [26.105283273950942]
  Domain shift is a well known problem where a model trained on a particular domain (source) does not perform well when exposed to samples from a different domain (target) In this work, we address the domain shift problem for the object detection task. Our approach relies on gradually removing the domain shift between the source and the target domains.
  arXiv  Detail & Related papers  (2021-08-02T15:24:40Z)
• Learning causal representations for robust domain adaptation [31.261956776418618]
  In many real-world applications, target domain data may not always be available. In this paper, we study the cases where at the training phase the target domain data is unavailable. We propose a novel Causal AutoEncoder (CAE), which integrates deep autoencoder and causal structure learning into a unified model.
  arXiv  Detail & Related papers  (2020-11-12T11:24:03Z)
• Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers [138.68213707587822]
  We propose a simple, practical, and intuitive approach for domain adaptation in reinforcement learning. We show that we can achieve this goal by compensating for the difference in dynamics by modifying the reward function. Our approach is applicable to domains with continuous states and actions and does not require learning an explicit model of the dynamics.
  arXiv  Detail & Related papers  (2020-06-24T17:47:37Z)
• Contradistinguisher: A Vapnik's Imperative to Unsupervised Domain Adaptation [7.538482310185133]
  We propose a model referred Contradistinguisher that learns contrastive features and whose objective is to jointly learn to contradistinguish the unlabeled target domain in an unsupervised way. We achieve the state-of-the-art on Office-31 and VisDA-2017 datasets in both single-source and multi-source settings.
  arXiv  Detail & Related papers  (2020-05-25T19:54:38Z)

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.