D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation

• URL: http://arxiv.org/abs/2505.04860v1
• Date: Thu, 08 May 2025 00:03:04 GMT
• Title: D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation
• Authors: I-Chun Arthur Liu, Jason Chen, Gaurav Sukhatme, Daniel Seita,
• Abstract summary: Diffusion for COordinated Dual-arm Data Augmentation (D-CODA) is a method for offline data augmentation tailored to eye-in-hand bimanual imitation learning.<n>D-CODA trains a diffusion model to synthesize novel, viewpoint-consistent wrist-camera images for both arms.<n>It employs constrained optimization to ensure that augmented states involving gripper-to-object contacts adhere to constraints suitable for bimanual coordination.
• Score: 3.208603707050157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning bimanual manipulation is challenging due to its high dimensionality and tight coordination required between two arms. Eye-in-hand imitation learning, which uses wrist-mounted cameras, simplifies perception by focusing on task-relevant views. However, collecting diverse demonstrations remains costly, motivating the need for scalable data augmentation. While prior work has explored visual augmentation in single-arm settings, extending these approaches to bimanual manipulation requires generating viewpoint-consistent observations across both arms and producing corresponding action labels that are both valid and feasible. In this work, we propose Diffusion for COordinated Dual-arm Data Augmentation (D-CODA), a method for offline data augmentation tailored to eye-in-hand bimanual imitation learning that trains a diffusion model to synthesize novel, viewpoint-consistent wrist-camera images for both arms while simultaneously generating joint-space action labels. It employs constrained optimization to ensure that augmented states involving gripper-to-object contacts adhere to constraints suitable for bimanual coordination. We evaluate D-CODA on 5 simulated and 3 real-world tasks. Our results across 2250 simulation trials and 300 real-world trials demonstrate that it outperforms baselines and ablations, showing its potential for scalable data augmentation in eye-in-hand bimanual manipulation. Our project website is at: https://dcodaaug.github.io/D-CODA/.

Related papers

• CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled Demonstrations [11.604546089466734]
  Learning robot policies using imitation learning requires collecting large amounts of costly action-labeled expert demonstrations.<n>A promising approach is to harness the abundance of unlabeled observations-e.g., from video demonstrations-to learn latent action labels in an unsupervised way.<n>We design continuous latent action models (CLAM) which incorporate two key ingredients we find necessary for learning to solve complex continuous control tasks from unlabeled observation data.
  arXiv  Detail & Related papers  (2025-05-08T07:07:58Z)
• HOIGaze: Gaze Estimation During Hand-Object Interactions in Extended Reality Exploiting Eye-Hand-Head Coordination [10.982807572404166]
  HOIGaze is a learning-based approach for gaze estimation during hand-object interactions (HOIs) in extended reality (XR)<n>The eye, hand, and head movements are closely coordinated during HOIs and this coordination can be exploited to identify samples that are most useful for gaze training.<n>We evaluate HOIGaze on the HOT3D and Aria digital twin (ADT) datasets and show that it significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-04-28T14:31:43Z)
• Learning to Align and Refine: A Foundation-to-Diffusion Framework for Occlusion-Robust Two-Hand Reconstruction [50.952228546326516]
  Two-hand reconstruction from monocular images faces persistent challenges due to complex and dynamic hand postures.<n>Existing approaches struggle with such alignment issues, often resulting in misalignment and penetration artifacts.<n>We propose a dual-stage Foundation-to-Diffusion framework that precisely align 2D prior guidance from vision foundation models.
  arXiv  Detail & Related papers  (2025-03-22T14:42:27Z)
• Enhancing 3D Gaze Estimation in the Wild using Weak Supervision with Gaze Following Labels [10.827081942898506]
  We introduce a novel Self-Training Weakly-Supervised Gaze Estimation framework (ST-WSGE)<n>We propose the Gaze Transformer (GaT), a modality-agnostic architecture capable of simultaneously learning static and dynamic gaze information from both image and video datasets.<n>By combining 3D video datasets with 2D gaze target labels from gaze following tasks, our approach achieves the following key contributions.
  arXiv  Detail & Related papers  (2025-02-27T16:35:25Z)
• CycleHOI: Improving Human-Object Interaction Detection with Cycle Consistency of Detection and Generation [37.45945633515955]
  We propose a new learning framework, coined as CycleHOI, to boost the performance of human-object interaction (HOI) detection. Our key design is to introduce a novel cycle consistency loss for the training of HOI detector. We perform extensive experiments to verify the effectiveness and generalization power of our CycleHOI.
  arXiv  Detail & Related papers  (2024-07-16T06:55:43Z)
• Gaze-guided Hand-Object Interaction Synthesis: Dataset and Method [61.19028558470065]
  We present GazeHOI, the first dataset to capture simultaneous 3D modeling of gaze, hand, and object interactions.<n>To tackle these issues, we propose a stacked gaze-guided hand-object interaction diffusion model, named GHO-Diffusion.<n>We also introduce HOI-Manifold Guidance during the sampling stage of GHO-Diffusion, enabling fine-grained control over generated motions.
  arXiv  Detail & Related papers  (2024-03-24T14:24:13Z)
• S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR [50.435592120607815]
  Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR) Previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. In this study, we introduce a novel single-stage bi-modal transformer framework for SGG in the OR, termed S2Former-OR.
  arXiv  Detail & Related papers  (2024-02-22T11:40:49Z)
• DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection [77.6648187359111]
  We propose a novel data augmentation method, named textbfDualAug, to keep the augmentation in distribution as much as possible at a reasonable time and computational cost. Experiments on supervised image classification benchmarks show that DualAug improve various automated data augmentation method.
  arXiv  Detail & Related papers  (2023-10-12T08:55:10Z)
• Unified Visual Relationship Detection with Vision and Language Models [89.77838890788638]
  This work focuses on training a single visual relationship detector predicting over the union of label spaces from multiple datasets. We propose UniVRD, a novel bottom-up method for Unified Visual Relationship Detection by leveraging vision and language models. Empirical results on both human-object interaction detection and scene-graph generation demonstrate the competitive performance of our model.
  arXiv  Detail & Related papers  (2023-03-16T00:06:28Z)
• Skeleton-based Action Recognition through Contrasting Two-Stream Spatial-Temporal Networks [11.66009967197084]
  We propose a novel Contrastive GCN-Transformer Network (ConGT) which fuses the spatial and temporal modules in a parallel way. We conduct experiments on three benchmark datasets, which demonstrate that our model achieves state-of-the-art performance in action recognition.
  arXiv  Detail & Related papers  (2023-01-27T02:12:08Z)
• Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition [65.78703941973183]
  We propose a novel correlation-driven joint-bone fusion graph convolutional network (CD-JBF-GCN) as an encoder and use a pose prediction head as a decoder. Specifically, the CD-JBF-GC can explore the motion transmission between the joint stream and the bone stream. The pose prediction based auto-encoder in the self-supervised training stage allows the network to learn motion representation from unlabeled data.
  arXiv  Detail & Related papers  (2022-02-08T16:03:15Z)
• DecAug: Augmenting HOI Detection via Decomposition [54.65572599920679]
  Current algorithms suffer from insufficient training samples and category imbalance within datasets. We propose an efficient and effective data augmentation method called DecAug for HOI detection. Experiments show that our method brings up to 3.3 mAP and 1.6 mAP improvements on V-COCO and HICODET dataset.
  arXiv  Detail & Related papers  (2020-10-02T13:59:05Z)

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.