Towards a Data Flywheel for Embodied Intelligence in Logistics

• URL: http://arxiv.org/abs/2606.05960v1
• Date: Thu, 04 Jun 2026 09:58:55 GMT
• Title: Towards a Data Flywheel for Embodied Intelligence in Logistics
• Abstract summary: This research studies a data-centric framework for industrial embodied intelligence by constructing a logistics data flywheel.<n>Our framework converts daily operations into reusable data assets, uses World Models to generate reliable supervision for long-tail parcel manipulation, and feeds deployment feedback back into policy improvement.
• Score: 10.629123284028298
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Embodied intelligence is moving from laboratory demonstrations toward industrial deployment, with the logistics industry serving as a key application scenario. Learning-based policies offer a promising path beyond traditional perception-planning-control pipelines, but their scalability depends on how embodied data can be collected, organized, and reused. This research studies a data-centric framework for industrial embodied intelligence by constructing a logistics data flywheel. Our framework converts daily operations into reusable data assets, uses World Models to generate reliable supervision for long-tail parcel manipulation, and feeds deployment feedback back into policy improvement. As an initial result, \textit{WM-DAgger} introduces a World-Model-based data aggregation framework that synthesizes out-of-distribution recovery data for robust imitation learning. Building on this result, ongoing work explores how large-scale in-the-wild multimodal data, including labeled human demonstrations, unlabeled operational videos, and system-level robot logs, can be aligned for policy learning and transformed into feedback for continual system improvement.

Related papers

• Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale [15.09465040407523]
  Existing data agents struggle to generalize to unseen data environments and analytical, especially in heterogeneous enterprise settings.<n>We introduce TOFFEE, a system for synthesizing high-quality data agent trajectories from given data environments via Monte Carlo Tree Search.<n>We show that TOFFEE can effectively generate scalable trajectory data for complex analytical tasks across heterogeneous environments.
  arXiv  Detail & Related papers  (2026-07-07T13:02:08Z)
• Towards Next-Generation LLM Training: From the Data-Centric Perspective [29.115711465172122]
  Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks and domains, with data playing a central role in enabling these advances.<n>Despite this success, the preparation and effective utilization of the massive datasets required for LLM training remain major bottlenecks.<n>We propose building a robust, agent-based automatic data preparation system that supports automated workflow construction and scalable data management.<n>We argue for a unified data-model interaction training system in which data is dynamically selected, mixed, and reweighted throughout the training process, enabling more efficient, adaptive, and performance-aware data utilization.
  arXiv  Detail & Related papers  (2026-03-16T01:40:09Z)
• Data Science and Technology Towards AGI Part I: Tiered Data Management [53.64581824953229]
  We argue that the development of artificial intelligence is entering a new phase of data-model co-evolution.<n>We introduce an L0-L4 tiered data management framework, ranging from raw uncurated resources to organized and verifiable knowledge.<n>We validate the effectiveness of the proposed framework through empirical studies.
  arXiv  Detail & Related papers  (2026-02-09T18:47:51Z)
• FELA: A Multi-Agent Evolutionary System for Feature Engineering of Industrial Event Log Data [7.129004248608012]
  Event log data represents one of the most valuable assets for modern digital services.<n>Existing automatic feature engineering approaches, such as AutoML or genetic methods, often suffer from limited explainability.<n>We propose FELA, a multi-agent evolutionary system that autonomously extracts meaningful and high-performing features from complex industrial event log data.
  arXiv  Detail & Related papers  (2025-10-29T06:57:32Z)
• Scaling Generalist Data-Analytic Agents [95.05161133349242]
  DataMind is a scalable data synthesis and agent training recipe designed to build generalist data-analytic agents.<n>DataMind tackles three key challenges in building open-source data-analytic agents.
  arXiv  Detail & Related papers  (2025-09-29T17:23:08Z)
• Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures [27.473508984130728]
  Large-scale scientific collaborations generate petabytes of data, with volumes soon expected to reach exabytes.<n>To manage these computational and storage demands, centralized workflow and data management systems are implemented.<n>A significant obstacle in adopting more effective or AI-driven solutions is the absence of a quick and reliable introspective dynamic model.
  arXiv  Detail & Related papers  (2025-06-24T12:42:36Z)
• Meta-Statistical Learning: Supervised Learning of Statistical Inference [59.463430294611626]
  This work demonstrates that the tools and principles driving the success of large language models (LLMs) can be repurposed to tackle distribution-level tasks.<n>We propose meta-statistical learning, a framework inspired by multi-instance learning that reformulates statistical inference tasks as supervised learning problems.
  arXiv  Detail & Related papers  (2025-02-17T18:04:39Z)
• Flex: End-to-End Text-Instructed Visual Navigation from Foundation Model Features [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies.<n>Our findings are synthesized in Flex (Fly lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors.<n>We demonstrate the effectiveness of this approach on a quadrotor fly-to-target task, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• A spectrum of physics-informed Gaussian processes for regression in engineering [0.0]
  Despite the growing availability of sensing and data in general, we remain unable to fully characterise many in-service engineering systems and structures from a purely data-driven approach. This paper pursues the combination of machine learning technology and physics-based reasoning to enhance our ability to make predictive models with limited data.
  arXiv  Detail & Related papers  (2023-09-19T14:39:03Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)
• Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming [77.38174112525168]
  We present Nemo, an end-to-end interactive Supervision system that improves overall productivity of WS learning pipeline by an average 20% (and up to 47% in one task) compared to the prevailing WS supervision approach.
  arXiv  Detail & Related papers  (2022-03-02T19:57:32Z)
• From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability [10.27718355111707]
  This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes. Grounded in this described data modeling pipeline for ITS, wedefine the characteristics, engineering requisites and intrinsic challenges to its three compounding stages, namely, data fusion, adaptive learning and model evaluation.
  arXiv  Detail & Related papers  (2020-02-06T12:02:30Z)
• DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier [58.979104709647295]
  We bridge the gap between the abundance of available data and lack of relevant data, for the future learning tasks of a trained network. We use the available data, that may be an imbalanced subset of the original training dataset, or a related domain dataset, to retrieve representative samples. We demonstrate that data from a related domain can be leveraged to achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2019-12-27T02:05:45Z)

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.