What Matters in Learning from Large-Scale Datasets for Robot Manipulation

• URL: http://arxiv.org/abs/2506.13536v1
• Date: Mon, 16 Jun 2025 14:25:29 GMT
• Title: What Matters in Learning from Large-Scale Datasets for Robot Manipulation
• Authors: Vaibhav Saxena, Matthew Bronars, Nadun Ranawaka Arachchige, Kuancheng Wang, Woo Chul Shin, Soroush Nasiriany, Ajay Mandlekar, Danfei Xu,
• Abstract summary: We conduct a large-scale dataset composition study to answer this question.<n>We develop a data generation framework to procedurally emulate common sources of diversity in existing datasets.<n>We find that camera poses and spatial arrangements are crucial dimensions for both diversity in collection and alignment in retrieval.
• Score: 12.703188997313223
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Imitation learning from large multi-task demonstration datasets has emerged as a promising path for building generally-capable robots. As a result, 1000s of hours have been spent on building such large-scale datasets around the globe. Despite the continuous growth of such efforts, we still lack a systematic understanding of what data should be collected to improve the utility of a robotics dataset and facilitate downstream policy learning. In this work, we conduct a large-scale dataset composition study to answer this question. We develop a data generation framework to procedurally emulate common sources of diversity in existing datasets (such as sensor placements and object types and arrangements), and use it to generate large-scale robot datasets with controlled compositions, enabling a suite of dataset composition studies that would be prohibitively expensive in the real world. We focus on two practical settings: (1) what types of diversity should be emphasized when future researchers collect large-scale datasets for robotics, and (2) how should current practitioners retrieve relevant demonstrations from existing datasets to maximize downstream policy performance on tasks of interest. Our study yields several critical insights -- for example, we find that camera poses and spatial arrangements are crucial dimensions for both diversity in collection and alignment in retrieval. In real-world robot learning settings, we find that not only do our insights from simulation carry over, but our retrieval strategies on existing datasets such as DROID allow us to consistently outperform existing training strategies by up to 70%. More results at https://robo-mimiclabs.github.io/

Related papers

• Efficient Data Collection for Robotic Manipulation via Compositional Generalization [70.76782930312746]
  We show that policies can compose environmental factors from their data to succeed when encountering unseen factor combinations. We propose better in-domain data collection strategies that exploit composition. We provide videos at http://iliad.stanford.edu/robot-data-comp/.
  arXiv  Detail & Related papers  (2024-03-08T07:15:38Z)
• BridgeData V2: A Dataset for Robot Learning at Scale [73.86688388408021]
  BridgeData V2 is a large and diverse dataset of robotic manipulation behaviors. It contains 60,096 trajectories collected across 24 environments on a publicly available low-cost robot.
  arXiv  Detail & Related papers  (2023-08-24T17:41:20Z)
• RT-1: Robotics Transformer for Real-World Control at Scale [98.09428483862165]
  We present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks.
  arXiv  Detail & Related papers  (2022-12-13T18:55:15Z)
• MetaGraspNet: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis [78.26022688167133]
  We present a large-scale benchmark dataset for vision-driven robotic grasping via physics-based metaverse synthesis. The proposed dataset contains 100,000 images and 25 different object types. We also propose a new layout-weighted performance metric alongside the dataset for evaluating object detection and segmentation performance.
  arXiv  Detail & Related papers  (2021-12-29T17:23:24Z)
• Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets [122.85598648289789]
  We study how multi-domain and multi-task datasets can improve the learning of new tasks in new environments. We also find that data for only a few tasks in a new domain can bridge the domain gap and make it possible for a robot to perform a variety of prior tasks that were only seen in other domains.
  arXiv  Detail & Related papers  (2021-09-27T23:42:12Z)
• Efficient Self-Supervised Data Collection for Offline Robot Learning [17.461103383630853]
  A practical approach to robot reinforcement learning is to first collect a large batch of real or simulated robot interaction data. We develop a simple-yet-effective goal-conditioned reinforcement-learning method that actively focuses data collection on novel observations.
  arXiv  Detail & Related papers  (2021-05-10T18:42:58Z)
• REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter [52.117388513480435]
  We present a new dataset named regrad to sustain the modeling of relationships among objects and grasps. Our dataset is collected in both forms of 2D images and 3D point clouds. Users are free to import their own object models for the generation of as many data as they want.
  arXiv  Detail & Related papers  (2021-04-29T05:31:21Z)

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.