SoftMimicGen: A Data Generation System for Scalable Robot Learning in Deformable Object Manipulation

• URL: http://arxiv.org/abs/2603.25725v1
• Date: Thu, 26 Mar 2026 17:58:40 GMT
• Title: SoftMimicGen: A Data Generation System for Scalable Robot Learning in Deformable Object Manipulation
• Abstract summary: We introduce SoftMimicGen, an automated data generation pipeline for deformable object manipulation tasks.<n>We introduce a suite of high-fidelity simulation environments that encompasses a wide range of deformable objects.<n>We apply SoftMimicGen to generate datasets across the task suite, train high-performing policies from the data, and systematically analyze the data generation system.
• Score: 45.71119158785489
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale robot datasets have facilitated the learning of a wide range of robot manipulation skills, but these datasets remain difficult to collect and scale further, owing to the intractable amount of human time, effort, and cost required. Simulation and synthetic data generation have proven to be an effective alternative to fuel this need for data, especially with the advent of recent work showing that such synthetic datasets can dramatically reduce real-world data requirements and facilitate generalization to novel scenarios unseen in real-world demonstrations. However, this paradigm has been limited to rigid-body tasks, which are easy to simulate. Deformable object manipulation encompasses a large portion of real-world manipulation and remains a crucial gap to address towards increasing adoption of the synthetic simulation data paradigm. In this paper, we introduce SoftMimicGen, an automated data generation pipeline for deformable object manipulation tasks. We introduce a suite of high-fidelity simulation environments that encompasses a wide range of deformable objects (stuffed animal, rope, tissue, towel) and manipulation behaviors (high-precision threading, dynamic whipping, folding, pick-and-place), across four robot embodiments: a single-arm manipulator, bimanual arms, a humanoid, and a surgical robot. We apply SoftMimicGen to generate datasets across the task suite, train high-performing policies from the data, and systematically analyze the data generation system. Project website: \href{https://softmimicgen.github.io}{softmimicgen.github.io}.

Related papers

• OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation [51.37436817948304]
  OASIS is a simulation-data-driven framework for humanoid loco-manipulation.<n>It reconstructs realistic object assets from real-world images using a 3D generative model.<n>Under zero-shot deployment, it achieves higher success rates on most tasks than that trained on real-robot teleoperation data.
  arXiv  Detail & Related papers  (2026-06-07T10:01:53Z)
• HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning [52.022681285103126]
  We present HumanoidMimicGen, a method for generating humanoid legged loco-manipulation data.<n>Our method adapts contact-rich whole-body skills from a handful of source demonstrations to new states.<n>We show that whole-body visuomotor policies co-trained with data generated by HumanoidMimicGen outperform those trained only on real-world data by 20%.
  arXiv  Detail & Related papers  (2026-05-26T21:57:11Z)
• ComSim: Building Scalable Real-World Robot Data Generation via Compositional Simulation [90.4702774169675]
  We propose a novel hybrid approach called Compositional Simulation, which combines classical simulation and neural simulation.<n>Our approach utilizes a closed-loop real-sim-real data augmentation pipeline, leveraging a small amount of real-world data.<n>We train a neural simulator to transform classical simulation videos into real-world representations, improving the accuracy of policy models trained in real-world environments.
  arXiv  Detail & Related papers  (2026-04-13T12:25:45Z)
• D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping [66.22412592525369]
  We introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine.<n>We show that our engine achieves accurate and robust performance in mass identification across various object geometries and mass values.<n>Those optimized mass values facilitate force-aware policy learning, achieving superior and high performance in object grasping.
  arXiv  Detail & Related papers  (2026-03-01T15:32:04Z)
• RoboPearls: Editable Video Simulation for Robot Manipulation [81.18434338506621]
  RoboPearls is an editable video simulation framework for robotic manipulation.<n>Built on 3D Gaussian Splatting (3DGS), RoboPearls enables the construction of photo-realistic, view-consistent simulations.<n>We conduct extensive experiments on multiple datasets and scenes, including RLBench, COLOSSEUM, Ego4D, Open X-Embodiment, and a real-world robot.
  arXiv  Detail & Related papers  (2025-06-28T05:03:31Z)
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation [52.2244588424002]
  We present RoboTwin 2.0, a scalable framework for automated, large-scale generation of diverse and realistic data.<n>At its core is RoboTwin-OD, an object library of 731 instances across 147 categories with semantic and manipulation-relevant annotations.<n>To improve sim-to-real transfer, RoboTwin 2.0 applies structured domain randomization along five axes.
  arXiv  Detail & Related papers  (2025-06-22T16:26:53Z)
• Synthetic Dataset Generation for Autonomous Mobile Robots Using 3D Gaussian Splatting for Vision Training [0.708987965338602]
  We propose a novel method for automatically generating annotated synthetic data in Unreal Engine.<n>We demonstrate that synthetic datasets can achieve performance comparable to that of real-world datasets.<n>This is the first application of synthetic data for training object detection algorithms in robot soccer.
  arXiv  Detail & Related papers  (2025-06-05T14:37:40Z)
• Sim-and-Real Co-Training: A Simple Recipe for Vision-Based Robotic Manipulation [40.96453435496208]
  We present a recipe for utilizing simulation data to solve vision-based robotic manipulation tasks.<n>Using two domains--a robot arm and a humanoid--we demonstrate that simulation data can enhance real-world task performance by an average of 38%.
  arXiv  Detail & Related papers  (2025-03-31T17:39:38Z)
• Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization [22.234170426206987]
  We present a low-cost data generation pipeline that integrates physics-based simulation, human demonstrations, and model-based planning.<n>We validate the pipeline's effectiveness by training diffusion policies for challenging contact-rich manipulation tasks.<n>The trained policies are deployed zero-shot on hardware for bimanual iiwa arms, achieving high success rates with minimal human input.
  arXiv  Detail & Related papers  (2025-02-27T18:56:01Z)
• DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning [42.88605563822155]
  We present a large-scale automated data generation system that synthesizes trajectories from human demonstrations for humanoid robots with dexterous hands.<n>We generate 21K demos across these tasks from just 60 source human demos.<n>We also present a real-to-sim-to-real pipeline and deploy it on a real-world humanoid can sorting task.
  arXiv  Detail & Related papers  (2024-10-31T17:48:45Z)
• 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)

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.