RoboCousin: Build Your Own Simulation Playground for Robust Bimanual Robotic Manipulation

• URL: http://arxiv.org/abs/2609.08339v1
• Date: Tue, 08 Sep 2026 07:13:07 GMT
• Title: RoboCousin: Build Your Own Simulation Playground for Robust Bimanual Robotic Manipulation
• Abstract summary: Bimanual manipulation policies require large and diverse training datasets.<n>Existing pipelines typically operate within closed asset libraries and predefined scenes.<n>We present RoboCousin, a simulation-based data-generation platform.
• Score: 16.581929671943325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bimanual manipulation policies require large and diverse training datasets, yet collecting demonstrations on physical robots is expensive and difficult to scale. Simulation can generate data efficiently, but existing pipelines typically operate within closed asset libraries and predefined scenes: adding a newly observed object or environment still requires substantial effort to reconstruct geometry, specify physical and semantic properties, annotate interactions, and integrate the result into executable tasks. We present RoboCousin, an extensible simulation-based data-generation platform that turns user-provided observations into reusable assets, scenes, and expert trajectories for bimanual manipulation. Built on RoboTwin~2.0, RoboCousin converts object images into simulation-ready assets with visual and collision geometry, semantic and physical metadata, and automatically generated grasp-contact candidates. It further constructs digital cousins that vary compatible objects, backgrounds, layouts, and language instructions while preserving task-relevant affordances and spatial relations. The same asset system supports tabletop and room-level scene construction, with collision-aware base control for interaction beyond a fixed workspace. We release RoboCousin-OBD, containing more than 3,000 annotated object instances and 50 background environments, and use RoboCousin to generate over one million expert trajectories across 50 tasks. Simulation and real-robot experiments show that the automatically generated interaction annotations are comparable to curated annotations, generated assets provide effective sim-to-real supervision, and tabletop cousins can improve transfer beyond training on a single reconstructed scene. RoboCousin therefore provides a practical path for expanding both the scale and coverage of synthetic bimanual manipulation data.

Related papers

• TableVerse: A Large-scale Tabletop Dataset with Real-world Grounded Layouts for Generalizable Manipulation [11.161552039605253]
  TableVerse is a fully automated Real2Sim pipeline that processes internet media into high-fidelity, simulation-ready tabletop environments.<n>Our framework seamlessly processes unscripted internet media into high-fidelity, simulation-ready tabletop environments with accurate metric scales, authentic topologies, and verified mechanical stability.<n>By capturing diverse asset compositions, realistic spatial distributions, and high-quality demonstrations, TableVerse-100K establishes a highly scalable and high-fidelity data foundation.
  arXiv  Detail & Related papers  (2026-07-23T08:02:53Z)
• RoboSnap: One-Shot Real-to-Sim Scene Generation for Generalizable Robot Learning and Evaluation [55.07260698542592]
  We present RoboSnap, a real-to-sim framework that turns a single RGB image into a simulation-ready scene.<n>Experiments on DROID scenes and real-robot tasks show that RoboSnap achieves reliable trajectory replay in the recovered scenes.<n>To further support real-to-sim research, we introduce DROID-Sim, a real-to-sim companion dataset constructed from 564 real-world scenes in DROID.
  arXiv  Detail & Related papers  (2026-07-07T18:19:10Z)
• GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments [63.222920701596564]
  Training embodied agents in the real world requires skilled operators and expensive hardware.<n> Simulation environments offer a compelling alternative by enabling large-scale, cost-effective data augmentation.<n>Fastly constructing high-fidelity simulation scenes with a minimal sim-to-real gap has become a critical objective in robot learning.
  arXiv  Detail & Related papers  (2026-06-16T05:00:42Z)
• AnnotateAnything: Automatic Annotation of 3D Assets for Robot Manipulation [37.64142499431465]
  AnnotateAnything converts passive 3D assets into manipulation-ready assets with structured, diverse, and executable manipulation labels.<n>A fully automatic and massively parallel physics pipeline grounds these priors in each asset's geometry and physical constraints.<n>This pipeline produces diverse and executable annotations, including grasp poses, dexterous contacts, articulation waypoints, insertion directions, hanging affordances, and navigation targets.
  arXiv  Detail & Related papers  (2026-06-16T03:00:58Z)
• V-Dreamer: Automating Robotic Simulation and Trajectory Synthesis via Video Generation Priors [30.872127145725187]
  V-Dreamer is a fully automated framework that generates open-vocabulary, simulation-ready manipulation environments.<n>V-Dreamer employs a novel generative pipeline that constructs physically grounded 3D scenes.<n>We show that our policies robustly generalize to unseen objects in simulation and achieve effective sim-to-real transfer.
  arXiv  Detail & Related papers  (2026-03-19T12:00:04Z)
• Articulated 3D Scene Graphs for Open-World Mobile Manipulation [55.97942733699124]
  We present MoMa-SG, a framework for building semantic-kinematic 3D scene graphs of articulated scenes.<n>We estimate articulation models using a novel unified twist estimation formulation.<n>We also introduce the novel Arti4D-Semantic dataset.
  arXiv  Detail & Related papers  (2026-02-18T10:40:35Z)
• SceneSmith: Agentic Generation of Simulation-Ready Indoor Scenes [19.995619927680476]
  SceneSmith builds environments from architectural layout to natural furniture population.<n>SceneSmith generates more objects than prior methods, with 2% interobject collisions and 96% of objects remaining stable under physics simulation.<n>SceneSmith environments can be used in an end-to-end pipeline for automatic policy evaluation.
  arXiv  Detail & Related papers  (2026-02-09T19:56:04Z)
• MobileManiBench: Simplifying Model Verification for Mobile Manipulation [70.30578259859512]
  MobileManiBench is a large-scale benchmark for mobile-based robotic manipulation.<n>MobileManiBench features 2 mobile platforms (parallel-gripper and dexterous-hand robots), 2 synchronized cameras (head and right wrist), 630 objects in 20 categories, 5 skills (open, close, pull, push, pick) with over 100 tasks performed in 100 realistic scenes, yielding 300K trajectories.
  arXiv  Detail & Related papers  (2026-02-05T02:49:52Z)
• ArtiWorld: LLM-Driven Articulation of 3D Objects in Scenes [43.19849355456126]
  ArtiWorld is a scene-aware pipeline that localizes candidate articulable objects from textual scene descriptions.<n>At the core of this pipeline is Arti4URDF, which leverages 3D point cloud and prior knowledge of a large language model.<n>We evaluate ArtiWorld at three levels: 3D simulated objects, full 3D simulated scenes, and real-world scan scenes.
  arXiv  Detail & Related papers  (2025-11-17T04:59:21Z)
• URDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model [76.08429266631823]
  We propose an end-to-end automatic reconstruction framework based on a 3D multimodal large language model (MLLM)<n>URDF-Anything utilizes an autoregressive prediction framework based on point-cloud and text multimodal input to jointly optimize geometric segmentation and kinematic parameter prediction.<n> Experiments on both simulated and real-world datasets demonstrate that our method significantly outperforms existing approaches.
  arXiv  Detail & Related papers  (2025-11-02T13:45:51Z)
• 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)

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.