IM-ENGINE: Image Editing for Embodied Data Generation

• URL: http://arxiv.org/abs/2609.06279v1
• Date: Sat, 05 Sep 2026 22:21:34 GMT
• Title: IM-ENGINE: Image Editing for Embodied Data Generation
• Abstract summary: We present IM-ENGINE, a simulator-grounded pipeline that uses image editing as an intermediate representation for embodied data generation.<n>We instantiate the pipeline for dexterous grasp synthesis and goal-state generation.<n>This combination of generative semantic priors and simulator grounding enables scalable task-relevant supervision for robot learning.
• Score: 32.31246464559414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning-based manipulation requires supervision that is both semantically meaningful and physically executable, but current data pipelines often provide only one of these properties. Human demonstrations capture intent but are costly to collect and constrained by the human-robot embodiment gap, while simulation can scale data generation but often under-specifies functional behavior. We present IM-ENGINE, a simulator-grounded pipeline that uses image editing as an intermediate representation for embodied data generation. Given a rendered scene with known geometry, depth, segmentation, and camera parameters, IM-ENGINE edits the image to inject task-relevant semantics, recovers explicit 3D state using simulator priors and an unchanged anchor object, refines the state in physics, and converts it into robot-executable supervision. We instantiate the pipeline for dexterous grasp synthesis and goal-state generation. For grasping, IM-ENGINE generates a human grasp in image space, recovers the hand-object interaction, retargets it to a robot hand, and refines it into physically validated robot grasps. For goal generation, it edits a rendered scene into a desired outcome, recovers the target-object pose, and refines it into physically valid, semantically meaningful goals and trajectories. This combination of generative semantic priors and simulator grounding enables scalable task-relevant supervision for robot learning.

Related papers

• RGB-D Video Generation for Improving Human-to-Robot Object Handover Prediction [51.76737991200891]
  Human-to-robot (H2R) object handover is a fundamental capability for human-robot collaboration, yet progress is hindered by the scarcity of large-scale, human-centric datasets.<n>We introduce Hand2Bot, an RGB-D video dataset that provides rich contextual information such as body posture and facial expressions.<n>We propose PassGen, a generative pipeline that synthesizes realistic handover sequences while ensuring hand-object consistency.
  arXiv  Detail & Related papers  (2026-08-13T09:55:59Z)
• GenHOI: Contact-Aware Humanoid-Object Interaction by Imitating Generated Videos without Task-Specific Training [20.414478780328437]
  Existing methods often require time-consuming task-specific policy training or rely on rigid trajectory replay.<n>We present textitGenHOI, a framework that enables humanoid robots to perform diverse object-interaction tasks in a zero-shot manner.<n>We validate the proposed framework in extensive simulation and real-world experiments across diverse object-interaction tasks.
  arXiv  Detail & Related papers  (2026-06-11T07:31:05Z)
• 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)
• GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors [113.71148915419246]
  We present GRAIL, a digital generation pipeline that composes 3D assets, simulator-ready scenes, and priors from video foundation models (VFMs) to synthesize interactions without rebuilding physical environments or teleoperating the robot.<n>GRAIL starts from fully specified 3D configurations in which object geometry, camera parameters, metric scale, environment depth, and a robot-proportioned character are known before video generation and reused during reconstruction.<n>We retarget the recovered motions to a humanoid robot and train complementary task-general morphology trackers.<n>GRAIL produces over 20,000 sequences spanning pick-up, object manipulation, sitting
  arXiv  Detail & Related papers  (2026-06-03T17:57:45Z)
• 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)
• FlowHOI: Flow-based Semantics-Grounded Generation of Hand-Object Interactions for Dexterous Robot Manipulation [23.19464039872024]
  FlowHOI is a flow-matching framework that generates temporally coherent HOI sequences.<n>We show that FlowHOI achieves the highest action recognition accuracy and a 1.7$times$ higher physics simulation success rate.
  arXiv  Detail & Related papers  (2026-02-13T20:46:08Z)
• SAGE: Scalable Agentic 3D Scene Generation for Embodied AI [67.43935343696982]
  Existing scene-generation systems often rely on rule-based or task-specific pipelines, yielding artifacts and physically invalid scenes.<n>We present SAGE, an agentic framework that, given a user-specified embodied task, understands the intent and automatically generates simulation-ready environments at scale.<n>The resulting environments are realistic, diverse, and directly deployable in modern simulators for policy training.
  arXiv  Detail & Related papers  (2026-02-10T18:59:55Z)
• Sim2Real Transfer for Vision-Based Grasp Verification [7.9471205712560264]
  We present a vision-based approach for grasp verification to determine whether the robotic gripper has successfully grasped an object.<n>Our method employs a two-stage architecture; first YOLO-based object detection model to detect and locate the robot's gripper.<n>To address the limitations of real-world data capture, we introduce HSR-Grasp Synth, a synthetic dataset designed to simulate diverse grasping scenarios.
  arXiv  Detail & Related papers  (2025-05-05T22:04:12Z)
• SIGHT: Synthesizing Image-Text Conditioned and Geometry-Guided 3D Hand-Object Trajectories [124.24041272390954]
  Modeling hand-object interaction priors holds significant potential to advance robotic and embodied AI systems.<n>We introduce SIGHT, a novel task focused on generating realistic and physically plausible 3D hand-object interaction trajectories from a single image.<n>We propose SIGHT-Fusion, a novel diffusion-based image-text conditioned generative model that tackles this task by retrieving the most similar 3D object mesh from a database.
  arXiv  Detail & Related papers  (2025-03-28T20:53:20Z)
• Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models [53.22792173053473]
  We introduce an interactive robotic manipulation framework called Polaris. Polaris integrates perception and interaction by utilizing GPT-4 alongside grounded vision models. We propose a novel Synthetic-to-Real (Syn2Real) pose estimation pipeline.
  arXiv  Detail & Related papers  (2024-08-15T06:40:38Z)

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.