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RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation

Kun Wu, Chengkai Hou, Jiaming Liu, Zhengping Che, Xiaozhu Ju, Zhuqin Yang, Meng Li, Yinuo Zhao, Zhiyuan Xu, Guang Yang, Shichao Fan, Xinhua Wang, Fei Liao, Zhen Zhao, Guangyu Li, Zhao Jin, Lecheng Wang, Jilei Mao, Ning Liu, Pei Ren, Qiang Zhang, Yaoxu Lyu, Mengzhen Liu, Jingyang He, Yulin Luo, Zeyu Gao, Chenxuan Li, Chenyang Gu, Yankai Fu, Di Wu, Xingyu Wang, Sixiang Chen, Zhenyu Wang, Pengju An, Siyuan Qian, Shanghang Zhang, Jian Tang

arXiv:2412.13877v3cs.ROcs.AI

TL;DR

General-purpose robotic models need rich, diverse data, but robotic trajectories are costly and difficult to collect consistently. RoboMIND addresses this gap with a standardized, multi-embodiment teleoperation dataset augmented by failure cases, language annotations, simulation, and benchmark experiments. The dataset supports imitation-learning and VLA models, while its current scope is limited by simple backgrounds and missing mobile-manipulation data.

  • Problem

    Robotic training data are difficult to collect at scale because controlled environments must record robot joints and end-effector information, while generalizable models require diverse scenes, tasks, and robot types.

  • Method

    RoboMIND collects standardized teleoperation trajectories from four robot embodiments and adds multimodal observations, failure cases, fine-grained language annotations, simulation, and VLA pretraining.

  • Results

    Experiments show that RoboMIND works with multiple single-task imitation-learning algorithms and VLA models, with full-dataset VLA pretraining improving task performance across multiple robot types.

  • Takeaways & Limitations

    RoboMIND provides a large-scale resource for studying cross-embodiment generalization, downstream adaptation, and visual- and task-level data augmentation.

  • Takeaways & Limitations

    RoboMIND currently uses relatively simple background environments and lacks data from mobile manipulation scenarios.

Abstract

from arXiv · show

In this paper, we introduce RoboMIND (Multi-embodiment Intelligence Normative Data for Robot Manipulation), a dataset containing 107k demonstration trajectories across 479 diverse tasks involving 96 object classes. RoboMIND is collected through human teleoperation and encompasses comprehensive robotic-related information, including multi-view observations, proprioceptive robot state information, and linguistic task descriptions. To ensure data consistency and reliability for imitation learning, RoboMIND is built on a unified data collection platform and a standardized protocol, covering four distinct robotic embodiments: the Franka Emika Panda, the UR5e, the AgileX dual-arm robot, and a humanoid robot with dual dexterous hands. Our dataset also includes 5k real-world failure demonstrations, each accompanied by detailed causes, enabling failure reflection and correction during policy learning. Additionally, we created a digital twin environment in the Isaac Sim simulator, replicating the real-world tasks and assets, which facilitates the low-cost collection of additional training data and enables efficient evaluation. To demonstrate the quality and diversity of our dataset, we conducted extensive experiments using various imitation learning methods for single-task settings and state-of-the-art Vision-Language-Action (VLA) models for multi-task scenarios. By leveraging RoboMIND, the VLA models achieved high manipulation success rates and demonstrated strong generalization capabilities. To the best of our knowledge, RoboMIND is the largest multi-embodiment teleoperation dataset collected on a unified platform, providing large-scale and high-quality robotic training data. Our project is at https://x-humanoid-robomind.github.io/.

I. INTRODUCTION

RoboMIND addresses the difficulty of collecting rich, diverse robotic training data by providing a large, standardized, multi-embodiment manipulation dataset. It combines successful and failed demonstrations with broad task, object, sensor, and language coverage, and experiments show utility across imitation-learning and VLA models.

  • Dataset motivation and contribution: 107k trajectories cover 479 tasks and 96 object classes across four robotic embodiments in a unified collection setting.The dataset includes Franka Emika Panda, Tien Kung, AgileX Cobot Magic V2.0, and UR5e robots.
  • Dataset motivation and contribution: RoboMIND uses standardized collection procedures to reduce variability and improve consistency and reliability for policy learning.The protocol captures data under similar conditions and supports validation and reproducibility.
  • Dataset motivation and contribution: The dataset contains multi-view RGB-D observations, robot and end-effector state information, and linguistic task descriptions collected through teleoperation.These modalities are intended to support learning complex manipulation tasks.
  • Dataset motivation and contribution: 5k real-world failure trajectories are documented alongside successful demonstrations, enabling models to learn from annotated failure causes.The paper connects these data to failure recovery, task planning, and related robot-learning applications.
  • Empirical validation: Experiments show that RoboMIND supports several single-task imitation-learning algorithms and adapts successfully to VLA models across real-world scenarios.Pretraining VLA models on the full dataset improves task performance across multiple robot types.

II. RELATED WORK

The paper situates RoboMIND within robotic manipulation, simulator-based data collection, and large-scale policy learning, then describes its unified teleoperation and storage platform. The platform collects multimodal trajectories from four embodiments using embodiment-specific interfaces.

  • Related work context: Imitation-learning policies acquire stable manipulation skills by imitating expert demonstrations, complementing state-based and visual-action policy approaches.The related work also notes diffusion-based methods as a recent direction.
  • Related work context: Simulators provide virtual environments that can reduce the cost and time of collecting data for complex and long-horizon robotic tasks.RoboMIND contributes a real-world dataset and digital-twin simulator for policy learning.
  • Related work context: Large and diverse datasets such as Open X-Embodiment support policy pretraining, improving fine-tuning efficiency and generalization capability.RoboMIND is positioned as another large-scale pretraining and real-world fine-tuning resource.
  • RoboMIND platform: RoboMIND collects 107k trajectories across four embodiments, using an intelligent platform for collection, filtering, processing, and standardized H5 storage.The stored data combine visual observations with proprioceptive robot information.
  • RoboMIND platform: Single-arm, dual-arm, and humanoid robots use different teleoperation interfaces, including 3D-printed controllers, bilateral auxiliary-arm control, and motion-capture mapping.The humanoid platform includes a 42-degree-of-freedom body, dexterous hands, and multiple depth cameras.

B. Data Preprocessing and Classification

RoboMIND preprocesses trajectories through operator-support measures, multi-stage quality inspection, and task-centric classification. The resulting task definitions organize data around embodiment, skill, objects, and scene context for fine-grained analysis.

  • Quality assurance: Real-time teleoperation can introduce errors from fatigue, habits, distractions, and external disruptions, so the pipeline uses operator rest measures and comprehensive quality checks.The process is designed to improve the reliability of collected data.
  • Quality assurance: Quality assurance proceeds through initial inspection, detailed frame-level or slow-motion review, and timestamped logging and categorization of non-compliant data.The inspections check technical problems and predefined trajectory-quality conditions.
  • Task classification: Each task is defined by its robotic embodiment, manipulation skill, involved objects, and detailed scene description.Scene descriptions include positions, spatial relationships, environmental constraints, and interfering elements.
  • Quality assurance: Eight criteria identify issues including unnecessary contact, jerky motion, repeated grasping, arm shaking, pre-grasp collisions, image distortion, failed placement, and an out-of-frame gripper.Failure examples are annotated from videos with visual markers indicating the causes.
  • Task classification: The task-centric framework supports systematic data collection and fine-grained analysis of manipulation capabilities across scenarios and tasks.Task names serve as the fundamental unit for dataset classification.

C. Data Annotation

RoboMIND supplements visual and proprioceptive trajectories with semantic language annotations, especially for long-horizon tasks. The annotation pipeline segments movements, generates descriptions, and manually refines them for accuracy and temporal consistency.

  • Language annotation: Detailed linguistic descriptions are provided for collection tasks to supply semantic information for training VLA models.Uniform task descriptions may be insufficient for long-horizon trajectories.
  • Language annotation: 10k successful robot motion trajectories receive fine-grained language annotations for individual movements and operational steps.The annotations are intended to capture relevant context throughout trajectories.
  • Annotation refinement: Gemini initially generates segment descriptions, which are then manually refined for object identity, critical actions, operational details, temporal granularity, and temporal logic.These checks target precision and reliability in the final annotations.
  • Annotation refinement: The example annotation divides an apple-placement video into six segments and produces descriptions for the key actions.The paper reports that the scheme can segment key actions accurately and provide precise descriptions.

IV. DATASET ANALYSIS

RoboMIND combines heterogeneous robots, varied task horizons, skills, and objects under a unified dataset design to support manipulation-policy generalization.

  • 107k trajectories span 4 embodiments, 479 tasks, 96 object classes, and 38 skills.
  • Heterogeneous Embodiments: Four embodiments include Franka and UR5e single-arm robots, AgileX dual-arm robots, and the Tien Kung humanoid with dexterous hands.
  • Tasks with Various Horizon Lengths: Franka and UR tasks typically have fewer than 200 time steps, whereas Tien Kung and AgileX tasks exceed 500 steps and target longer-horizon learning.
  • Tasks with Various Horizon Lengths: An AgileX example chains picking, handoff, and placement across both arms, illustrating complex coordination and long-horizon manipulation.
  • Task Classification: Tasks are categorized by summarized manipulation skills across actions, objects, and trajectory horizons, including articulated, coordination, and basic manipulations.
  • Diverse Objects: The dataset covers over 96 object categories across domestic, industrial, kitchen, office, and retail scenarios.

B. Qualitative Analysis

RoboMIND emphasizes standardized real-world data collection and explicitly documented failures, extending its value beyond successful demonstrations.

  • Standardized Settings: Standardized settings distinguish RoboMIND from Open X-Embodiment by making data collection more consistent across the dataset.
  • Failure Case Demonstrations: 5k failure trajectories record unsuccessful human-operated or robot-executed tasks for qualitative analysis and downstream learning.
  • Failure Case Demonstrations: Examples include plate-placement misalignment with slipping and an unexpectedly opening gripper that drops a carrot.
  • Failure Case Demonstrations: Failed trajectories are identified, categorized, and documented during quality inspection.
  • Failure Case Demonstrations: The failure data supports failure detection and recovery, data augmentation, and reinforcement-learning reward generation.

V. ANALYZING ROBOT LEARNING WITH ROBOMIND

The learning analysis evaluates RoboMIND across multiple embodiments, camera configurations, and representative manipulation tasks to characterize its benchmarking scope.

  • Real-world Robotic Setup: The real-world platforms use embodiment-specific visual setups, including multi-view cameras for Franka and built-in or external cameras for other robots.
  • Representative Tasks: RoboMIND contributes 479 distinct manipulation tasks collected across four robotic embodiments.
  • Representative Tasks: Representative tasks include curved-path drawer closing, rotating and placing an upright cup, extracting corn with coordinated arms, and closing a stacked drawer.

B. Single-task Imitation Learning Models

Single-task experiments train and deploy three imitation-learning methods across 45 tasks, using repeated real-world success-rate evaluations across four robots.

  • Experimental Task Design: 45 tasks were selected across Franka, Tien Kung, AgileX, and UR5e, with 15, 10, 15, and 5 tasks respectively.
  • Training and Evaluation Setup: ACT, Diffusion Policy, and BAKU were trained from scratch for each dataset and evaluated through direct real-world deployment.
  • Training and Evaluation Setup: Each model was tested ten times per task, with success or failure and failure reasons recorded.
  • Experimental Results: 55.3% average success on AgileX made ACT stronger there than on Franka at 30.7%, UR5e at 38.0%, and Tien Kung at 34.0%.
  • Experimental Results: ACT reached 60% success on the HR-CloseDrawerLowerCabinet task.
  • Experimental Results: Diffusion Policy outperformed ACT on several Franka and Tien Kung tasks, while BAKU showed lower success rates across most tasks.
  • Experimental Results: The reported BAKU gap is attributed to hyper-parameters optimized for simulation rather than the evaluated real-world platforms.

C. Vision-Language-Action Large Models

RoboMIND demonstrations were used to fine-tune VLA models across multiple robot embodiments and tasks. Full-dataset pretraining and expert multitask fine-tuning substantially improved performance, especially for dual-arm and humanoid manipulation.

  • Training and Evaluation Setup: RoboMIND fine-tuning evaluated OpenVLA, RDT-1B, and CrossFormer on diverse real-world tasks using multitask demonstrations.Each task-model experiment used ten trials; OpenVLA was evaluated only on the Franka single-arm robot because it supports one end effector.
  • Experimental Results: RDT-1B demonstrated significantly higher task performance than CrossFormer and OpenVLA across multiple robot embodiments.The improvement was especially notable for dual-arm tasks, while RDT-1B remained comparable on straightforward tasks such as FR-PlaceBreadPlate and FR-SlideCloseDrawer.
  • Experimental Results: CrossFormer improved on single-arm and humanoid tasks after RoboMIND fine-tuning, whereas it had no success on those tasks without fine-tuning.
  • Experimental Results: Full RoboMIND pretraining followed by expert multitask fine-tuning significantly improved VLA success rates across varied robot tasks.For CrossFormer, dual-arm tasks changed from unsuccessful completion to nearly every test succeeding on AX-TakePotato, AX-PutPepper, and AX-AppleBluePlate; HR-PressDownToaster reached 100%.
  • Ablation Studies on Humanoid Data: Ablation experiments examined whether humanoid data improves policy learning for other robots, using RDT-1B pretrained with and without that data.

E. Generalization of VLA Large Models

The VLA generalization evaluation tested unseen objects and backgrounds, while failure analysis identified recurring causes across embodiments. Models generalized better to object variations than to unseen backgrounds.

  • Generalization Evaluation: OpenVLA, RDT-1B, and CrossFormer were evaluated on FR-PlaceBreadPlate with unseen backgrounds and substituted objects across ten trials per experiment.The objects were an apple, banana, and corn, and the backgrounds were three unseen tablecloth patterns.
  • Experimental Results: RDT-1B and CrossFormer generalized well to several substituted objects, especially bananas resembling bread-like training objects.All three models performed relatively poorly when generalizing across unseen backgrounds.
  • Failure Analysis: Nine predefined categories recorded whether failures involved positioning, gripping, object handling, returning home, collision recovery, or speed.
  • Failure Analysis: 48% of humanoid ACT failures were attributed to inaccurate positioning, the most common failure reason across embodiments.Cannot Close Gripper and Object Drop were also significant contributors, partly because gripper actions typically use few frames.

G. Real and Simulation Data

RoboMIND’s digital twin enabled joint real-and-simulation training and sim-real evaluation. More simulation data improved success in both environments, but simulation-only training transferred poorly to the real world for a contact-rich task.

  • Co-training with Real and Simulation Data: A digital twin simulation environment mirrored the real FR-UprightBlueCup setup for experiments combining real-world and simulation data.The task requires rotating the arm nearly 90 degrees, inserting the gripper horizontally, and restoring an overturned cup.
  • Co-training with Real and Simulation Data: Increasing the simulation-data proportion improved success rates in both real-world and simulation environments.
  • Co-training with Real and Simulation Data: 90% success in simulation from 100 real-world plus 500 simulation trajectories contrasted with 10% real-world success using simulation data alone.Simulation-only failures primarily involved the cup slipping during rotation because the gripper did not close sufficiently.
  • Performance Correlations between Real and Simulation Environments: Simulation and real-world test results showed positive performance correlations for ACT and Diffusion Policy across five tasks.

VI. DISCUSSION AND FUTURE WORK

The discussion presents RoboMIND as a broad, standardized resource for multi-embodiment robot manipulation and evaluates its usefulness for model training and generalization. It also identifies simple backgrounds and missing mobile-manipulation data as current limitations.

  • Discussion: RoboMIND contains four embodiments, 107k demonstrations, 479 tasks, 96 objects, and 38 skills collected through a quality-assured data platform.
  • Discussion: Quantitative analyses cover heterogeneous embodiments, episode lengths, task categories, and objects across domestic, industrial, kitchen, office, and retail scenarios.The paper also qualitatively compares RoboMIND with Open X-Embodiment on uniform settings, multiple viewpoints, and embodiment diversity.
  • Discussion and Future Work: Experiments indicate that current imitation-learning algorithms need more accurate positioning and precise control, particularly for long-horizon tasks.The authors identify cross-embodiment generalization, downstream adaptation, and visual- and task-level data augmentation as potential uses of RoboMIND.
  • Discussion and Future Work: RoboMIND is being expanded through standardized collection and quality-assurance procedures as a ready-to-use dataset for embodied AI research.
  • Limitations: RoboMIND currently has relatively simple backgrounds and lacks mobile-manipulation data.The authors plan to add more complex backgrounds and mobile-manipulation tasks.
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