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

Abstract: 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/.

• The paper introduces RoboMIND, a benchmark dataset with 55k real-world trajectories across 279 tasks from four distinct robotic embodiments.
• It employs a standardized human teleoperation method to capture multimodal sensory data, ensuring consistency and natural action patterns.
• Experiments show high success in imitation learning, highlighting key failure cases that inform future refinements in robotic manipulation models.

An Examination of RoboMIND: Multi-Embodiment Intelligence Normative Data for Robot Manipulation

The paper "RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation" introduces RoboMIND, a comprehensive dataset designed to address the need for diverse and large-scale robot manipulation data. The dataset features a substantial 55k real-world demonstration trajectories spanning 279 tasks and 61 distinct object classes, using four different types of robotic embodiments: Franka Emika Panda, UR-5e, AgileX dual-arm robot, and Tien Kung humanoid robot. This work represents a significant effort in standardizing the data collection process for robotic manipulation in heterogeneous environments, providing a foundation for developing robust and generalizable robotic manipulation policies.

Dataset Construction and Characteristics

The RoboMIND dataset is collected through human teleoperation, ensuring that the manipulation actions mirror natural human behavior. This approach is instrumental in capturing realistic interaction patterns and strategies. The dataset includes multimodal sensory data such as multi-view RGB-D images, proprioceptive robot state information, end-effector details, and linguistic task descriptions to enhance its utility in learning complex robotic tasks.

A distinct attribute of RoboMIND is its standardized data collection framework, which is crucial for achieving consistency and reliability across trajectories from different robot embodiments. This standardized approach is particularly beneficial for policy learning, as it reduces the noise and variability inherent in datasets sourced from non-uniform environments.

Analytical Insights

The authors conduct a thorough quantitative and qualitative analysis of RoboMIND, shedding light on various dimensions such as task diversity, object complexity, and skill coverage. The dataset encompasses a mix of articulated, coordination, basic manipulation, object interaction, precision, and scene understanding tasks, thereby challenging the current models' ability to generalize across different scenarios. The inclusion of a digital twin environment within the Isaac Sim simulator further supports low-cost data collection and facilitates efficient evaluation, bridging the gap between real-world and simulation tasks.

Quantitative results focus on the proportions of tasks across different skill categories and embodiments, revealing a broad and balanced range that enhances research applicability. Particularly, the dual-arm and dexterous hand trajectories introduce complexity that is often missing in existing datasets, making RoboMIND a valuable resource for multi-task and long-horizon manipulation learning.

Implications for Robotic Learning

RoboMIND is tested with state-of-the-art imitation learning methods, demonstrating a high manipulation success rate and significant potential for improving model generalization. The experimental outcomes showcase that RoboMIND's diverse, high-quality data can effectively supplement training for both single-task and multi-task learning models, highlighting its utility as a benchmark for evaluating robotic manipulation algorithms across varying levels of complexity.

Furthermore, the failure case analysis in the experiments provides insights into the prevalent shortcomings in current robotic training approaches, such as positioning inaccuracies and object detachment in task execution. This analysis not only emphasizes the critical need for precise data collection practices but also provides directions for refining data-driven models to enhance their accuracy and robustness.

Future Directions

The development of RoboMIND opens pathways for extensive research in robotic manipulation, particularly in improving cross-embodiment task generalization and exploring data augmentation techniques to enhance visual and task learning capabilities. Further augmentations to the dataset could include mobile manipulations and high-level planning annotations, boosting its applicability in dynamic environments.

In conclusion, RoboMIND marks a commendable advancement in robotic manipulation datasets. Its emphasis on standardized, diverse, and large-scale data collection sets a precedent for future datasets and has the potential to significantly accelerate the progress in creating general-purpose and adaptable robotic systems. The provision of such a dataset is timely, given the burgeoning interest in the field of embodied AI, and provides a robust platform for researchers to push the boundaries of what is possible in robotic manipulation.