SPARC (Spatial Annotations from Robot Demonstrations at Scale)
About This Dataset
SPARC (Spatial Annotations from Robot Demonstrations with Reliability Calibration) is a research-lab category framework and dataset developed by the Intuitive Robots Lab at the Karlsruhe Institute of Technology. It addresses the critical need for high-quality spatial data in robotics by providing an automated auto-labeling pipeline that generates structured annotations like bounding boxes and object trajectories from robot demonstrations. The dataset includes the Interaction-Aware Bench (IA-Bench), which features 1,700 human-annotated demonstrations across 12 diverse robot embodiments, including bimanual setups and Franka platforms. SPARC introduces a unique reliability calibration score to filter noisy labels, significantly improving object localization accuracy compared to standard detection baselines. This open-access resource is designed for researchers developing vision-language-action (VLA) models and grounded robot policies. By leveraging spatio-temporal physical cues, SPARC enables the scaling of training data for complex manipulation tasks in cluttered, real-world environments without requiring exhaustive manual review, thus bridging the gap between raw demonstrations and structured embodied intelligence.
Methodology
SPARC utilizes a risk-aware framework to automatically label robot demonstrations. It isolates manipulated objects using physical cues such as phase-aligned motion and 3D gripper proximity. A spatial filter is applied to suppress the robot's own geometry, ensuring that the annotations focus on the interaction. The core innovation is the Composite Annotation Reliability Score, which captures the likelihood of interaction and serves as a per-annotation estimate of correctness.
Collection
The dataset includes the Interaction-Aware Bench (IA-Bench), a novel benchmark consisting of 1,700 hand-annotated start and target locations of manipulated objects. These demonstrations span 12 diverse robot embodiments and various settings, including bimanual configurations. The data was collected to evaluate model accuracy in grounding the locations of interacted objects within spatio-temporal contexts.
Use Cases
SPARC is intended for training grounded robot policies, embodied foundation models, and vision-language-action (VLA) models. It is particularly effective for motion planning and hierarchical task composition. The reliability scores allow users to trade off annotation quality and dataset scale, making it suitable for training models that must operate in cluttered or visually ambiguous real-world scenes.
Tasks Covered
This dataset covers manipulation, pick-and-place, bimanual, long-horizon tasks across Franka, bimanual setups, 12 diverse embodiments robot platforms.
Access and Licensing
Citation
Intuitive Robots Research Team (2026). SPARC (Spatial Annotations from Robot Demonstrations at Scale). https://intuitive-robots.github.io/sparc-labeling/
Robot Embodiments
This dataset includes demonstrations collected using the following robot platforms.
- Franka
- bimanual setups
- 12 diverse embodiments
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Frequently Asked Questions
About Intuitive Robots Research Team
SPARC (Spatial Annotations from Robot Demonstrations at Scale) is a Research Lab dataset maintained by Intuitive Robots Research Team, released in 2026 under the null license. The dataset is restricted to non-commercial research use. It covers manipulation, pick-and-place, bimanual, long-horizon tasks using Franka, bimanual setups, 12 diverse embodiments robot platforms. The full dataset is freely accessible at https://intuitive-robots.github.io/sparc-labeling/.