Large-Scale Datasets in Robotic Manipulation
In recent years, the field of robot learning has undergone a significant paradigm shift, moving from small-scale, task-specific datasets toward large-scale, cross-embodiment data. To achieve stronger generalization and train universal robot foundation models, the community has introduced several milestone datasets by integrating data from various robotic platforms.
For instance, Open X-Embodiment aggregates hundreds of thousands of demonstration trajectories, aiming to cover diverse environments and tasks through sheer data volume. Research such as RT-1 and BridgeData V2 has further demonstrated that models trained on massive datasets can exhibit impressive zero-shot transfer capabilities, adapting to unseen instructions and scenarios. The success of these datasets is primarily attributed to their scale, which enables models to learn general visuomotor priors. This allows them to perform object manipulation and relocation tasks within unstructured, real-world settings.
High-Precision Datasets for Industrial Manipulation
Recent advancements in robot learning have been driven by the emergence of large-scale datasets collected across diverse environments. Foundation initiatives such as Open X-Embodiment, RT-1, and BridgeData V2 have aggregated extensive demonstrations to enable general-purpose robotic control. These datasets typically focus on unstructured settings, such as household or kitchen environments, where robots perform fundamental, short-horizon tasks like picking, placing, and object rearrangement.
To address the limitations of primitive skills, research has shifted towards long-horizon and contact-rich manipulation tasks. FurnitureBench has made progress in addressing long-horizon tasks. However, despite the extended temporal horizon, furniture assembly generally involves loose mating parts, lacking the tight tolerances of precision manufacturing.
To tackle high-precision challenges, recent benchmarks like REASSEMBLE and RH20T have adopted standardized industrial protocols, such as the NIST Task Board. These works emphasize that visual sensing alone is often insufficient for tasks like gear meshing or connector insertion, necessitating the use of high-frequency force-torque sensors and proprioception. While incorporating such modalities improves physical feedback, it introduces hardware dependencies and complicates data collection, often limiting dataset scale. For example, REASSEMBLE contains approximately 4,000 demonstrations.
In contrast, this work bridges the gap between large-scale generalist datasets and high-precision industrial needs. Vision-based policies can implicitly learn the contact dynamics required for tight-tolerance assembly when supported by substantial data scale and hardware diversity. To this end, the dataset comprises 5,000 trajectories covering a wide range of industrial task scenarios, collected across multiple robotic platforms including Franka, Realman, and LEJU. Furthermore, to enable rigorous evaluation of interaction quality, a subset of the data is equipped with tactile sensors, serving as a high-fidelity benchmark for fine-grained testing and validation.
PRISM Dataset
Precision and contact-rich Real-world Industrial Skill dataset with Multimodal sensing, or PRISM, is a dataset for contact-rich industrial manipulation.
Data Collection and Processing
Unlike most existing datasets that rely on a single teleoperation interface, demonstrations are collected using three complementary teleoperation methods: exoskeleton-based, tracker-based, and VR-based. This design not only broadens the coverage of human control styles and interaction behaviors, but also enables a controlled comparison of how the same task, collected under different teleoperation modalities, differs in data quality, including smoothness, precision, contact stability, and failure rates. It also enables analysis of how these differences translate into downstream learning performance.
The dataset includes hardware configurations, multimodal synchronization and calibration, and post-processing steps that ensure consistent, high-fidelity multimodal records across robots, end-effectors, and collection sessions.
Processing
After data collection, raw logs are processed into episode files with clean timestamps, calibrated sensor parameters, and metadata suitable for multimodal learning. Since different sensors run at different native rates, original timestamps are retained for all streams. Per-episode indexing is provided so that modalities can be aligned by timestamp during training, using methods such as nearest-neighbor matching or interpolation.
Incomplete episodes are removed, and leading and trailing idle segments are trimmed based on task start and end markers and operator annotations.
Unified Episode Packaging
All processed data is serialized into a common schema shared across the three platforms. The schema includes:
- Robot states and actions, including joint angles and torques, end-effector pose, and gripper states
- End-effector wrench, when available
- Multi-view RGB-D observations
- Visuotactile imagery, when available
- Calibration parameters
- Timestamps
- Platform identifiers
- Task identifiers
- Outcome labels
- Volunteer-provided ratings
This standardized composition enables training and evaluation across heterogeneous robots, end-effectors, and teleoperation interfaces while maintaining consistent multimodal alignment.
Experimental Setup
Platform
Experiments are conducted on a bimanual Realman robot with a three-degree-of-freedom waist, matching the embodiment used during data collection. Each arm is equipped with a 3D-printed parallel-jaw gripper, and visual observations are captured using Intel RealSense D515 RGB-D cameras.
The workspace layout, background, and tabletop settings are kept consistent with the data collection environment to ensure that evaluation reflects the same sensing configuration and physical constraints encountered in the dataset.
Procedure
Policies are evaluated on three representative tasks collected in the industrial workcell:
- Electronic component plug and unplug
- Packaging a vernier caliper
- Conveyor-based sorting
These tasks reflect three common categories in industrial manipulation: precise contact-rich operations, product packaging, and dynamic object sorting, respectively.
For each task, 200 demonstrations are collected. Each demonstration contains synchronized RGB-D observations, action sequences, and six-axis end-effector force and torque measurements.
Frequently Asked Questions
What is the PRISM dataset designed for? PRISM is designed for contact-rich industrial manipulation and multimodal robot learning.
How many trajectories does PRISM contain? The dataset comprises 5,000 trajectories covering a wide range of industrial task scenarios.
Which teleoperation methods are used for data collection? Demonstrations are collected using exoskeleton-based, tracker-based, and VR-based teleoperation methods.
What tasks are used for policy evaluation? Policies are evaluated on electronic component plug and unplug, vernier caliper packaging, and conveyor-based sorting.