Egocentric Video Capture
Record first-person view video in RGB format from the operator's perspective to generate visual context required for robotics, embodied AI, and Vision-Language-Action (VLA) model training.
Product · RebocamRebocam: multimodal
Rebocam: multimodal
data capture for Physical AI
Collect egocentric, training-ready data directly from real-world settings. Rebocam unifies video, depth, audio, and IMU streams into structured datasets built for robotics, embodied AI, and Vision-Language-Action (VLA) model development.
Egocentric Data CollectionMultimodal SynchronizationVLA-ReadyS3 Pipeline Delivery
Rebocam
Production-readyPurpose-built for Physical AI data collection. Deploy across homes, manufacturing units, health care facilities, warehouses, and other operational environments to capture first-person, multimodal datasets at scale.
4K/30fpsEgocentric Capture
First-person video designed to match robot deployment viewpoints.
Frame-AccurateSensor Sync
Video, depth maps, audio, and IMU synchronized across every recording session.
50+ CitiesDeployment Network
Collect data globally through Robgence's distributed operator infrastructure.
S3-ReadyDataset Delivery
Structured outputs compatible with OpenVLA, pi0, and custom robotics training pipelines.
What it capturesCapture every signal
Capture every signal
that matters
Multimodal Sensor Streams
Combine RGB, depth, audio, IMU, and optional force-torque and tactile data into one consistent time-synchronized multi-modal sensor stream, preserving consistency across modalities.
Annotation-Ready Data
Structure captured sessions with timestamps, session metadata, and modality alignment for downstream labeling workflows, enabling action annotation, object interaction tracking, contact event labeling, and behavioral analysis.
Real-Time Data Validation
Monitor capture quality during collection to detect missing modalities, timestamp drift, synchronization issues, and incomplete sessions before deployment.
Automated Cloud Delivery
Upload and organize datasets directly into centralized cloud storage pipelines, reducing operational overhead and accelerating access for research teams.
Training-Ready Outputs
Export structured datasets compatible with robotics training, embodied AI research, imitation learning workflows, and VLA model development, including OpenVLA- and pi0-ready formats where applicable.
How it works
Deploy · collect · deliver
Rebocam eliminates the operational complexity of multimodal data collection. From deployment and sensor synchronization to validation and dataset delivery, every step is designed to produce training-ready robotics data with minimal setup overhead.
01
Deploy
Attach Rebocam to an operator, workstation, robotic cell, or test environment. Pre-configured hardware, sensor calibration, and fast-mount accessories enable rapid deployment without custom integration or synchronization workflows.
02
Collect
Capture RGB, depth, audio, IMU, and optional force-torque streams via a unified data capture pipeline. Real-time validation monitors modality availability, timestamp alignment, motion blur, lighting conditions, and recording integrity during collection.
03
Deliver
Automatically package, validate, and transfer recordings into structured datasets. Export normalized outputs compatible with robotics research workflows, imitation learning pipelines, VLA training, OpenVLA, pi0, LeRobot, RLDS, MCAP, and cloud-based data infrastructure.
Built forBuilt for the data
Built for the data
Physical AI needs
Manipulation Learning
Capture manipulation trajectories, tool use, grasp strategies, and station-to-station workflows across manufacturing, logistics, and industrial environments.
Embodied AI & VLA Models
Generate egocentric demonstrations paired with rich task context and natural-language descriptions for Vision-Language-Action model training and instruction-following policies.
Human-Robot Interaction
Capture human-object interactions, proximity behavior, articulated object manipulation, and real-world task execution in homes, office, and service settings.
Large-Scale Robotics Research
Standardize multimodal data collection across operators, environments, and geographies to build diverse datasets for world models, imitation learning, and next-generation Physical AI systems.
Specifications
Built for the field
- Sensors
- 3× global-shutter RGB (1.6 MP) · 1× ToF depth (1 MP) · 9-axis IMU · 6-DOF F/T · 24-zone tactile array
- Capture rate
- 30 / 60 / 120 fps switchable · hardware PTP-synced across all modalities
- Onboard storage
- 1 TB NVMe — 24 hrs sustained at peak rate
- Compute
- Onboard NPU at 32 TOPS (INT8) — perception, validation, redaction local
- Battery
- 8 hrs continuous · hot-swappable · 30 min fast charge
- Operating range
- −10 °C to +45 °C · IP54-rated body
- Connectivity
- Wi-Fi 6E · LTE Cat-12 · USB-C 3.2 · 2.5 GbE · ROS 2 driver included
- Weight
- 1.4 kg with mount · 980 g without
Works with
LeRobotOpenVLApi0RLDSMCAPROS 2TensorBoardS3 / GCS / Azure
Why RebocamFrom data collection to dataset
From data collection to dataset
delivery — without the
engineering overhead
Building a multimodal robotics capture stack takes months of hardware integration, synchronization, validation, and data infrastructure work. Rebocam delivers a production-ready data collection system that gets robotics teams from deployment to training-ready datasets in days, not months.
| Rebocam | Build your own | Consumer rig | |
|---|---|---|---|
| Time to First Dataset | < 1 Day | 3 – 6 Months | 2 – 4 Weeks |
| Synchronized Multimodal Capture | |||
| Real-Time Capture Validation | |||
| Privacy & Compliance Controls | |||
| Automated Dataset Delivery | |||
| Training-Ready Output Formats | |||
| Scalable Multi-Operator Deployment |
Frequently askedFrequently asked
Frequently asked
questions
Everything you need to know about deploying Rebocam for multimodal robotics data collection. Have a specific requirement? Our team can help design a collection workflow tailored to your research or deployment goals.
Talk to an EngineerWhat kind of data can Rebocam collect?
Rebocam is designed for multimodal robotics data collection, which includes egocentric video, depth, audio, motion, force, tactile, and interaction data. The system collects synchronized data, which may be used for embodied AI, imitation learning, teleoperation, manipulation learning, and VLA modeling.
How quickly can we start collecting data?
Rebocam is built for rapid deployment. Most teams can move from setup to their first training-ready dataset in less than a day, without building custom synchronization pipelines or collection infrastructure.
Which environments does Rebocam support?
Rebocam is designed for real-world deployment across manufacturing, logistics, domestic homes, hospitality, office settings, and other operational spaces, where robotics systems are deployed.
How are datasets delivered?
Captured sessions are validated, processed, and delivered as structured datasets that integrate with existing robotics research and machine learning workflows. Dataset delivery can be configured to match your team's storage and training infrastructure requirements.
How is privacy and compliance handled?
Robgence follows structured data collection, consent, and quality assurance workflows designed for enterprise and research environments. Privacy controls, data governance, and dataset handling procedures are incorporated throughout the collection pipeline.
Can Rebocam support large-scale collection programs?
Yes. Rebocam is designed to operate as part of Robgence's broader data infrastructure, enabling coordinated collection across multiple operators, environments, and locations. This allows teams to scale from pilot studies to large-scale robotics dataset generation without redesigning their workflow.
Get startedStart collecting
Start collecting
real-world robotics data
See how Rebocam fits into your data collection workflow. Our team will help you evaluate capture requirements, deployment environments, and dataset specifications, so you can move from collection to training-ready data faster.