Robot Learning Datasets for Manipulation and Navigation AI
Teleoperated demonstration datasets, interaction logs, and scene annotations for robot learning. Built for imitation learning, reinforcement learning, and foundation model training for robotic manipulation and navigation.
20+
Years in AI training data
1,000+
Language locales
1M+
Hours of video annotated
ISO
27001 certified
The Challenge
Beyond Public Robot Learning Benchmarks
Open X-Embodiment and BridgeData V2 aggregated robot demonstration data across platforms. Fine-tuning robot foundation models for your specific hardware and task domain requires demonstrations on your robot in your environment.
Robot learning models trained on diverse heterogeneous demonstration data must be fine-tuned on in-domain demonstrations to achieve reliable task performance on specific hardware. Platform-specific kinematics, sensor configurations, and workspace layouts require matched demonstration data.
Off-the-shelf robot datasets suffer from embodiment mismatch (different robot morphology, sensor suite, and actuation limits) and task gaps for specific manipulation or navigation tasks your deployment requires.
LXT collects custom robot learning datasets on your specific hardware platform and task configuration. We provide teleoperation demonstration collection, expert annotator training, and structured task protocols that deliver high-quality behavioral data for your robot learning pipeline.
Why Teams Upgrade
Limitations of Public Robot Learning Datasets
Standard benchmarks serve research well. Production deployments need more.
| Dataset | Primary Limitation | Impact |
|---|---|---|
| Open X-Embodiment | Highly diverse platforms and tasks; heterogeneous quality; embodiment mismatch requires fine-tuning; limited coverage of specific manipulation tasks | Requires fine-tuning |
| BridgeData V2 | WidowX arm in tabletop only; specific workspace; cannot be used directly for other robot platforms or environments | WidowX only |
| RT-1 Dataset | Proprietary Google robot only; specific mobile manipulator; not publicly available for commercial fine-tuning | Proprietary |
| DROID | Diverse but requires significant fine-tuning for specific platforms; collection protocol variance across sites | Diverse variance |
| ALOHA | Bimanual manipulation only; specific ALOHA hardware; limited task coverage outside tabletop manipulation | ALOHA hardware |
Not sure which specs you need?
Our data specialists help you scope the right dataset for your model architecture.
Configurable Specifications
Specs Built Around Your Model
Public datasets come fixed. Yours is configured for your architecture, environment, and use case.
Data Collection
Demonstration Protocol
- Teleoperation: Expert human operators demonstrate tasks via kinesthetic or VR control
- Task Protocols: Structured task specifications with success criteria and variation coverage
- Trial Count: Minimum successful and failed demonstrations per task configuration
Hardware Scope
Robot Platform
- Manipulation: 6-DOF arms, parallel grippers, dexterous hands, and mobile manipulators
- Navigation: Wheeled, legged, and aerial platforms with sensor suite matching
- Sensor Suite: RGB cameras, depth, LiDAR, force-torque, and proprioception logging
Annotation Types
Label Coverage
- State Labels: Gripper state, joint positions, end-effector pose per timestep
- Object Annotations: Target object poses, grasps, and interaction state labels
- Task Labels: Subtask boundaries, success markers, and failure mode labels
Need a custom configuration?
We've built datasets across dozens of domains and use cases. Let's scope yours.
Capturing Complexity
Edge Cases in Robot Datasets
High-accuracy models handle rare attributes that public datasets miss.
Failure Mode Coverage
Successful demonstrations alone are insufficient for robust robot learning. Annotated failure trajectories with failure mode labels train recovery behaviors and uncertainty estimation models.
Object Variation and Generalization
Task objects vary in appearance, weight, and compliance. Systematic variation across object instances within task categories ensures generalization to novel object instances.
Environment and Workspace Variation
Lighting, clutter, and workspace configuration change in deployment. Systematic collection across environment variations trains policies that generalize beyond fixed lab conditions.
Rare and Recovery Transitions
Partial task completion, re-grasp attempts, and error recovery produce distinctive trajectory segments. Annotated recovery transitions support robust policy training for real-world deployment.
Ground Truth Quality
Human-in-the-Loop Annotation
Precise annotation bridges raw data and learnable signal. Expert annotators deliver precision automated tools can't match.
Teleoperation Collection
Trained operators collect high-quality demonstrations via kinesthetic teaching or VR teleop interface. Success rate tracking and trial logging included per task configuration.
State and Action Logging
Full proprioceptive state, action, and sensor data logged at system frequency. Synchronized RGB, depth, and force-torque streams with consistent timestamp format.
Subtask and Object Labels
Subtask boundary annotations, object pose labels, and failure mode classification delivered as structured metadata alongside raw demonstration trajectories.
Industry Applications
Robot Datasets for Your Domain
Custom taxonomies and collection protocols for specific deployment contexts.
Manufacturing
Assembly, bin picking, kitting, inspection tasks
Healthcare Robotics
Surgical assistance, patient handling, dispensing
Logistics
Pick-and-pack, sorting, palletizing, last-mile delivery
Food and Agriculture
Harvesting, processing, quality inspection
Retail
Shelf restocking, inventory, customer assistance
Service Robots
Cleaning, delivery, hospitality, household tasks
Research
Foundation model training, sim-to-real transfer
Evaluation
Task success benchmarking, policy comparison
Compliance & Ethics
Secure and Ethical Data Collection
Data collection involving people and sensitive content requires robust security, compliance, and ethical protocols at every stage.
Global Demographic Reach
Collection across 1,000+ locales and diverse demographics to prevent algorithmic bias in your deployed models.
ISO 27001 Certified
Sensitive projects processed in certified secure facilities meeting the highest information security standards.
GDPR & Privacy Compliance
All collection and annotation protocols vetted for consent and privacy. Legally robust for global deployment.
Frequently Asked Questions
Robot Dataset FAQs
Can you collect on our specific robot hardware?+
Yes. We train operators on your robot platform and collect demonstrations in your workspace. Remote and on-site collection options available depending on hardware access requirements.
How do you train expert demonstrators?+
Demonstrators undergo structured training on each task with success criterion verification. Only demonstrations meeting quality checks are included in delivery.
What data logging format do you use?+
HDF5 with timestamped state, action, and sensor streams; ROS bag; LeRobot format; and custom formats. All formats include synchronized metadata and episode success labels.
Can you cover both success and failure demonstrations?+
Yes. Failure demonstrations with annotated failure modes are available as an add-on. Failure coverage ratios and mode distributions are agreed upfront.
How do you ensure task variation coverage?+
We define a task variation matrix upfront and track completion of each variation cell. Delivery is blocked until all specified object, environment, and configuration combinations are covered.
What does a custom robot dataset cost?+
Projects range from $20K for focused single-task collections (500-2,000 demonstrations) to $200K+ for large multi-task, multi-object manipulation datasets.
Can you collect remotely using our teleop interface?+
Yes for platforms that support remote teleoperation. We coordinate with your engineering team to integrate with your teleop system and logging infrastructure.
Get Started
Scope Your Custom Robot Learning Dataset
Share your robot platform, task configuration, and demonstration requirements. A robot learning data specialist will provide a detailed proposal within 48 hours.
Please provide us with the details of your inquiry and one of our team members will be in touch.
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