Data Ingestion Pipeline
500+ episodes/hour throughput. Upload via API, S3/GCS sync, fleet agent streaming, or direct SVRC data collection integration. Automatic schema validation, timestamp sync checks, and metadata extraction on every ingest.
The Complete Operating System for Robot Learning
From data collection to fleet deployment — 86+ integrated tools for ingestion, annotation, training, simulation, teleoperation, safety validation, and enterprise operations. One platform, every stage of the robot learning lifecycle.
Three Problems Every Robot Learning Team Hits
Robot learning is advancing fast, but the infrastructure around it has not kept up. Most teams run into the same three walls once they move past proof-of-concept:
Data Is Scattered Across Drives
Teleoperation episodes live on individual laptops, shared NAS drives, and random S3 buckets. Nobody knows which dataset was used for which training run, and finding a specific failure episode takes hours of manual searching. When a researcher leaves, their data organization leaves with them.
You Cannot See Why a Policy Failed
Your deployed policy drops an object 12% of the time. Where exactly does it fail? At what joint angle? On which object type? Without frame-level replay linked to joint states and gripper data, debugging is guesswork. Teams spend weeks re-collecting data for failures they cannot precisely diagnose.
No Systematic Way to Improve
You trained v7 of your policy. Is it actually better than v6? On which tasks? Without experiment tracking, held-out evaluation sets, and regression testing, every release is a leap of faith. Teams oscillate between versions because they lack the evidence to make confident decisions.
Fearless exists to solve these three problems with a single platform that connects data collection, analysis, evaluation, and retraining into one continuous workflow.
Technical Architecture
Fearless is built as a modular pipeline with five stages. Each stage operates independently but shares a unified data model.
Stage 1
Data Ingestion Pipeline
Episodes enter Fearless through the upload API, fleet agent streaming, or direct SVRC data collection integration. The ingestion pipeline validates episode structure against your schema, checks timestamp synchronization, extracts metadata, and indexes all streams for fast retrieval. Supports batch upload (S3/GCS sync) and real-time streaming from deployed robots. Ingestion throughput: 500+ episodes/hour for standard HDF5 format.
Stage 2
Annotation & Labeling Tools
Built-in annotation tools for adding language instructions, task phase labels, keyframe markers, success/failure labels, and custom tags to episodes. Supports batch annotation workflows for large datasets. Annotations are stored as structured metadata linked to specific timestamps, not baked into the data files. Export annotations in COCO, VIA, or custom JSON schemas.
Stage 3
Training Job Management
Define training configurations that reference specific dataset versions. Kick off training runs on your own compute or SVRC-managed GPU clusters. Track hyperparameters, training curves, and resource utilization. Automatic checkpointing and experiment comparison. Integrates natively with LeRobot and Hugging Face training scripts. Custom training frameworks supported through a standard launcher API.
Stage 4
Model Versioning & Registry
Every trained model is versioned and linked to the exact dataset, training configuration, and evaluation results that produced it. Compare model versions across metrics. Promote models through staging environments (dev, staging, production). Full audit trail from data to deployed model. Export models in ONNX, TorchScript, or native framework format.
Stage 5
Deployment & Feedback Loop
Lightweight fleet agent streams deployment data back into Fearless. Monitor success rates, failure modes, and performance degradation in real-time. When a deployed policy encounters a new failure, the episode flows directly into your failure mining queue. Automatic alerts when policy performance drops below configured thresholds. This closed loop is what separates teams who improve steadily from teams who plateau.
86+ Integrated Tools
Ingestion
Annotation
Quality
Browser
Registry
Pipeline
VLA
Training
World Models
Studio
3D Generation
Simulation
Fleet
Mission
Teleop
Observability
Validation
Replay
Anomaly
Performance
Billing
Tickets
Maintenance
Knowledge
Platform Capabilities
Every stage of the robot learning lifecycle — from raw data ingestion through fleet deployment and safety validation — organized into six domains.
◆ Collect & Annotate
Annotation Studio
Add language instructions, task phase labels, keyframe markers, success/failure tags, and custom metadata. Batch annotation workflows for large datasets. Export in COCO, VIA, or custom JSON schemas. Annotations link to specific timestamps, never baked into data files.
Data Quality Scoring
Automatic quality metrics on every episode: timestamp jitter, camera frame drops, joint state discontinuities, and calibration drift. Flag low-quality episodes before they contaminate your training set. Quality dashboards across your entire data estate.
Episode Browser & Replay
Frame-level replay with synchronized joint states, camera feeds, and gripper aperture. Scrub to the exact moment a grasp fails. Overlay target vs. actual positions. Filter by task, operator, robot, success/failure, or date range. Export annotated clips for review.
◆ Train & Evaluate
Model Registry
Every model versioned and linked to exact dataset, config, and eval results. Compare versions across metrics. Promote through dev/staging/production. Export ONNX, TorchScript, or native format. Full audit trail from data to deployed model.
Policy Pipeline
End-to-end pipeline from dataset curation to training, evaluation, and deployment. Trigger retraining on failure-rate thresholds or dataset size. Native LeRobot and Hugging Face integration. Custom frameworks via standard launcher API.
VLA Paradigm Lab
Experiment with vision-language-action architectures. Benchmark ACT, Diffusion Policy, Octo, RT-2, OpenVLA, and custom VLA models. Side-by-side evaluation on held-out episodes. Track which architecture works best for your task distribution.
Training Job Management
Kick off training on your compute or SVRC-managed GPU clusters. Track hyperparameters, training curves, and resource utilization. Automatic checkpointing and experiment comparison. Model evaluation scores flow back for tracking.
◆ Simulate & Generate
World Models
Generate synthetic training scenarios from learned world models. Predict future states, test policy robustness against simulated perturbations, and augment real-world datasets with physics-consistent synthetic episodes.
World Studio
Visual scene editor for constructing simulation environments. Drag-and-drop objects, configure physics properties, set up camera viewpoints, and define task specifications. Export scenes to MuJoCo, Isaac Sim, or custom simulators.
Text-to-3D Scene Generation
Describe a scene in natural language and generate 3D environments for simulation. Create diverse training scenarios at scale without manual asset creation. Integrate generated scenes directly into your simulation pipeline.
Robotics & AGV Simulation
Built-in robotics simulation for policy testing before real-world deployment. AGV path planning and fleet logistics simulation. Validate policies in diverse environments before committing to physical hardware time.
◆ Deploy & Operate
Fleet Management
Monitor every robot across facilities from a single dashboard. Track deployment status, software versions, uptime, and utilization. Push policy updates to selected robots or entire fleets. Role-based access per facility or robot group.
Mission Control
Define, schedule, and monitor robot missions. Real-time task progress, queue management, and priority overrides. Automatic fallback handling when a robot encounters an unrecoverable state. Mission logs feed directly into the episode browser.
Teleoperation Hub
Low-latency remote teleoperation with multi-camera views, force feedback, and VR controller support. Operator performance tracking. Every teleop session automatically captured as a training episode. Works with glove, pendant, and leader-follower setups.
Real-Time Observability
Live dashboards for joint torques, latency, success rates, and failure modes across your fleet. Configurable alerts when performance drops below thresholds. Historical trend analysis. Export metrics to Grafana, Datadog, or custom monitoring stacks.
◆ Safety & Validation
Validation Hub
Structured safety validation with ODD (Operational Design Domain) definitions, FMEA worksheets, and safety case templates. Link safety requirements to test episodes that verify them. Maintain a traceable chain from hazard analysis to evidence.
Runtime Replay & Failure Mining
Automatically flag anomalous episodes: force spikes, unexpected velocities, gripper mismatches, task timeouts. Surface highest-impact failures first. Cluster similar failures to separate systematic issues from noise. One-click replay from failure report.
Anomaly Detection
ML-based anomaly detection across joint trajectories, force profiles, and camera feeds. Detect distribution shift between training data and production behavior. Early warning before failures manifest. Configurable sensitivity per robot and task.
Performance Engineering
Benchmark cycle times, throughput, and resource utilization across your fleet. Identify bottlenecks in perception, planning, and execution. A/B test policy versions on live traffic. Quantify the impact of every model update.
◆ Enterprise Operations
Billing & Usage
Transparent usage-based billing with per-team and per-project breakdowns. Storage, compute, and API usage tracked in real time. Invoice history, budget alerts, and cost allocation tags for finance teams.
Service Tickets & Parts
Integrated service ticket system for robot maintenance and repair. Parts inventory management with reorder alerts. Link tickets to specific robots, episodes, and failure reports. Track mean time to repair across your fleet.
Maintenance Logs
Complete maintenance history for every robot: calibrations, part replacements, firmware updates, and inspection records. Schedule preventive maintenance based on usage hours or cycle counts. Exportable for compliance audits.
Knowledge Base & Playbooks
Centralized operations knowledge base with runbooks, troubleshooting guides, and best practices. Operations playbooks for common scenarios. Searchable across your organization. New team members get up to speed faster.
How Fearless Fits in Your Stack
Fearless is the operating system layer between your hardware, your AI models, and your deployed fleet. It does not replace your training framework or control stack — it connects every piece into a single closed loop.
Hardware
Arms, humanoids, AGVs
→
Arms, humanoids, AGVs
Collect & Annotate
Teleop, autonomous, simulation
→
Teleop, autonomous, simulation
Fearless Platform
86+ tools across 6 domains
→
86+ tools across 6 domains
Train & Simulate
VLA, world models, LeRobot
→
VLA, world models, LeRobot
Deploy & Validate
Fleet ops, safety, observability
↶
Fleet ops, safety, observability
Deployment data flows back into Fearless automatically. Every failure in production becomes a data point for the next training cycle. This is the closed loop that separates teams who improve steadily from teams who plateau.
Supported Data Formats
Fearless ingests the formats robot learning teams actually use. No conversion scripts required for standard formats; custom formats are supported through a pluggable parser API.
| Format | Use Case | Support Level | Details |
|---|---|---|---|
| HDF5 | ACT, ALOHA, and most imitation learning pipelines | Native | Hierarchical episode structure, random access via h5py, supports nested observation/action groups |
| RLDS | Google DeepMind RT-X and Open X-Embodiment datasets | Native | TFRecord serialization, tf.data streaming, cross-embodiment schema compatible |
| LeRobot Parquet | Hugging Face LeRobot training and dataset sharing | Native | Compact MP4 video storage, one-command HF Hub push, Apache Arrow for fast columnar access |
| MP4 + JSON | Video recordings with sidecar metadata files | Native | H.264/H.265 video with JSON metadata sidecar, automatic frame extraction |
| ROS Bag | ROS1/ROS2 recordings from robot systems | Import | Automatic topic extraction and conversion to native format on import |
| Custom | Proprietary formats via pluggable parser API | API | Python parser interface, schema definition DSL, automatic validation |
Integrations & SDK
Fearless connects to your existing stack through three integration paths.
Bridge
ROS2 Bridge
A lightweight ROS2 node that subscribes to your robot's topics (joint states, camera images, gripper commands) and streams them directly to Fearless as structured episodes. Configure topic mappings in YAML. Supports ROS2 Humble and Iron. Automatic episode segmentation based on configurable triggers (e.g., gripper open/close, task start signal).
pip install fearless-ros2-bridge
SDK
Python SDK
Full programmatic access to the platform. Upload episodes, create datasets, trigger evaluations, query metrics, and manage fleet data. Type-annotated, async-compatible, with comprehensive docstrings. Supports batch operations for large-scale workflows.
pip install fearless-sdk
API
REST API
OpenAPI 3.1 specification covering all platform operations. Episode upload, dataset management, evaluation triggers, metric queries, fleet data ingestion, and model registry operations. TypeScript SDK also available. Rate limits: 1,000 req/min (Startup), unlimited (Enterprise).
Docs: developers/
How Fearless Compares
Fearless is purpose-built for robot learning data. Here is how it compares to alternatives teams commonly use.
| Capability | Fearless | Custom Scripts | W&B / MLflow | Scale AI |
|---|---|---|---|---|
| Robot episode replay (joint + camera sync) | Built-in | Manual build | Not supported | Not supported |
| HDF5 / RLDS / LeRobot native support | Native | Per-format code | Generic artifact | Not supported |
| Failure mining & anomaly detection | Automatic | Manual analysis | Metric tracking only | Not supported |
| Policy evaluation framework | ACT, DP, VLA, custom | Custom eval scripts | Generic metrics | Not supported |
| Fleet deployment monitoring | Real-time dashboard | Custom telemetry | Not designed for | Not supported |
| Dataset versioning with hardware lineage | Built-in | Git LFS / DVC | Artifact versioning | Not supported |
| Self-hosted / air-gapped deployment | Enterprise plan | Yes (you build it) | W&B Server only | No |
Pricing
Start free for research. Scale up when your team and data grow.
Academic
Research
Free
For university labs and non-commercial research
- Up to 5 team members
- 500 GB storage
- Episode Viewer & Dataset Management
- Policy Evaluation (100 runs/month)
- Community support
Most Popular
Startup
$249/mo
For early-stage robotics companies
- Up to 20 team members
- 5 TB storage
- All Research features
- Failure Mining & Retraining Pipeline
- Fleet Integration (up to 10 robots)
- API access (1,000 req/min)
- Email support (24h response)
Scale
Enterprise
Custom
For production robotics operations
- Unlimited team members
- Unlimited storage
- All Startup features
- Self-hosted deployment option
- Unlimited fleet robots
- SSO / SAML integration
- GDPR & SOC 2 compliance
- Dedicated support engineer
Technical Specifications
API & SDKs
RESTful API with OpenAPI 3.1 specification. Python and TypeScript SDKs. Covers all 86+ platform operations: episode upload, dataset management, training triggers, fleet commands, simulation control, and safety validation. Rate limits: 1,000 req/min (Startup), unlimited (Enterprise). Webhook and event-stream support.
VLA & Model Support
Native support for vision-language-action architectures: ACT, Diffusion Policy, Octo, RT-2, OpenVLA, and custom VLA models. World model inference for synthetic data generation. Simulation-in-the-loop evaluation before real-world deployment. Model export in ONNX, TorchScript, and native formats.
Simulation & World Models
Integrated simulation environments for policy validation. World Studio for 3D scene construction. Text-to-3D generation for synthetic training scenarios. MuJoCo, Isaac Sim, and custom simulator export. AGV logistics simulation for warehouse and factory floor planning.
Deployment Options
Cloud-hosted on SVRC infrastructure (US-West and EU regions) with 99.9% uptime SLA. Enterprise self-hosting via Docker + Kubernetes (Helm chart provided). Air-gapped installations for defense and regulated industries. Minimum self-host: 8-core CPU, 32 GB RAM, GPU recommended.
Data Privacy & Security
Encrypted at rest (AES-256) and in transit (TLS 1.3). Logical tenant isolation. No data shared across organizations or used for SVRC model training. GDPR-compliant with US and EU residency options. Full data export at any time in original formats. SOC 2 Type II audit in progress.
Export & Portability
Export datasets in HDF5, RLDS, or LeRobot format. Bulk export via API. Push directly to Hugging Face Hub. No vendor lock-in: your data stays in standard, open formats. Cancel and download everything within 90 days. Simulation scenes exportable to MuJoCo, Isaac Sim, and USD.
Who Fearless Is Built For
Research Labs
You are collecting teleoperation data for imitation learning research. You need to organize episodes across multiple students and projects, compare policy variants for publications, and share datasets with collaborators. Fearless gives you versioned datasets, reproducible evaluations, and a single place where your lab's data lives beyond any individual researcher.
Robotics Startups
You are building a product that relies on learned manipulation policies. You need to iterate quickly: collect data, train, evaluate, deploy, observe failures, and retrain. Fearless connects this loop so your engineering team stops spending 40% of their time on data pipeline plumbing and starts spending it on the model and the product.
Enterprise Robotics Teams
You are running robots in production across multiple facilities. You need fleet-wide visibility into policy performance, systematic failure analysis, and an auditable trail from data collection through deployment. Fearless provides the compliance, access controls, and operational dashboards that production environments require.
Compatible Hardware
Supported Hardware
Works with any robot that produces standard data formats. Buy compatible hardware directly from our store.
OpenArm
SVRC's open-source 7-DOF research arm
Buy →Franka Research 3
Precision research manipulator
Buy →UR5e / UR10e
Universal Robots collaborative arms
Buy →Unitree G1
Humanoid robot platform
Buy →Unitree Go2
Quadruped robot
Buy →AgileX Piper
Lightweight leader-follower arm
Buy →Mobile ALOHA
Bimanual mobile manipulation
Buy →SO-100
Affordable entry-level arm
Buy →These are just the robots we stock. Fearless works with any robot that produces standard data formats (HDF5, RLDS, LeRobot, ROS Bag, MP4+JSON). Browse our full hardware catalog →
Frequently Asked Questions
Is my data private?
Yes. Your data is encrypted at rest and in transit, logically isolated from other organizations, and never used for SVRC's own training or shared with third parties. Enterprise customers can self-host for complete data sovereignty. We support GDPR data residency requirements with US and EU hosting options.
What robots does it support?
Fearless is robot-agnostic. It works with any hardware that produces standard data formats (HDF5, RLDS, LeRobot, ROS Bag, MP4+JSON). We have tested integrations with OpenArm, Franka Research 3, UR5e/UR10e/UR20, Unitree G1/Go2, AgileX Piper, Mobile ALOHA, SO-100, and many more. If your robot produces joint states and camera images, Fearless can ingest it. Browse compatible hardware in our store.
Can I self-host?
Yes, on the Enterprise plan. Self-hosted Fearless runs as a set of Docker containers orchestrated by Kubernetes (Helm chart provided). Minimum requirements: 8-core CPU, 32 GB RAM, and GPU recommended for evaluation workloads. Air-gapped deployments are supported for defense and regulated environments.
How does it integrate with LeRobot?
Fearless reads and writes LeRobot Parquet format natively. You can push a curated dataset from Fearless directly to a Hugging Face Hub repository for training with LeRobot. Retraining pipeline triggers can invoke LeRobot training scripts on your own compute or on SVRC-managed GPU clusters. Evaluation results from LeRobot training runs flow back into Fearless for tracking.
Is there an API?
Yes. The Fearless API follows OpenAPI 3.1 and supports all platform operations: episode upload, dataset management, evaluation triggers, metric queries, and fleet data ingestion. Python and TypeScript SDKs are available. API access is included in the Startup and Enterprise plans.
How much data can I store?
Research plan: 500 GB. Startup plan: 5 TB. Enterprise plan: unlimited. Storage is measured by raw uploaded data size. For reference, a typical ALOHA bimanual episode (3 cameras, 50 Hz joint data, 30-second task) is approximately 150 MB. A 500 GB allocation holds roughly 3,300 episodes of this type.
Can I import ROS bag files?
Yes. The platform supports ROS bag import with automatic topic extraction and conversion. Specify the topic-to-field mapping in the import configuration, and Fearless converts the bag into a native episode format for replay, annotation, and evaluation.
How does the ROS2 bridge work?
The fearless-ros2-bridge is a lightweight ROS2 node that subscribes to your robot's topics and streams data directly to Fearless. Configure topic mappings in a YAML file. Automatic episode segmentation based on triggers you define (gripper events, task signals, timeouts). Supports ROS2 Humble and Iron distributions. Install via pip.
What training frameworks are supported?
Native integration with LeRobot and Hugging Face training pipelines. Custom training frameworks are supported through a standard launcher API — you provide a training script that accepts a dataset path and config file, and Fearless handles orchestration, checkpointing, and result tracking.
Can I use Fearless with SVRC data collection services?
Yes. Enterprise data collection campaigns include Fearless Platform access. Data collected by SVRC operators flows directly into your Fearless workspace with full metadata, QA reports, and lineage information. This is the most efficient path to a closed-loop data-to-deployment pipeline.
Related Resources
Data Collection Services
Managed teleoperation campaigns that deliver training-ready datasets directly into Fearless.
Hardware Catalog
OpenArm 101, DK1, UR series, and more. Purchase or lease the hardware that generates your training data.
Data Format Comparison Guide
Technical deep dive on HDF5, RLDS, and LeRobot formats with conversion examples.
All Guides
Technical guides covering data collection, teleoperation setup, policy training, and deployment.