Upload your teleoperation data, replay failures frame-by-frame, benchmark policies, and close the loop from deployment back to training.
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:
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.
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.
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.
Fearless is built as a modular pipeline with five stages. Each stage operates independently but shares a unified data model.
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.
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.
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.
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.
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.
Six capabilities that turn disconnected robot data into a structured learning pipeline.
Replay any teleoperation episode frame-by-frame with synchronized joint states, camera feeds, and gripper aperture. Scrub to the exact moment a grasp fails. Overlay target vs. actual joint positions to see where the policy diverged from the demonstration. Filter episodes by task, operator, success/failure, or date range. Export annotated clips for team review or publication.
Run your trained model against held-out test episodes and get structured success-rate metrics broken down by task, object category, and difficulty level. Compare two policy versions side-by-side on the same evaluation set. Track evaluation scores over time to detect regressions before they reach deployment. Supports ACT, Diffusion Policy, and custom model architectures through a standard evaluation API.
Organize episodes into versioned datasets with tags, splits, and access controls. Every dataset maintains a complete lineage: which hardware collected it, which operator demonstrated, what calibration was active. Share datasets across your team with granular permissions. Fork a public dataset, add your own episodes, and track the delta. Never lose track of what data trained which model.
Automatically flag anomalous episodes based on force spikes, unexpected joint velocities, gripper state mismatches, or task timeout. Surface the highest-impact failures first: episodes where the policy was most confident but the outcome was worst. Cluster similar failures to identify systematic issues rather than one-off noise. Generate failure reports that link directly to the Episode Viewer for root-cause investigation.
Integrate with LeRobot and Hugging Face to kick off new training runs directly from curated datasets. Define retraining triggers: automatically start a new run when failure rate exceeds a threshold or when a dataset reaches a minimum episode count. Track training runs alongside the datasets and evaluations that produced them, creating a complete audit trail from data to deployed model.
Receive live deployment data back into the platform for continuous improvement. Connect deployed robots to Fearless through a lightweight agent that streams episode data, performance metrics, and failure events. Monitor fleet-wide success rates on a real-time dashboard. When a deployed policy encounters a new failure mode, the episode flows directly into your failure mining queue for analysis and eventual retraining.
Fearless is the connective layer between your hardware, your training infrastructure, and your deployed robots. It does not replace your training framework or your robot control stack; it connects them.
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.
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 |
Fearless connects to your existing stack through three integration paths.
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
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
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/
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 |
Start free for research. Scale up when your team and data grow.
Free
For university labs and non-commercial research
$249/mo
For early-stage robotics companies
Custom
For production robotics operations
RESTful API with OpenAPI 3.1 specification. Python and TypeScript SDKs. Upload episodes, trigger evaluations, query metrics, and manage datasets programmatically. Rate limits: 1,000 requests/minute (Startup), unlimited (Enterprise). Webhook support for event-driven workflows.
Cloud-hosted on SVRC infrastructure (US-West and EU regions) with 99.9% uptime SLA. Enterprise customers can self-host on their own infrastructure using a Docker-based deployment with Helm charts for Kubernetes. Air-gapped installations available for defense and regulated industries. Minimum self-host requirements: 8-core CPU, 32 GB RAM, GPU recommended.
Data is encrypted at rest (AES-256) and in transit (TLS 1.3). Each team's data is logically isolated. No data is shared across organizations or used for SVRC's own model training. GDPR-compliant with data residency options in the US and EU. Full data export available at any time in original formats. SOC 2 Type II audit in progress.
Export any dataset in HDF5, RLDS, or LeRobot format. Bulk export via API. No vendor lock-in: your data remains in standard, open formats. Cancel your subscription and download everything within 90 days. We also support export to Hugging Face Hub directly from the platform.
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.
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.
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.
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.
Fearless is robot-agnostic. It works with any hardware that produces standard data formats (HDF5, RLDS, LeRobot, MP4+JSON). We have tested integrations with WidowX, ViperX, Franka Emika, UR5/UR10, Unitree arms, Koch v1.1, SO-100, and custom research platforms. If your robot produces joint states and camera images, Fearless can ingest it.
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.
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.
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.
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.
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.
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.
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.
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.
Managed teleoperation campaigns that deliver training-ready datasets directly into Fearless.
OpenArm 101, DK1, UR series, and more. Purchase or lease the hardware that generates your training data.
Technical deep dive on HDF5, RLDS, and LeRobot formats with conversion examples.
Technical guides covering data collection, teleoperation setup, policy training, and deployment.