OpenBot
Summary
Robotics research teams routinely lose days before a single training run begins — comparing dataset licenses across browser tabs, untangling HDF5 format quirks, and arguing over whether 200 rollouts on a single seed actually tells you anything. OpenBot is the infrastructure layer built specifically for that pre-deployment grind.
The platform covers four connected steps: dataset discovery across 26 indexed egocentric and robot sets with license and format metadata compared side by side, teleop data curation that deduplicates and detects operator drift before an HDF5 dump becomes a training artifact, policy evaluation at 200 rollouts across 10 seeds with per-subtask breakdowns, and failure replay that rebuilds flagged rollouts in simulation for targeted retraining. Free access covers dataset browsing; curation and evaluation are paid-only services. The catalog currently skews egocentric and manipulation — mobile and navigation datasets are described as in progress, so teams working outside that scope hit gaps. API access is async and idempotent REST with tool-use schemas for OpenAI, Anthropic, and LangChain, so wiring evaluation into a CI runner is documented rather than improvised.
Bottom line: Pick this when your bottleneck is finding clean, license-clear teleop data and measuring whether a policy actually generalizes across seeds — but plan around the catalog gaps if your embodiment is mobile or navigation-first.
Community Performance Report Card
No community ratings yet. Be the first to rate this tool!
Community Benchmarks Community
Sign in to submit a benchmarkNo community benchmarks yet. Be the first to share a real-world data point.
Pros
Sign in to edit- License, format, and sensor signal metadata compared across 26 datasets in a single catalog, so teams stop losing hours to tab-switching and README archaeology before a training run.
- Operator drift detection and deduplication during data ingestion, which means a raw HDF5 teleop dump becomes a versioned, replay-ready artifact instead of a liability that poisons the next training run.
- Per-subtask, per-seed policy evaluation at 200 rollouts across 10 seeds by default, so a single lucky run no longer masquerades as a deployment verdict — the exact subtask where a VLA breaks is named.
- Synth rebuilds the specific failed rollouts Bench flags and sweeps the fragile randomization axes, so teams feed targeted failure data back into training rather than guessing at augmentation strategy.
- Async idempotent REST API with tool-use schemas for OpenAI, Anthropic, and LangChain, so the evaluation loop wires into an existing CI runner without a custom integration layer.
Cons
Sign in to edit- Dataset catalog coverage at 26 sets is concentrated in egocentric and manipulation data — the vendor states mobile and navigation categories are still being indexed, so a team working on mobile manipulation or navigation-first tasks hits catalog gaps immediately and must maintain their own dataset index in parallel.
- Curation and evaluation services are paid-only with no self-service path described; teams that need to run a quick evaluation iteration outside a contracted engagement are blocked at 'Talk to us' with no documented turnaround or pricing signal.
- No self-hosted option exists, so teams with data governance requirements that prohibit sending robot telemetry or policy checkpoints to a third-party cloud cannot use any paid service tier — at that point they build or choose infrastructure that runs on their own hardware.
Community Reviews
Sign in to write a reviewNo reviews yet. Be the first to share your experience.
About
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-06-28T18:25:07.498Z
Best For
Who it's for
- Embodied AI and robotics research teams
- Policy evaluation across multiple embodiments
- Teleop data curation and failure analysis
- Teams moving from datasets to deployment verdicts
What it does well
- Compare datasets before training runs
- Clean raw teleop data into versioned LeRobot or RLDS sets
- Evaluate policies with 200 rollouts across 10 seeds
- Replay and retrain on flagged failure cases in simulation
Integrations
Discussion Community
Sign in to commentNo discussion yet. Sign in to start the conversation.
Compare OpenBot
Spotted incorrect or missing data? Join our community of contributors.
Sign Up to ContributeCommunity Notes & Tips Community
Sign in to contributeBe the first to contribute. General notes, observations, gotchas, and tips from people who use this tool day-to-day.
Frequently Asked Questions
- Is OpenBot free?
- OpenBot has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is OpenBot open source?
- No — OpenBot is a closed-source tool. Source code is not publicly available.
- Does OpenBot have an API?
- Yes. OpenBot exposes a developer API. See the official documentation at https://openbot.ai for details.
Hours Saved & ROI Stories Community
Sign in to contributeBe the first to contribute. Concrete time/cost savings, with context. e.g. "Cut my code review backlog from 4h to 45m per week."
Curated lists that include this category
Teleop data rarely arrives clean, dataset licenses are scattered across PDFs and GitHub READMEs, and a single lucky rollout is not a deployment verdict. OpenBot addresses that specific stack with four modules in sequence: Catalog indexes 26 ego and robot datasets with access terms, format, and sensor signals compared in one place; Data ingests RLDS, LeRobot, Open X-Embodiment, and HDF5 dumps, then deduplicates and detects operator drift to produce a versioned, replay-ready artifact; Bench runs 200 rollouts across 10 seeds by default and surfaces per-subtask survival rates, sim-to-real gap, and intervention rate per embodiment rather than an aggregate number; and Synth rebuilds the exact failed rollouts Bench flags, sweeps the randomization axes identified as fragile, and routes the augmented data back into training.
The differentiating capability is the closed loop between Bench and Synth. Most evaluation pipelines tell you a policy failed — they do not hand you the failure scenes reconstructed in simulation with the fragile axes already identified. The vendor’s framing is explicit: the bottleneck is not a better model, it is finding the right data, cleaning it, and measuring readiness. Bench and Synth together are the answer to ‘we know it fails, we do not know why or what to feed it next.’
The platform fits embodied AI research teams moving from a checkpoint to a deployment decision, particularly those working with egocentric and manipulation data where the catalog is deepest at 26 indexed sets. It does not fit teams that need to run infrastructure on their own hardware — the vendor describes no self-hosted option. Mobile and navigation dataset categories are described as in progress, so teams outside the manipulation and egocentric scope will find the catalog incomplete. Curation and evaluation services require contacting the vendor directly; free access is limited to browsing the dataset catalog.
Programmatic access runs through an async, idempotent REST API with one call per real task. Tool-use schemas are provided for OpenAI, Anthropic, and LangChain, which means the evaluation loop can be wired directly into an existing runner or CI pipeline without building a custom integration layer from scratch.