OpenBot

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.