「 Inside the Physical Data Compiler 」
Physical AI DBMS
makes physical experience callable.
Agents describe the task evidence they need. The system plans the query, resolves lineage, schedules data and compute, and returns training-ready or evaluation-ready assets.
Explore resource orchestration「 Why A Physical AI DBMS 」
Physical experience cannot be managed as a folder of files.
Robot data is multimodal, time-dependent, task-dependent, and continuously revised. It needs database semantics designed for physical tasks.
01
One task spans many signals
Video, action, force, robot state, contact, and outcome must remain aligned on one task clock.
02
Every slice needs context
A frame or trajectory is useful only when its task stage, environment, calibration, and outcome remain traceable.
03
Agents need more than file paths
Training systems and agents need to retrieve task stages, failures, comparisons, and evaluation-ready assets directly.
「 Resource Orchestration 」
Every agent request becomes an executable data plan.
The control plane resolves task intent, lineage, quality, and locality before scheduling index scans, episode reads, CPU validation, GPU compilation, caching, and export.
「 Task-Native Data Lifecycle 」
From synchronized evidence to model-ready assets.
Physical AI DBMS organizes every episode as task structure, preserves evidence lineage, applies quality gates, and serves compiled operational data in training-ready formats.
「 What It Serves 」
The same physical experience, organized for different jobs.
One governed task corpus can supply training, evaluation, failure analysis, and intelligence services without duplicating the source of truth.
Training-ready task slices
Retrieve aligned episodes by task stage, embodiment, environment, outcome, or quality threshold.
Evaluation and regression sets
Build versioned benchmarks from success, failure, recovery, and edge-case evidence.
Failure and lineage queries
Trace a result back through task context, source episode, calibration, compiler version, and quality decision.
Agent-native data access
Let agents request task evidence and compiled assets through structured or natural-language interfaces.