Introduction
Progress in autonomous systems demands transparency, auditability, and interoperability from the simulation phase onward. Unclear ownership models, ambiguous data lineage, and tooling silos limit adoption at scale. Deeptics introduces on‑chain asset standards, open job orchestration, and execution proofs so every artifact from a robot mesh to an experiment report has identity, permissions, and economic value.
Problem Statement
Weak provenance: difficult to trace asset origin and version history.
Complex licensing and monetization: assets are reused without fair compensation.
Closed execution: simulation outputs are hard to audit; reproducibility is low.
Ecosystem fragmentation: disparate tools and formats slow collaboration.
Compute scalability: large experiments need efficient orchestration and cost control.
Deeptics Solutions
On‑Chain Asset Identity — Each asset (robots, sensors, environments, controllers, datasets) is registered with metadata, versions, and licensing policy.
Job Orchestration & Execution — Users describe jobs (scenarios, inputs, physics constraints); the network executes on verified compute nodes.
Proof‑of‑Simulation (PoSml) — Verifiable on‑chain/off‑chain proofs: hash‑bound logs, artifact checksums, and deterministic/semi‑deterministic verification.
License & Results Marketplace — Creators earn royalties automatically; buyers acquire clear usage rights. Simulation results are tradable.
Interoperable Formats — Support for common industry conventions (URDF/SDF‑style concepts, kinematic chains, sensor rigs) without vendor lock‑in.
Product Framework
Deeptics Studio Assemble robots/environments, author scenarios, manage versions, and licenses.
Asset Registry Manage asset identity, content hashes, semantic versioning, licensing policies, and compatibility mappings.
Orchestrator Handles job scheduling, node selection, retries, checkpointing, and proof collection.
Compute Nodes CPU/GPU executors with sandboxing, performance profiling, and isolation.
Marketplace Listings for assets, compute packages, and simulation outputs. Supports bundling and revenue sharing.
Analytics Hub Dashboards for performance evaluation, experiment comparison, and metric tracking.
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