vST for Robotics and Control Policies#

Validation‑Space‑Time Framework for High‑Dimensional Control Systems#

This artifact defines a substrate‑level framework for analyzing, validating, and comparing robotics and control policies using the Validation‑Space‑Time (vST) system and the 1024D dimensional substrate. It provides a structured, invariant‑preserving method for interpreting policy behavior, latent‑space dynamics, scaling behavior, and cross‑version drift in robotic controllers and reinforcement‑learning (RL) policies.

The goal is to offer a reproducible, model‑agnostic substrate for understanding control‑policy behavior across time, action spaces, and latent regimes.

1. Purpose#

Robotics and control‑policy systems operate in high‑dimensional latent spaces and exhibit:

• stable and unstable control regimes
• transitions between behavioral phases
• scaling‑law behavior across policy sizes and architectures
• drift across training runs, fine‑tuning, or hardware changes
• projection‑compatible structure for interpretability

This artifact applies the Resonance Substrate Model (RSM) and vST validation layers to:

• classify latent‑space regimes
• analyze scaling behavior across policy architectures
• detect drift across training checkpoints or hardware configurations
• map coherence surfaces in policy latent space
• project high‑dimensional policy states into 3D–9D triadic cores

The result is a unified, interpretable substrate for robotics and control‑policy behavior.

2. Contents#

This directory contains:

• substrate_definition.md
  Defines the control‑policy substrate, primitives, and latent‑space structure.

• policy_latent_regimes.md
  Describes stable, transitional, and dispersed regimes in policy dynamics.

• scaling_behavior_rl_policies.md
  Maps policy scaling laws onto the 3D–1024D dimensional ladder.

• projection_and_policy_alignment.md
  Defines invertible projection from high‑dimensional policy states into triadic cores.

• validation_layers_vst_rl.md
  Extends vST (V₁–V₄) to robotics and RL‑policy behavior.

• drift_detection_rl.md
  Provides a substrate‑level framework for detecting cross‑version drift.

• examples/
  Demonstrations of latent‑trajectory analysis, projection, and drift detection.

• appendix/
  Terminology and references.

Each file is self‑contained and designed for clarity, reproducibility, and cross‑policy comparison.

3. Scope#

This artifact is:

• model‑agnostic
  Works with any control‑policy architecture (RL, MPC, imitation learning, hybrid controllers).

• robot‑agnostic
  Applies to manipulators, mobile robots, drones, legged robots, and simulated agents.

• method‑independent
  Compatible with model‑free RL, model‑based RL, classical control, and hybrid systems.

• substrate‑aligned
  Uses the same primitives, invariants, and validation layers as the rest of the RSM canon.

4. Intended Use#

This framework supports:

• latent‑space analysis
• cross‑checkpoint comparison
• drift detection
• scaling‑law evaluation
• regime‑transition mapping
• policy‑stability diagnostics
• reproducible inference and controller analysis

It is not a performance benchmark or robotics tutorial.
It is a substrate‑level interpretability and validation framework.

5. Relationship to Other Artifacts#

This artifact extends:

• Dimensional Substrate Structures (3D–1024D substrate)
• Validation‑Space‑Time (vST)
• Triadic Dimensional Cores (3D–9D)

It parallels:

• vST for Large Language Models
• vST for Protein Language Models
• vST for Scientific Simulators
• vST for Robotics and Control Policies (this artifact)
• vST for Multi‑Model Alignment

Each artifact stands alone but shares a common substrate grammar.

6. Citation#

A CITATION.cff file is included for formal citation.
A zenodo.json file is provided for DOI‑ready metadata.

7. License#

Released under the MIT License.