APPLIED PROCEDURAL KINETICS

Structured task data unlocks new capabilities across the robotics development lifecycle

TRAINING DATA

High-quality procedural demonstrations for imitation learning and reinforcement learning. Use M-IDL procedures as:

  • Distillation targets for vision-language-action models
  • Chain-of-thought prompts for LLM planners
  • Reward shaping with explicit success criteria
  • Curriculum learning with structured task decomposition

Structured procedures reduce hallucination in LLM planners and enable zero-shot transfer to unseen appliances.

SIMULATION

Ground truth task specifications for physics-based simulators. M-IDL provides:

  • Task definitions for Isaac Sim, RoboCasa, MuJoCo, iGibson
  • Goal conditions and valid action sequences
  • Constraint parameters (forces, torques, clearances)
  • Success/failure criteria for automated evaluation

Replace synthetic tasks with real manufacturer procedures. Sim-to-real transfer improves when simulation matches real-world task structure.

PLANNING

Task and motion planning with explicit procedural knowledge. M-IDL exports directly to:

  • PDDL domains with typed predicates and actions
  • Behavior Trees with pre/postcondition guards
  • Hierarchical Task Networks with method decomposition

Reduce search space by 10-100x. Improve plan reliability with manufacturer-validated action sequences and explicit failure recovery paths.

SAFETY

Formal verification of robot behaviors against manufacturer specifications. M-IDL encodes:

  • Hazard annotations per step (pinch points, hot surfaces, electrical)
  • Constraint validation (max forces, required clearances)
  • Failure propagation (what breaks if step X fails)
  • Recovery procedures (manufacturer-specified fixes)

When regulators ask "why did your robot do that?" — you'll have the audit trail. Explainable, verifiable, compliant.

INTEGRATION WORKFLOW

01

INGEST

Query our API for specific appliance models or task categories. Receive structured M-IDL documents with full procedural specifications.

02

TRANSFORM

Convert M-IDL to your target format (PDDL, ROS2, custom). Use our reference implementations or build custom transformers.

03

EXECUTE

Deploy procedural knowledge in your robotics stack. Monitor execution, collect feedback, and iterate on task specifications.

WHO USES THIS

FOUNDATION MODEL TEAMS

Training VLAs and robot transformers on structured task data. Reduce hallucination, improve zero-shot generalization.

SIMULATION ENGINEERS

Building realistic task environments in Isaac Sim, RoboCasa, MuJoCo. Ground synthetic data in real procedures.

PLANNING RESEARCHERS

Task and motion planning with explicit domain models. PDDL, HTN, behavior tree integration.

SAFETY & COMPLIANCE

Formal verification against manufacturer specs. Audit trails for deployment in human environments.