Spec-Driven Development SDD — Humanoid Training Specification (Training SDD)

Anh Nguyen

— 1 min read
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(Human‑authored, governs what the robot is allowed to learn)

1. Purpose

Defines the data, simulation, reward shaping, safety envelopes, and validation gates for training humanoid locomotion, manipulation, perception, and human‑interaction policies.

Ensures the robot only learns safe, reversible, bounded behaviors.

2. Scope

Applies to:

  • reinforcement learning
  • imitation learning
  • supervised learning
  • multi‑modal perception training
  • simulation‑to‑real transfer
  • human‑interaction learning

3. Training Data Requirements

Training data must:

  • represent real‑world conditions
  • include diverse human poses
  • include multi‑modal sensor data
  • include failure cases
  • include domain randomization

Training data must not include:

  • unsafe human behaviors
  • unbounded force application
  • unbounded trajectories
  • irreversible actions
  • corrupted sensor data

4. Negative Requirements

Training must never:

  • learn policies that exceed torque limits
  • learn unsafe trajectories
  • learn unsafe human‑interaction patterns
  • learn irreversible actions
  • learn from invalid data
  • learn from unbounded exploration

5. Safety Invariants (Training‑Time)

Must always hold:

  • center of mass stability
  • force‑torque envelopes
  • thermal limits
  • collision detection
  • valid sensor fusion
  • reward penalties for unsafe behavior

6. Simulation Requirements

Simulation must include:

  • accurate physics
  • actuator latency
  • sensor noise
  • domain randomization
  • perturbation events
  • safety envelope enforcement

Simulation must not allow:

  • unrealistic physics
  • unbounded exploration
  • irreversible actions

7. Reward Shaping Requirements

Rewards must:

  • encourage stability
  • encourage safe manipulation
  • encourage human‑aware motion
  • penalize unsafe force
  • penalize unsafe proximity
  • penalize irreversible actions

8. Policy Validation Requirements

Policies must pass:

  • stability validation
  • manipulation validation
  • human‑interaction validation
  • collision‑avoidance validation
  • thermal‑load validation
  • drift‑detection validation

9. Deployment Gates

A policy may only be deployed if:

  • all invariants pass
  • all validation tests pass
  • all evidence logs complete
  • dual approval granted

10. Evidence Requirements

Training must produce:

  • reward curves
  • loss curves
  • invariant violation logs
  • drift metrics
  • trajectory samples
  • force‑torque samples
  • thermal profiles

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