Active Inference Robotics Skill (Second-Order) Skill

Active Inference Robotics Skill (Second-Order)

"The agent's job is to predict its actions by predicting its sensations." — Patrick Kenny

Trigger Conditions

User asks about bridging active inference with robot control
Questions about predictive coding in locomotion policies
Connecting KL divergence minimization to RL training
Mean field approximation in robotics state estimation
Sim2Real as inference about future observations

Overview

Second-order skill synthesizing Patrick Kenny's discrete active inference framework with K-Scale's JAX/MuJoCo robotics stack. This skill emerges from the constructive collision between:

Active Inference Institute (ActInf ModelStream 019.1, Jan 2025)
K-Scale Labs (ksim, kos, kinfer ecosystem)
MuJoCo Playground (DeepMind's sim2real framework)

The Constructive Collision

┌─────────────────────────────────────────────────────────────────────────────┐
│  CONSTRUCTIVE COLLISION: Two Threads Converging                              │
│                                                                              │
│  Thread A: Patrick Kenny (Nov 2025)                                          │
│  ════════════════════════════════════                                        │
│  "Active inference can be formulated as constrained KL divergence           │
│   minimization solved by standard mean field methods"                        │
│                                                                              │
│  Key insight: Expected Free Energy ≈ KL Divergence + Entropy Regularizer    │
│                                                                              │
│  Thread B: K-Scale Labs (2024-2025)                                          │
│  ═══════════════════════════════════                                         │
│  "RL-based closed-loop control using policies trained in simulation         │
│   has firmly won as the best way of achieving real-time control"            │
│                                                                              │
│  Key insight: Stateless vs Stateful behaviors as pure/coalgebraic semantics │
│                                                                              │
│  COLLISION POINT: Both minimize surprise about future observations          │
│  ══════════════════════════════════════════════════════════════════         │
│                                                                              │
│       Active Inference              Robotics RL                              │
│       ────────────────              ──────────                               │
│       Predictive Distribution  ←→   Policy π(a|s)                           │
│       Hidden Markov Model      ←→   MDP/POMDP                                │
│       Mean Field Updates       ←→   PPO Gradient Steps                       │
│       Variational Free Energy  ←→   Policy Loss                              │
│       Expected Free Energy     ←→   Value Function + Entropy                 │
│       Perception/Action Loop   ←→   Observation/Action Loop                  │
│                                                                              │
└─────────────────────────────────────────────────────────────────────────────┘

Kenny's Key Contribution

From arXiv:2511.20321:

Perception/Action Divergence = VFE(past) + KL(future states)

Where:
- VFE(past) = Standard variational free energy on observed history
- KL(future) = Divergence of predictive distribution from HMM

This differs from Expected Free Energy by an ENTROPY REGULARIZER:
  EFE ≈ Pragmatic Value + Mutual Information
  PAD ≈ Pragmatic Value + Entropy(Q)

Why Entropy Regularization Matters for Robotics

# In ksim PPO training, entropy bonus prevents policy collapse:
loss = policy_loss + value_loss - entropy_coef * entropy

# Kenny's formulation shows this is NOT ad-hoc but principled:
# Entropy regularizer = not being overconfident about predictions
# Biological rationale: know limitations of future predictions

Mapping to ksim Architecture

| Active Inference Concept | ksim Implementation | |--------------------------|---------------------| | Hidden Markov Model | PhysicsEngine (MJX/MuJoCo) | | Observation distribution | Observation.observe(state) | | State inference Q(s) | Critic.forward(obs, carry) | | Action inference Q(a) | Actor.forward(obs, carry) | | Mean field factorization | Independent Q(s_t) per timestep | | Predictive distribution | Policy rollout trajectory | | VFE minimization | PPO policy gradient | | EFE/PAD minimization | Value function + entropy bonus |

Second-Order Behavior Types

1. Reflexive Control (Kenny's "Sufficient" Model)

# Agent predicts proprioceptive sensations → fulfills reflexively
class ReflexiveController:
    """
    Kenny: "If the agent can successfully predict its future sensations,
    it can fulfill them unconsciously via motor reflexes."
    """
    def step(self, predicted_proprio: Array) -> Action:
        # Low-level PD control fulfills proprioceptive predictions
        return self.pd_controller(predicted_proprio, self.current_state)

2. Deliberative Planning (EFE Extension)

# When reflexive prediction fails, engage deliberative inference
class DeliberativeController:
    """
    Extends reflexive control with policy search over trajectories.
    This is where EFE differs from Kenny's PAD formulation.
    """
    def plan(self, beliefs: Distribution, horizon: int) -> Policy:
        # Tree search over policies weighted by expected free energy
        for policy in self.policy_space:
            efe = self.expected_free_energy(beliefs, policy, horizon)
            # EFE includes mutual information (curiosity/exploration)
            # PAD would use entropy instead (uncertainty awareness)

3. Hierarchical Composition

Level 3: Goal Selection (minimize long-horizon EFE)
    ↓ sets reference for
Level 2: Trajectory Planning (predictive distribution)
    ↓ sets reference for  
Level 1: Reflexive Execution (fulfill proprio predictions)
    ↓ actuates
Level 0: Motor Primitives (PD control, actuator dynamics)

GF(3) Balanced Quad

active-inference (0) ⊗ kscale-ksim (0) ⊗ mujoco-playground (0) = 0 ✓

All three are ERGODIC — coordination/infrastructure skills.
This is a "resonant triad" where all components coordinate.

For generation (+1), add: skill-creator, algorithmic-art
For verification (-1), add: sheaf-cohomology, code-review

Skill Colors (drand seed 12005093902789493003)

| Skill | Trit | Color | Role | |-------|------|-------|------| | active-inference | 0 | #DF8D0F | Coordination (theory) | | kscale-ksim | 0 | #25BC3D | Coordination (simulation) | | mujoco-playground | 0 | #93DBDA | Coordination (framework) |

2-3-5-7 Prime Sieve Experts

Applying prime-indexed refinement to identify domain experts:

| Prime | Expert | Domain | Key Contribution | |-------|--------|--------|------------------| | 2 | Patrick Kenny | Active Inference | Mean field formulation, PAD criterion | | 3 | Thomas Parr | Active Inference | 2022 textbook, EFE derivation | | 5 | Ben Bolte | K-Scale | ksim architecture, open-source humanoids | | 7 | Karl Friston | Free Energy Principle | FEP foundations, continuous formulation | | 11 | (DeepMind team) | MuJoCo Playground | MJX, sim2real zero-shot | | 13 | Wesley Maa | K-Scale | Tooling, visualization |

Mutual Awareness

This skill references and is referenced by:

depends_on:
  - kscale-ksim        # Simulation implementation
  - kscale-ecosystem   # Hardware context
  - mujoco-playground  # Framework foundation
  
referenced_by:
  - cognitive-superposition  # Team mental models
  - parametrised-optics-cybernetics  # Category theory bridge
  - reafference-corollary-discharge  # Sensorimotor prediction

Implementation Pattern

# Unified Active Inference + RL Training Loop
class ActiveInferenceTrainer:
    """
    Combines Kenny's PAD criterion with ksim's PPO.
    """
    def __init__(self, hmm: PhysicsEngine, config: Config):
        self.hmm = hmm
        self.actor = Actor(config)
        self.critic = Critic(config)
        
    def perception_action_divergence(
        self, 
        observations: Array,  # O_{1:t} (past)
        q_future: Distribution  # Q(S_{t+1:T}, O_{t+1:T})
    ) -> Scalar:
        """
        Kenny's PAD = VFE(past) + KL(future states from HMM)
        """
        # Past: standard VFE on observation history
        vfe_past = self.variational_free_energy(observations)
        
        # Future: KL divergence of predicted states from HMM
        # Note: Observable emissions cancel out in future KL
        kl_future = self.kl_future_states(q_future, self.hmm)
        
        return vfe_past + kl_future
    
    def train_step(self, trajectory: Trajectory) -> Metrics:
        # PPO updates approximate mean field coordinate ascent
        # Entropy bonus provides Kenny's regularization
        return ppo_update(
            self.actor, 
            self.critic, 
            trajectory,
            entropy_coef=0.01  # ← The regularizer!
        )

References

ACSet Schema

@present SchActiveInferenceRobotics(FreeSchema) begin
    # Objects
    HMM::Ob           # Hidden Markov Model (generative model)
    State::Ob         # Latent state
    Observation::Ob   # Sensory observation
    Action::Ob        # Motor command
    Policy::Ob        # Action sequence
    
    # Morphisms (inference)
    perceive::Hom(Observation, State)    # Perception: O → S
    predict::Hom(State, Observation)     # Prediction: S → O
    act::Hom(State, Action)              # Action selection: S → A
    transition::Hom(State × Action, State)  # Dynamics: S × A → S'
    
    # Attributes
    FreeEnergy::AttrType
    vfe::Attr(State, FreeEnergy)         # Variational free energy
    efe::Attr(Policy, FreeEnergy)        # Expected free energy
    pad::Attr(Policy, FreeEnergy)        # Perception/action divergence
    
    # The key relationship (Kenny's contribution):
    # pad ≈ efe + entropy_regularizer
end

Agent Skills: Active Inference Robotics Skill (Second-Order)

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