The Year of Physical AI: NVIDIA Cosmos 3 and Figure 03 Ignite the Intelligence Revolution

// Humanoid Robot MPC Motion Control - Simplified Implementation
// Model Predictive Control for walking and manipulation tasks
package main

import (
	"fmt"
	"math"
	"math/rand"
	"time"
)

// ============ Kinematics Model ============

// RobotState defines the full robot state
type RobotState struct {
	// Center of Mass position
	COMX, COMY, COMZ float64
	// Center of Mass velocity
	COMVX, COMVY, COMVZ float64
	// Body orientation (Euler angles)
	Roll, Pitch, Yaw float64
	// Body angular velocity
	RollVel, PitchVel, YawVel float64
	// Joint angles (44 DoF)
	JointAngles []float64
	// Joint velocities
	JointVels []float64
}

// MPCConfig MPC controller configuration
type MPCConfig struct {
	PredictionHorizon int     // Prediction horizon (steps)
	ControlHorizon    int     // Control horizon (steps)
	Dt                float64 // Time step (s)
	
	// Weight matrices
	QPos      float64 // Position tracking weight
	QVel      float64 // Velocity tracking weight
	QAngular  float64 // Attitude tracking weight
	RControl  float64 // Control input weight
	RDelta    float64 // Control rate weight
	
	// Constraints
	MaxJointTorque   float64
	MaxJointVelocity float64
	JointLimitMin    []float64
	JointLimitMax    []float64
}

// MPCController the MPC solver
type MPCController struct {
	Config     MPCConfig
	JointNum   int
	prevControl []float64
	optSolution []float64
}

func NewMPCController(config MPCConfig, jointNum int) *MPCController {
	return &MPCController{
		Config:      config,
		JointNum:    jointNum,
		prevControl: make([]float64, jointNum),
		optSolution: make([]float64, jointNum*config.ControlHorizon),
	}
}

// ============ Dynamics Model ============

// FloatingBaseDynamics using LIPM (Linear Inverted Pendulum Model)
type FloatingBaseDynamics struct {
	Mass       float64
	Height     float64
	Gravity    float64
	FootRadius float64
}

func NewFloatingBaseDynamics(mass, height float64) *FloatingBaseDynamics {
	return &FloatingBaseDynamics{
		Mass:       mass,
		Height:     height,
		Gravity:    9.81,
		FootRadius: 0.1,
	}
}

// LIPMDynamics LIPM model dynamics
// State space: [com_x, com_y, com_vx, com_vy]^T
// Control: [cop_x, cop_y]^T (center of pressure)
func (dyn *FloatingBaseDynamics) LIPMDynamics(state [4]float64, control [2]float64, dt float64) [4]float64 {
	// x_ddot = g/h * (x - p_x), y_ddot = g/h * (y - p_y)
	g_h := dyn.Gravity / dyn.Height
	
	nextState := [4]float64{}
	nextState[0] = state[0] + state[2]*dt + 0.5*g_h*(state[0]-control[0])*dt*dt
	nextState[1] = state[1] + state[3]*dt + 0.5*g_h*(state[1]-control[1])*dt*dt
	nextState[2] = state[2] + g_h*(state[0]-control[0])*dt
	nextState[3] = state[3] + g_h*(state[1]-control[1])*dt
	return nextState
}

// ============ Motion Planning ============

type FootstepPlanner struct {
	StepLength     float64
	StepWidth      float64
	StepHeight     float64
	SwingDuration  float64
	StanceDuration float64
	CycleTime      float64
}

func NewFootstepPlanner(stepLength, stepWidth float64) *FootstepPlanner {
	return &FootstepPlanner{
		StepLength:     stepLength,
		StepWidth:      stepWidth,
		StepHeight:     0.05,
		SwingDuration:  0.4,
		StanceDuration: 0.6,
		CycleTime:      1.0,
	}
}

type Footstep struct {
	X, Y, Z    float64
	Yaw        float64
	IsLeftFoot bool
	Phase      float64
}

func (fp *FootstepPlanner) PlanFootsteps(numSteps int, direction float64) []Footstep {
	steps := make([]Footstep, numSteps)
	for i := 0; i < numSteps; i++ {
		isLeft := i%2 == 0
		x := float64(i) * fp.StepLength * math.Cos(direction)
		y := (float64(i) + 1.0) * fp.StepWidth * 0.5
		if !isLeft { y = -y }
		steps[i] = Footstep{X: x, Y: y, Yaw: direction, IsLeftFoot: isLeft, Phase: float64(i) / float64(numSteps)}
	}
	return steps
}

// ============ MPC Solver (Simplified) ============

func (mpc *MPCController) SolveMPC(
	currentState *RobotState,
	targetTrajectory []RobotState,
	dynamics *FloatingBaseDynamics,
) ([]float64, float64) {
	start := time.Now()
	horizon := mpc.Config.PredictionHorizon
	controlDim := mpc.JointNum

	controlSeq := make([]float64, controlDim*mpc.Config.ControlHorizon)
	copy(controlSeq, mpc.prevControl)

	lipmState := [4]float64{currentState.COMX, currentState.COMY, currentState.COMVX, currentState.COMVY}

	for t := 0; t < horizon; t++ {
		var refState [4]float64
		if t < len(targetTrajectory) {
			refState = [4]float64{
				targetTrajectory[t].COMX, targetTrajectory[t].COMY,
				targetTrajectory[t].COMVX, targetTrajectory[t].COMVY,
			}
		}

		copX := lipmState[0] + lipmState[2]*dyn.Height/dyn.Gravity*0.5
		copY := lipmState[1] + lipmState[3]*dyn.Height/dyn.Gravity*0.5
		kpGain := 10.0
		copX += kpGain * (refState[0] - lipmState[0])
		copY += kpGain * (refState[1] - lipmState[1])

		control := [2]float64{copX, copY}
		lipmState = dyn.LIPMDynamics(lipmState, control, mpc.Config.Dt)

		if t < mpc.Config.ControlHorizon {
			for j := 0; j < mpc.JointNum; j++ {
				idx := t*controlDim + j
				targetAngle := refState[0] * 0.1 * math.Sin(float64(j)*0.5)
				currentAngle := currentState.JointAngles[j]
				kp, kd := 50.0, 5.0
				torque := kp*(targetAngle-currentAngle) - kd*currentState.JointVels[j]
				if torque > mpc.Config.MaxJointTorque { torque = mpc.Config.MaxJointTorque }
				if torque < -mpc.Config.MaxJointTorque { torque = -mpc.Config.MaxJointTorque }
				controlSeq[idx] = torque
			}
		}
	}

	mpc.prevControl = controlSeq[:controlDim]
	return controlSeq[:controlDim], time.Since(start).Seconds() * 1000
}

func main() {
	rand.Seed(time.Now().UnixNano())
	fmt.Println("===== Humanoid Robot MPC Motion Control =====")
	fmt.Println("Reference: Figure 03 / Tesla Optimus Control Systems")
	fmt.Println()

	jointNum := 44
	initialState := &RobotState{
		COMX: 0, COMY: 0, COMZ: 0.95,
		JointAngles: make([]float64, jointNum),
		JointVels:   make([]float64, jointNum),
	}
	fmt.Printf("[1/5] Robot initialized: 60kg, %d DoF\n", jointNum)

	mpcConfig := MPCConfig{
		PredictionHorizon: 50, ControlHorizon: 10, Dt: 0.01,
		QPos: 100.0, QVel: 10.0, QAngular: 50.0, RControl: 0.1, RDelta: 0.01,
		MaxJointTorque: 200.0, MaxJointVelocity: 10.0,
	}
	mpc := NewMPCController(mpcConfig, jointNum)
	dyn := NewFloatingBaseDynamics(60.0, 0.95)
	planner := NewFootstepPlanner(0.4, 0.2)
	steps := planner.PlanFootsteps(10, 0.0)
	fmt.Printf("[2-4/5] MPC configured | LIPM dynamics | %d steps planned\n", len(steps))

	// Run MPC
	targetTrajectory := make([]RobotState, mpcConfig.PredictionHorizon)
	for t := 0; t < mpcConfig.PredictionHorizon; t++ {
		targetTrajectory[t] = RobotState{
			COMX: 0.4 * float64(t) * mpcConfig.Dt,
			COMY: 0.05 * math.Sin(float64(t)*0.5),
			COMZ: 0.95, COMVX: 0.4,
			COMVY: 0.05 * math.Cos(float64(t)*0.5) * 0.5,
		}
	}

	totalSolveTime := 0.0
	const iterations = 100
	fmt.Println("[5/5] Running MPC control loop...")
	for i := 0; i < iterations; i++ {
		control, solveTime := mpc.SolveMPC(initialState, targetTrajectory, dyn)
		totalSolveTime += solveTime
		initialState.COMX += 0.4 * mpcConfig.Dt
		initialState.COMVX = 0.4
		if i%20 == 0 { fmt.Printf("  Step %d: COM (%.3f, %.3f), solve %.2fms\n", i, initialState.COMX, initialState.COMY, solveTime); _ = control }
	}

	fmt.Println("\n===== MPC Performance =====")
	fmt.Printf("  Avg solve time: %.3fms\n", totalSolveTime/float64(iterations))
	fmt.Printf("  Control frequency: %.0f Hz\n", 1000.0/(totalSolveTime/float64(iterations)))
	fmt.Printf("  Total distance: %.2fm\n", initialState.COMX)
	fmt.Println("\n===== Figure 03 Comparison =====")
	fmt.Println("  Helix 02: 1kHz joint commands, 44 DoF")
	fmt.Println("  MPC: 100-1000Hz (this Go version ~100Hz, C++/CUDA can reach 1kHz)")
}

// VLA End-to-End Inference Pipeline Simulation
// Visual Encoding → Language Understanding → Action Planning → Execution Feedback
// Reference: NVIDIA Alpamayo 2 Super / Figure Helix Architecture
package main

import (
	"fmt"
	"math"
	"math/rand"
	"time"
)

// ===== Visual Encoding Layer =====

type ImageFeature []float64

type VisualEncoder struct {
	InputChannels int
	FeatureDim    int
	NumViews      int
	Resolution    [2]int
}

func NewVisualEncoder() *VisualEncoder {
	return &VisualEncoder{
		InputChannels: 3, FeatureDim: 4096,
		NumViews: 6, Resolution: [2]int{1280, 720},
	}
}

func (ve *VisualEncoder) Encode(rawPixels []float64) ([]ImageFeature, float64) {
	start := time.Now()
	features := make([]ImageFeature, ve.NumViews)
	for v := 0; v < ve.NumViews; v++ {
		f := make(ImageFeature, ve.FeatureDim)
		for i := 0; i < ve.FeatureDim; i++ {
			f[i] = math.Tanh(float64(rand.Intn(1000))/1000.0*2.0 - 1.0)
		}
		features[v] = f
	}
	return features, time.Since(start).Seconds() * 1000
}

// ===== Language Reasoning Layer =====

type LanguageReasoner struct {
	HiddenDim int
	NumLayers int
	VocabSize int
}

func NewLanguageReasoner() *LanguageReasoner {
	return &LanguageReasoner{HiddenDim: 8192, NumLayers: 56, VocabSize: 128000}
}

type Instruction struct {
	RawText  string
	TokenIDs []int
}

type ReasonedPlan struct {
	GoalDescription string
	SubTasks        []string
	Confidence      float64
	LatencyMs       float64
}

func (lr *LanguageReasoner) Reason(visualFeatures []ImageFeature, instruction Instruction) ReasonedPlan {
	start := time.Now()
	plan := ReasonedPlan{
		GoalDescription: "Grasp the blue box on shelf level 3 and place it on the conveyor belt",
		SubTasks: []string{
			"1. Locate target object (visual localization)",
			"2. Plan grasp trajectory (inverse kinematics)",
			"3. Execute grasp (force control adjustment)",
			"4. Place on conveyor (trajectory planning)",
		},
		Confidence: 0.92,
	}
	plan.LatencyMs = time.Since(start).Seconds() * 1000
	return plan
}

// ===== Action Planning Layer =====

type ActionPlanner struct {
	JointNum    int
	ActionDim   int
	ControlFreq int
	UseMPC      bool
}

func NewActionPlanner() *ActionPlanner {
	return &ActionPlanner{JointNum: 44, ActionDim: 64, ControlFreq: 1000, UseMPC: true}
}

type JointCommand struct {
	JointIndex int
	TargetPos  float64
	TargetVel  float64
	Torque     float64
	Timestamp  int64
}

type ActionSequence struct {
	Commands    [][]JointCommand
	Horizon     int
	TotalTime   float64
	SuccessRate float64
}

func (ap *ActionPlanner) PlanAction(plan ReasonedPlan, sceneContext map[string]float64) ActionSequence {
	start := time.Now()
	horizon := 500
	dt := 1.0 / float64(ap.ControlFreq)

	seq := ActionSequence{Commands: make([][]JointCommand, horizon), Horizon: horizon}
	for t := 0; t < horizon; t++ {
		commands := make([]JointCommand, ap.JointNum)
		for j := 0; j < ap.JointNum; j++ {
			phase := float64(t) * dt * 2.0 * math.Pi * 0.5
			commands[j] = JointCommand{
				JointIndex: j,
				TargetPos:  0.5 * math.Sin(phase+float64(j)*0.1),
				TargetVel:  0.5 * math.Cos(phase+float64(j)*0.1) * 2.0 * math.Pi * 0.5,
				Torque:     computeTorque(0.5*math.Sin(phase+float64(j)*0.1), 0.5*math.Cos(phase+float64(j)*0.1)*2.0*math.Pi*0.5, sceneContext),
				Timestamp:  int64(t),
			}
		}
		seq.Commands[t] = commands
	}
	seq.TotalTime = time.Since(start).Seconds() * 1000
	seq.SuccessRate = 0.97
	return seq
}

// ===== Execution Feedback Layer =====

type FeedbackCollector struct {
	SensorFusionEnabled bool
	FeedbackDim         int
}

func NewFeedbackCollector() *FeedbackCollector {
	return &FeedbackCollector{SensorFusionEnabled: true, FeedbackDim: 128}
}

type ExecutionFeedback struct {
	ActualPos      []float64
	ActualVel      []float64
	ErrorPos       []float64
	ForceReadings  []float64
	Stability      float64
	TaskProgress   float64
	RecoveryNeeded bool
}

func (fc *FeedbackCollector) CollectFeedback(seq ActionSequence) ExecutionFeedback {
	fb := ExecutionFeedback{
		ActualPos: make([]float64, 44), ActualVel: make([]float64, 44),
		ErrorPos: make([]float64, 44), ForceReadings: make([]float64, 16),
		TaskProgress: 0.85, Stability: 0.93,
	}
	for j := 0; j < 44; j++ {
		fb.ActualPos[j] = seq.Commands[0][j].TargetPos + (rand.Float64()-0.5)*0.02
		fb.ActualVel[j] = seq.Commands[0][j].TargetVel + (rand.Float64()-0.5)*0.01
		fb.ErrorPos[j] = math.Abs(fb.ActualPos[j] - seq.Commands[0][j].TargetPos)
	}
	for i := 0; i < 16; i++ { fb.ForceReadings[i] = 0.5 + rand.Float64()*2.0 }
	fb.RecoveryNeeded = false
	return fb
}

func computeTorque(targetPos, targetVel float64, ctx map[string]float64) float64 {
	return 100.0*targetPos + 10.0*targetVel + ctx["gravity_comp"]*9.8
}

func findMax(arr []float64) float64 {
	max := arr[0]
	for _, v := range arr { if v > max { max = v } }
	return max
}

func findMin(arr []float64) float64 {
	min := arr[0]
	for _, v := range arr { if v < min { min = v } }
	return min
}

func main() {
	rand.Seed(time.Now().UnixNano())
	fmt.Println("===== VLA End-to-End Inference Pipeline =====")
	fmt.Println()

	encoder := NewVisualEncoder()
	reasoner := NewLanguageReasoner()
	planner := NewActionPlanner()
	fbCollector := NewFeedbackCollector()

	fmt.Println("[1/5] Visual Encoding: Processing 6 surround-view cameras...")
	pixels := make([]float64, 1280*720*3)
	features, encodeTime := encoder.Encode(pixels)
	fmt.Printf("  Feature dim: %d/view, Features: %d, Latency: %.2fms\n",
		encoder.FeatureDim, len(features), encodeTime)

	fmt.Println("[2/5] Language Understanding...")
	instruction := Instruction{
		RawText:  "Grasp the blue box on shelf level 3 and place on conveyor",
		TokenIDs: make([]int, 128),
	}
	for i := range instruction.TokenIDs { instruction.TokenIDs[i] = rand.Intn(128000) }
	plan := reasoner.Reason(features, instruction)
	fmt.Printf("  Goal: %s\n", plan.GoalDescription)
	fmt.Printf("  Subtasks: %v\n", plan.SubTasks)
	fmt.Printf("  Confidence: %.2f%%, Latency: %.2fms\n", plan.Confidence*100, plan.LatencyMs)

	fmt.Println("[3/5] Action Planning: Generating joint motion sequence...")
	sceneCtx := map[string]float64{"gravity_comp": 1.0, "load_weight": 2.5, "friction": 0.3}
	actionSeq := planner.PlanAction(plan, sceneCtx)
	fmt.Printf("  Horizon: %d steps (%.1fms)\n",
		actionSeq.Horizon, float64(actionSeq.Horizon)/float64(planner.ControlFreq)*1000)
	fmt.Printf("  Joints: %d, Generation time: %.2fms\n", planner.JointNum, actionSeq.TotalTime)

	fmt.Println("[4/5] Execution & Feedback...")
	fb := fbCollector.CollectFeedback(actionSeq)
	fmt.Printf("  Progress: %.1f%%, Stability: %.2f\n", fb.TaskProgress*100, fb.Stability)
	fmt.Printf("  Max pos error: %.4f rad\n", findMax(fb.ErrorPos))
	fmt.Printf("  Force range: %.2f - %.2f N\n", findMin(fb.ForceReadings), findMax(fb.ForceReadings))

	fmt.Println("[5/5] VLA Closed-Loop Feedback...")
	if fb.RecoveryNeeded {
		fmt.Println("  Recovery needed: re-planning trajectory")
	} else {
		fmt.Println("  Execution normal: errors within tolerance")
	}

	fmt.Println("\n===== End-to-End Latency Analysis =====")
	fmt.Printf("  Visual Encoding: %.2fms\n", encodeTime)
	fmt.Printf("  Language Reasoning: %.2fms\n", plan.LatencyMs)
	fmt.Printf("  Action Planning: %.2fms\n", actionSeq.TotalTime)
	fmt.Printf("  Total Latency: %.2fms\n", encodeTime+plan.LatencyMs+actionSeq.TotalTime)
	fmt.Printf("  Control Frequency: %d Hz\n", planner.ControlFreq)
	fmt.Printf("  Joint Commands/Step: %d\n", planner.JointNum)
	fmt.Printf("  Success Rate: %.1f%%\n", actionSeq.SuccessRate*100)

	fmt.Println("\n===== Architecture Scale Comparison =====")
	fmt.Println("Alpamayo 2 Super: 32B params, 360° perception, 1kHz control")
	fmt.Println("Figure 03 Helix 02: 44 DoF, end-to-end VLA, shared weights")
	fmt.Println("Cosmos 3 Super: 32-64B params, MoT, omnimodal generation")
}

"""
Cosmos 3 World Model Sampling and Physics Simulation
Based on NVIDIA open-source cosmos-framework
Demonstrates: Action-conditioned world model inference
"""
import torch
import numpy as np
from typing import Optional, Tuple
import math

# ==================== 1. Configuration ====================

class Cosmos3Config:
    """Cosmos 3 model configuration"""
    def __init__(self, model_size: str = "nano"):
        if model_size == "nano":
            self.hidden_dim = 4096
            self.num_layers = 32
            self.num_heads = 32
            self.vocab_size = 128000
        elif model_size == "super":
            self.hidden_dim = 8192
            self.num_layers = 56
            self.num_heads = 64
            self.vocab_size = 128000
        else:
            raise ValueError(f"Unknown model size: {model_size}")
        
        self.max_seq_len = 16384
        self.vae_latent_dim = 64
        self.action_dim = 44
        self.audio_dim = 80
        self.reasoner_layers = self.num_layers
        self.generator_layers = self.num_layers
        self.diffusion_steps = 50

class Cosmos3Tokenizer:
    """Multimodal tokenizer (simulated)"""
    def __init__(self, config: Cosmos3Config):
        self.config = config
        self.text_tokenizer = {"type": "sentinel", "vocab_size": self.config.vocab_size}
        self.vae_encoder = {"type": "vae", "latent_dim": self.config.vae_latent_dim}
        self.action_encoder = {"type": "mlp", "input_dim": self.config.action_dim}
    
    def encode_text(self, text: str) -> torch.Tensor:
        seq_len = min(len(text) // 2 + 10, 256)
        return torch.randint(0, self.config.vocab_size, (1, seq_len))
    
    def encode_image(self, image: torch.Tensor) -> torch.Tensor:
        B, C, H, W = image.shape
        h, w = H // 8, W // 8
        return torch.randn(B, h * w, self.config.vae_latent_dim)
    
    def encode_video(self, video: torch.Tensor) -> torch.Tensor:
        B, T, C, H, W = video.shape
        t, h, w = T // 4, H // 8, W // 8
        return torch.randn(B, t * h * w, self.config.vae_latent_dim)

# ==================== 2. 3D Multimodal RoPE ====================

class Multimodal3DRoPE:
    """3D Multimodal Rotary Position Embedding"""
    def __init__(self, dim: int, max_time: int = 512, max_h: int = 64, max_w: int = 64):
        self.dim = dim
        assert dim % 6 == 0, "dim must be divisible by 6"
        self.dim_per_axis = dim // 3
        self._init_freqs()
    
    def _init_freqs(self):
        inv_freq_t = 1.0 / (10000 ** (torch.arange(0, self.dim_per_axis, 2) / self.dim_per_axis))
        inv_freq_h = 1.0 / (10000 ** (torch.arange(0, self.dim_per_axis, 2) / self.dim_per_axis))
        inv_freq_w = 1.0 / (10000 ** (torch.arange(0, self.dim_per_axis, 2) / self.dim_per_axis))
        self.inv_freq_t, self.inv_freq_h, self.inv_freq_w = inv_freq_t, inv_freq_h, inv_freq_w

# ==================== 3. MoT Dual-Tower Layer ====================

class MoTDecoderLayer(torch.nn.Module):
    """MoT decoder layer with Reasoner and Generator towers"""
    def __init__(self, config: Cosmos3Config):
        super().__init__()
        self.config = config
        
        # Reasoner tower (autoregressive)
        self.reasoner_self_attn = torch.nn.MultiheadAttention(
            config.hidden_dim, config.num_heads, batch_first=True
        )
        self.reasoner_norm1 = torch.nn.LayerNorm(config.hidden_dim)
        self.reasoner_ffn = torch.nn.Sequential(
            torch.nn.Linear(config.hidden_dim, config.hidden_dim * 4),
            torch.nn.GELU(),
            torch.nn.Linear(config.hidden_dim * 4, config.hidden_dim),
        )
        self.reasoner_norm2 = torch.nn.LayerNorm(config.hidden_dim)
        
        # Generator tower (diffusion)
        self.generator_cross_attn = torch.nn.MultiheadAttention(
            config.hidden_dim, config.num_heads, batch_first=True
        )
        self.generator_norm1 = torch.nn.LayerNorm(config.hidden_dim)
        self.generator_ffn = torch.nn.Sequential(
            torch.nn.Linear(config.hidden_dim, config.hidden_dim * 4),
            torch.nn.GELU(),
            torch.nn.Linear(config.hidden_dim * 4, config.hidden_dim),
        )
        self.generator_norm2 = torch.nn.LayerNorm(config.hidden_dim)
    
    def forward_reasoner(self, ar_hidden: torch.Tensor, causal_mask: torch.Tensor) -> torch.Tensor:
        attn_out, _ = self.reasoner_self_attn(ar_hidden, ar_hidden, ar_hidden, attn_mask=causal_mask, is_causal=True)
        ar_hidden = self.reasoner_norm1(ar_hidden + attn_out)
        ffn_out = self.reasoner_ffn(ar_hidden)
        return self.reasoner_norm2(ar_hidden + ffn_out)
    
    def forward_generator(self, dm_hidden: torch.Tensor, ar_hidden: torch.Tensor) -> torch.Tensor:
        kv = torch.cat([ar_hidden, dm_hidden], dim=1)
        attn_out, _ = self.generator_cross_attn(dm_hidden, kv, kv)
        dm_hidden = self.generator_norm1(dm_hidden + attn_out)
        ffn_out = self.generator_ffn(dm_hidden)
        return self.generator_norm2(dm_hidden + ffn_out)

# ==================== 4. World Model Inference Pipeline ====================

class Cosmos3WorldModel:
    """Cosmos 3 world model inference pipeline"""
    def __init__(self, config: Cosmos3Config):
        self.config = config
        self.tokenizer = Cosmos3Tokenizer(config)
        self.layers = torch.nn.ModuleList([MoTDecoderLayer(config) for _ in range(config.num_layers)])
    
    def generate_video(self,
                       text_prompt: str,
                       action_sequence: Optional[torch.Tensor] = None,
                       num_frames: int = 16) -> torch.Tensor:
        """
        Video generation (world model simulation)
        
        Args:
            text_prompt: Text description
            action_sequence: Action sequence (optional)
            num_frames: Number of frames to generate
            
        Returns:
            Generated video tensor [B, T, C, H, W]
        """
        print(f"\n[WorldModel] Starting world model inference")
        print(f"  Text prompt: '{text_prompt}'")
        print(f"  Frames: {num_frames}")
        print(f"  Action-conditioned: {'Yes' if action_sequence is not None else 'No'}")
        
        # 1. Encode input
        text_tokens = self.tokenizer.encode_text(text_prompt)
        ar_seq_len = text_tokens.shape[1]
        ar_hidden = torch.randn(1, ar_seq_len, self.config.hidden_dim)
        
        # 2. Generate DM subsequence
        dm_seq_len = num_frames * 16 * 16
        dm_hidden = torch.randn(1, dm_seq_len, self.config.hidden_dim)
        print(f"  AR seq length: {ar_seq_len}, DM seq length: {dm_seq_len}")
        
        # 3. Pass through MoT layers
        causal_mask = torch.triu(torch.full((ar_seq_len, ar_seq_len), float('-inf')), diagonal=1)
        
        for i, layer in enumerate(self.layers):
            ar_hidden = layer.forward_reasoner(ar_hidden, causal_mask)
            dm_hidden = layer.forward_generator(dm_hidden, ar_hidden)
            if i % 8 == 0: print(f"  MoT layer {i}/{len(self.layers)}: complete")
        
        # 4. Decode video (simplified: simulate diffusion denoising)
        B = dm_hidden.shape[0]
        noise = torch.randn(B, num_frames, 3, 128, 128)
        video = noise
        for step in range(self.config.diffusion_steps):
            alpha = math.cos((step / self.config.diffusion_steps) * math.pi / 2)
            video = alpha * video + (1 - alpha) * torch.randn_like(video)
            if step % 10 == 0: print(f"  Diffusion denoising: step {step}/{self.config.diffusion_steps}")
        
        print(f"  [WorldModel] Video generation complete: shape={video.shape}")
        return video


# ==================== 5. Action-Conditioned World Model ====================

class ActionConditionedWorldModel:
    """
    Action-conditioned world model
    Input: robot action sequence → Output: future world state (video)
    This is Cosmos 3's "forward dynamics" capability
    """
    def __init__(self, config: Cosmos3Config):
        self.config = config
        self.world_model = Cosmos3WorldModel(config)
    
    def forward_dynamics(self,
                         action_sequence: torch.Tensor,
                         num_predict_frames: int = 8) -> torch.Tensor:
        """Forward dynamics prediction: actions → future world state"""
        text_prompt = "Based on current observations and action sequence, predict future physical world state changes"
        return self.world_model.generate_video(text_prompt, action_sequence, num_predict_frames)
    
    def inverse_dynamics(self, video_sequence: torch.Tensor) -> torch.Tensor:
        """Inverse dynamics: video observation → inferred actions"""
        print(f"\n[InverseDynamics] Inferring action sequence from video")
        B, T = video_sequence.shape[:2]
        inferred_actions = torch.randn(B, T, self.config.action_dim)
        print(f"  Inferred actions: shape={inferred_actions.shape}")
        return inferred_actions


# ==================== 6. Main Demo ====================

def demo_world_model():
    """Cosmos 3 world model demonstration"""
    print("=" * 60)
    print("Cosmos 3 World Model - Physical AI Inference Demo")
    print("=" * 60)
    
    config = Cosmos3Config("nano")
    print(f"\n[Config] Model: Nano (8B)")
    print(f"  Hidden dim: {config.hidden_dim}, Layers: {config.num_layers}")
    
    # Scene 1: Text-to-Video
    print("\n" + "-" * 50)
    print("Scene 1: Text-to-Video Generation")
    print("-" * 50)
    model = Cosmos3WorldModel(config)
    video = model.generate_video(
        "A robot moving a blue box from shelf level 3 to a conveyor belt",
        num_frames=16
    )
    print(f"  Output: {video.shape}")
    
    # Scene 2: Forward Dynamics
    print("\n" + "-" * 50)
    print("Scene 2: Forward Dynamics - Action-Conditioned Prediction")
    print("-" * 50)
    awm = ActionConditionedWorldModel(config)
    action_seq = torch.randn(1, 10, config.action_dim)
    predicted = awm.forward_dynamics(action_seq, num_predict_frames=8)
    print(f"  Input actions: {action_seq.shape}")
    print(f"  Predicted frames: {predicted.shape}")
    
    # Scene 3: Inverse Dynamics
    print("\n" + "-" * 50)
    print("Scene 3: Inverse Dynamics - Video to Actions")
    print("-" * 50)
    video_seq = torch.randn(1, 16, 3, 256, 256)
    inferred = awm.inverse_dynamics(video_seq)
    
    # Scene 4: AlpaGym
    print("\n" + "-" * 50)
    print("Scene 4: AlpaGym Closed-Loop RL Training")
    print("-" * 50)
    print("""
    AlpaGym Training Loop (pseudocode):
    =====================================
    for episode in range(num_episodes):
        obs = env.reset()
        done = False
        while not done:
            action = policy(obs)
            predicted_next = world_model.forward_dynamics(obs, action)
            actual_next, reward, done = alpa_sim.step(action)
            pred_error = mse(predicted_next, actual_next)
            policy.update(obs, action, reward, actual_next, pred_error)
            obs = actual_next
    """)

if __name__ == "__main__":
    demo_world_model()