物理AI元年：英伟达Cosmos 3与Figure 03引爆的智能革命

// 人形机器人运动控制MPC算法简化实现
// 模型预测控制（Model Predictive Control）
// 应用于人形机器人行走、操作等运动控制任务
package main

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

// ============ 运动学模型 ============

// RobotState 机器人状态
type RobotState struct {
	// 质心位置
	COMX, COMY, COMZ float64
	// 质心速度
	COMVX, COMVY, COMVZ float64
	// 身体姿态（欧拉角）
	Roll, Pitch, Yaw float64
	// 身体角速度
	RollVel, PitchVel, YawVel float64
	// 关节角度 (44个自由度)
	JointAngles []float64
	// 关节速度
	JointVels []float64
}

// MPCConfig MPC控制器配置
type MPCConfig struct {
	PredictionHorizon int     // 预测时域（步数）
	ControlHorizon    int     // 控制时域（步数）
	Dt                float64 // 时间步长 (s)
	
	// 权重矩阵
	QPos      float64 // 位置跟踪权重
	QVel      float64 // 速度跟踪权重
	QAngular  float64 // 姿态跟踪权重
	RControl  float64 // 控制输入权重
	RDelta    float64 // 控制变化率权重
	
	// 约束
	MaxJointTorque   float64
	MaxJointVelocity float64
	JointLimitMin    []float64
	JointLimitMax    []float64
}

// MPCController MPC控制器
type MPCController struct {
	Config     MPCConfig
	JointNum   int
	
	// 内部状态
	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),
	}
}

// ============ 动力学模型 ============

// FloatingBaseDynamics 浮动基座动力学（简化版LIPM模型）
// 使用线性倒立摆模型（Linear Inverted Pendulum Model）
type FloatingBaseDynamics struct {
	Mass         float64 // 机器人总质量
	Height       float64 // 质心高度
	Gravity      float64
	FootRadius   float64 // 支撑脚区域半径
}

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

// LIPMDynamics LIPM模型动力学
// 状态空间: [com_x, com_y, com_vx, com_vy]^T
// 控制输入: [cop_x, cop_y]^T (压力中心)
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{}
	
	// 位置更新（半隐式欧拉）
	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
	
	// 速度更新
	nextState[2] = state[2] + g_h*(state[0]-control[0])*dt
	nextState[3] = state[3] + g_h*(state[1]-control[1])*dt
	
	return nextState
}

// ============ 运动规划 ============

// FootstepPlanner 步态规划器
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,
	}
}

// Footstep 落脚点
type Footstep struct {
	X, Y, Z    float64
	Yaw        float64
	IsLeftFoot bool
	Phase      float64 // 0-1 当前步态周期相位
}

// PlanFootsteps 规划落脚点序列
func (fp *FootstepPlanner) PlanFootsteps(numSteps int, direction float64) []Footstep {
	steps := make([]Footstep, numSteps)
	
	for i := 0; i < numSteps; i++ {
		phase := float64(i) / float64(numSteps)
		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,
			Z:          0.0,
			Yaw:        direction,
			IsLeftFoot: isLeft,
			Phase:      phase,
		}
	}
	
	return steps
}

// ============ MPC求解器（简化版） ============

// SolveMPC 求解MPC优化问题
// 简化实现：使用LIPM模型的解析解 + 前馈补偿
func (mpc *MPCController) SolveMPC(
	currentState *RobotState,
	targetTrajectory []RobotState,
	dynamics *FloatingBaseDynamics,
) ([]float64, float64) {
	start := time.Now()
	
	horizon := mpc.Config.PredictionHorizon
	controlDim := mpc.JointNum
	
	// 初始化控制序列
	controlSeq := make([]float64, controlDim*mpc.Config.ControlHorizon)
	copy(controlSeq, mpc.prevControl)
	
	// LIPM状态
	lipmState := [4]float64{
		currentState.COMX,
		currentState.COMY,
		currentState.COMVX,
		currentState.COMVY,
	}
	
	// 简化MPC：基于LIPM模型前向模拟 + PD补偿
	totalCost := 0.0
	
	for t := 0; t < horizon; t++ {
		// 计算参考状态
		var refState [4]float64
		if t < len(targetTrajectory) {
			refState = [4]float64{
				targetTrajectory[t].COMX,
				targetTrajectory[t].COMY,
				targetTrajectory[t].COMVX,
				targetTrajectory[t].COMVY,
			}
		}
		
		// 计算CoP控制输入（简化版：位置误差反馈）
		copX := lipmState[0] + lipmState[2]*dyn.Height/dyn.Gravity*0.5
		copY := lipmState[1] + lipmState[3]*dyn.Height/dyn.Gravity*0.5
		
		// 添加跟踪误差反馈
		kpGain := 10.0
		copX += kpGain * (refState[0] - lipmState[0])
		copY += kpGain * (refState[1] - lipmState[1])
		
		// 前向模拟
		control := [2]float64{copX, copY}
		lipmState = dyn.LIPMDynamics(lipmState, control, mpc.Config.Dt)
		
		// 计算代价
		posCost := mpc.Config.QPos * (math.Pow(lipmState[0]-refState[0], 2) + math.Pow(lipmState[1]-refState[1], 2))
		velCost := mpc.Config.QVel * (math.Pow(lipmState[2]-refState[2], 2) + math.Pow(lipmState[3]-refState[3], 2))
		ctrlCost := mpc.Config.RControl * (math.Pow(copX, 2) + math.Pow(copY, 2))
		
		totalCost += posCost + velCost + ctrlCost
		
		// 简化为关节力矩指令（使用雅可比转置）
		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 := 50.0
				kd := 5.0
				torque := kp*(targetAngle-currentAngle) - kd*currentState.JointVels[j]
				
				// 限幅
				if torque > mpc.Config.MaxJointTorque {
					torque = mpc.Config.MaxJointTorque
				} else if torque < -mpc.Config.MaxJointTorque {
					torque = -mpc.Config.MaxJointTorque
				}
				
				controlSeq[idx] = torque
			}
		}
	}
	
	// 保存控制输出
	mpc.prevControl = controlSeq[:controlDim]
	
	elapsed := time.Since(start)
	return controlSeq[:controlDim], elapsed.Seconds() * 1000
}

func main() {
	rand.Seed(time.Now().UnixNano())
	
	fmt.Println("===== 人形机器人运动控制MPC算法简化实现 =====")
	fmt.Println("参考：Figure 03 / Tesla Optimus 运动控制系统")
	fmt.Println()
	
	// 1. 初始化机器人状态
	fmt.Println("[1/5] 初始化机器人状态")
	jointNum := 44
	initialState := &RobotState{
		COMX: 0, COMY: 0, COMZ: 0.95,
		COMVX: 0, COMVY: 0, COMVZ: 0,
		Roll: 0, Pitch: 0, Yaw: 0,
		JointAngles: make([]float64, jointNum),
		JointVels:   make([]float64, jointNum),
	}
	fmt.Printf("  机器人质量: 60kg, 自由度: %d\n", jointNum)
	
	// 2. 初始化MPC控制器
	fmt.Println("[2/5] 配置MPC控制器")
	mpcConfig := MPCConfig{
		PredictionHorizon: 50,
		ControlHorizon:    10,
		Dt:                0.01, // 10ms控制周期
		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)
	fmt.Printf("  预测时域: %d步 (%.1fms)\n",
		mpcConfig.PredictionHorizon, 
		float64(mpcConfig.PredictionHorizon)*mpcConfig.Dt*1000)
	
	// 3. 动力学模型
	fmt.Println("[3/5] 初始化动力学模型 (LIPM)")
	dyn := NewFloatingBaseDynamics(60.0, 0.95)
	fmt.Printf("  质心高度: %.2fm\n", dyn.Height)
	
	// 4. 落脚点规划
	fmt.Println("[4/5] 步态规划")
	planner := NewFootstepPlanner(0.4, 0.2)
	steps := planner.PlanFootsteps(10, 0.0)
	fmt.Printf("  规划步数: %d\n", len(steps))
	for i, s := range steps[:5] {
		side := "左"
		if !s.IsLeftFoot { side = "右" }
		fmt.Printf("    第%d步: %s脚 (%.2f, %.2f)\n", i+1, side, s.X, s.Y)
	}
	
	// 5. 执行MPC控制
	fmt.Println("[5/5] MPC控制求解")
	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
	
	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("    步 %d: 质心 (%.3f, %.3f), 耗时 %.2fms\n",
				i, initialState.COMX, initialState.COMY, solveTime)
			_ = control
		}
	}
	
	fmt.Println("\n===== MPC控制性能 =====")
	fmt.Printf("  平均求解时间: %.3fms\n", totalSolveTime/float64(iterations))
	fmt.Printf("  控制频率: %.0f Hz\n", 1000.0/(totalSolveTime/float64(iterations)))
	fmt.Printf("  总距离: %.2fm\n", initialState.COMX)
	
	fmt.Println("\n===== Figure 03规格对比 =====")
	fmt.Println("  Helix 02: 1kHz关节指令, 44自由度")
	fmt.Println("  MPC频率: 100-1000Hz (本简化~100Hz, C++/CUDA可达1kHz)")
}

// VLA模型端到端推理管道模拟
// 视觉编码→语言理解→动作规划→执行反馈
// 参考：英伟达Alpamayo 2 Super / Figure Helix架构
package main

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

// ===== 视觉编码层 =====

type ImageFeature []float64

// VisualEncoder 视觉编码器（模拟ViT + 多视角环视）
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++ {
			val := float64(rand.Intn(1000)) / 1000.0
			f[i] = math.Tanh(val*2.0 - 1.0)
		}
		features[v] = f
	}
	return features, time.Since(start).Seconds() * 1000
}

// ===== 语言理解层 =====

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
	TokenizedTime float64
}

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: "抓取货架第三层的蓝色箱子，放置到传送带上",
		SubTasks: []string{
			"1. 定位目标物体位置 (视觉定位)",
			"2. 规划抓取路径 (运动学逆解)",
			"3. 执行抓取动作 (力控调整)",
			"4. 放置到传送带 (轨迹规划)",
		},
		Confidence: 0.92,
	}
	
	plan.LatencyMs = time.Since(start).Seconds() * 1000
	return plan
}

// ===== 动作规划层 =====

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
			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

			commands[j] = JointCommand{
				JointIndex: j,
				TargetPos:  targetPos,
				TargetVel:  targetVel,
				Torque:     computeTorque(targetPos, targetVel, sceneContext),
				Timestamp:  int64(t),
			}
		}
		seq.Commands[t] = commands
	}

	seq.TotalTime = time.Since(start).Seconds() * 1000
	seq.SuccessRate = 0.97
	return seq
}

// ===== 执行反馈层 =====

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,
	}

	// 模拟与预期轨迹的偏差
	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 {
	kp := 100.0
	kd := 10.0
	gravityComp := ctx["gravity_comp"] * 9.8
	return kp*targetPos + kd*targetVel + gravityComp
}

func computeStatistics(stats map[string]float64) {
	fmt.Println("\n===== VLA推理管道性能统计 =====")
	for k, v := range stats {
		fmt.Printf("  %s: %.2f\n", k, v)
	}
}

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 端到端推理管道模拟 =====")
	fmt.Println()

	// 1. 视觉编码
	encoder := NewVisualEncoder()
	reasoner := NewLanguageReasoner()
	planner := NewActionPlanner()
	fbCollector := NewFeedbackCollector()

	fmt.Println("[1/5] 视觉编码: 处理6路环视图像...")
	pixels := make([]float64, 1280*720*3)
	features, encodeTime := encoder.Encode(pixels)
	fmt.Printf("  特征维度: %d/视角, 特征向量数: %d, 耗时: %.2fms\n",
		encoder.FeatureDim, len(features), encodeTime)

	// 2. 指令解析
	fmt.Println("[2/5] 语言理解...")
	instruction := Instruction{
		RawText:  "抓取货架第三层的蓝色箱子，放置到传送带上",
		TokenIDs: make([]int, 128),
	}
	for i := range instruction.TokenIDs {
		instruction.TokenIDs[i] = rand.Intn(128000)
	}

	plan := reasoner.Reason(features, instruction)
	fmt.Printf("  目标: %s\n", plan.GoalDescription)
	fmt.Printf("  子任务: %v\n", plan.SubTasks)
	fmt.Printf("  置信度: %.2f%%, 耗时: %.2fms\n", plan.Confidence*100, plan.LatencyMs)

	// 3. 动作规划
	fmt.Println("[3/5] 动作规划: 生成关节运动序列...")
	sceneCtx := map[string]float64{
		"gravity_comp": 1.0,
		"load_weight":  2.5,
		"friction":     0.3,
	}
	actionSeq := planner.PlanAction(plan, sceneCtx)
	fmt.Printf("  规划步长: %d步 (%.1fms)\n",
		actionSeq.Horizon, float64(actionSeq.Horizon)/float64(planner.ControlFreq)*1000)
	fmt.Printf("  关节数: %d, 生成耗时: %.2fms\n",
		planner.JointNum, actionSeq.TotalTime)

	// 4. 执行与反馈
	fmt.Println("[4/5] 执行与反馈...")
	fb := fbCollector.CollectFeedback(actionSeq)
	fmt.Printf("  任务进度: %.1f%%, 稳定性: %.2f\n",
		fb.TaskProgress*100, fb.Stability)
	fmt.Printf("  最大位置误差: %.4f rad\n", findMax(fb.ErrorPos))
	fmt.Printf("  触觉力: %.2f - %.2f N\n", findMin(fb.ForceReadings), findMax(fb.ForceReadings))

	// 5. VLA闭环
	fmt.Println("[5/5] VLA闭环反馈...")
	if fb.RecoveryNeeded {
		fmt.Println("  需要恢复动作: 重新规划轨迹")
	} else {
		fmt.Println("  执行正常: 误差在容忍范围内")
	}

	stats := map[string]float64{
		"视觉编码延迟 (ms)":         encodeTime,
		"语言推理延迟 (ms)":         plan.LatencyMs,
		"动作规划延迟 (ms)":         actionSeq.TotalTime,
		"总延迟 (ms)":               encodeTime + plan.LatencyMs + actionSeq.TotalTime,
		"控制频率 (Hz)":             float64(planner.ControlFreq),
		"单步关节指令数":             float64(planner.JointNum),
		"动作序列成功率 (%)":         actionSeq.SuccessRate * 100,
	}
	computeStatistics(stats)

	fmt.Println("\n===== 架构规模对比 =====")
	fmt.Println("Alpamayo 2 Super: 320亿参数, 360°环视, 1kHz控制")
	fmt.Println("Figure 03 Helix 02: 44自由度, 端到端VLA, 单网络权重共享")
	fmt.Println("Cosmos 3 Super: 640亿参数, 混合Transformer, 全模态生成")
}

"""
Cosmos 3 世界模型采样与物理仿真
基于英伟达开源框架 cosmos-framework
演示：动作条件化世界模型推理
"""
import torch
import numpy as np
from typing import Optional, List, Tuple
import math

# ==================== 1. 配置与模型加载 ====================

class Cosmos3Config:
    """Cosmos 3模型配置"""
    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  # 44自由度动作空间
        self.audio_dim = 80   # MEL频谱维度
        
        # MoT双塔配置
        self.reasoner_layers = self.num_layers
        self.generator_layers = self.num_layers
        self.diffusion_steps = 50

class Cosmos3Tokenizer:
    """多模态Tokenizer（模拟）"""
    def __init__(self, config: Cosmos3Config):
        self.config = config
        self.text_tokenizer = self._init_text_tokenizer()
        self.vae_encoder = self._init_vae_encoder()
        self.action_encoder = self._init_action_encoder()
        
    def _init_text_tokenizer(self):
        print("[Tokenizer] 初始化文本Tokenizer (sentinel-based)")
        return {"type": "sentinel", "vocab_size": self.config.vocab_size}
    
    def _init_vae_encoder(self):
        print(f"[Tokenizer] 初始化VAE编码器, 潜在维度={self.config.vae_latent_dim}")
        return {"type": "vae", "latent_dim": self.config.vae_latent_dim}
    
    def _init_action_encoder(self):
        print(f"[Tokenizer] 初始化动作编码器, 动作维度={self.config.action_dim}")
        return {"type": "mlp", "input_dim": self.config.action_dim}
    
    def encode_text(self, text: str) -> torch.Tensor:
        """文本编码为token IDs (简化模拟)"""
        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:
        """图像编码为潜在表示 (模拟VAE)"""
        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:
        """视频编码 (模拟3D VAE)"""
        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)
    
    def encode_action(self, actions: torch.Tensor) -> torch.Tensor:
        """动作序列编码"""
        return actions  # 简化：直接传入
    

# ==================== 2. 3D多模态RoPE位置编码 ====================

class Multimodal3DRoPE:
    """3D多模态旋转位置编码 (MRoPE)"""
    def __init__(self, dim: int, max_time: int = 512, max_h: int = 64, max_w: int = 64):
        self.dim = dim
        self.max_time = max_time
        self.max_h = max_h
        self.max_w = max_w
        
        # 将维度分配到三个轴
        assert dim % 6 == 0, "dim must be divisible by 6"
        self.dim_per_axis = dim // 3
        
        # 预计算频率
        self._init_freqs()
    
    def _init_freqs(self):
        """初始化各轴的频率"""
        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.register_buffer('inv_freq_t', inv_freq_t)
        self.register_buffer('inv_freq_h', inv_freq_h)
        self.register_buffer('inv_freq_w', inv_freq_w)
    
    def register_buffer(self, name: str, tensor: torch.Tensor):
        setattr(self, name, tensor)
    
    def _compute_rope(self, x: torch.Tensor, inv_freq: torch.Tensor) -> torch.Tensor:
        """计算单轴RoPE"""
        seq_len = x.shape[1]
        freqs = torch.einsum('i,j->ij', torch.arange(seq_len), inv_freq)
        emb = torch.cat((freqs, freqs), dim=-1)
        return emb[None, :, :].to(x.device)
    
    def forward(self, 
                t_ids: torch.Tensor,  # [B, L] 时间索引
                h_ids: torch.Tensor,  # [B, L] 高度索引
                w_ids: torch.Tensor   # [B, L] 宽度索引
               ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """计算3D RoPE"""
        # 简化实现：实际应使用复数旋转
        t_pe = torch.cos(t_ids.float() @ self.inv_freq_t[None])  # 时间轴
        h_pe = torch.cos(h_ids.float() @ self.inv_freq_h[None])  # 高度轴
        w_pe = torch.cos(w_ids.float() @ self.inv_freq_w[None])  # 宽度轴
        
        return t_pe, h_pe, w_pe
    

# ==================== 3. MoT双塔Transformer层 ====================

class MoTDecoderLayer(torch.nn.Module):
    """MoT解码器层：包含Reasoner和Generator双塔"""
    def __init__(self, config: Cosmos3Config, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        
        # Reasoner塔（自回归）
        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塔（扩散）
        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:
        """Reasoner前向：因果自注意力"""
        # 因果自注意力
        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
        ffn_out = self.reasoner_ffn(ar_hidden)
        ar_hidden = self.reasoner_norm2(ar_hidden + ffn_out)
        
        return ar_hidden
    
    def forward_generator(self,
                          dm_hidden: torch.Tensor,
                          ar_hidden: torch.Tensor) -> torch.Tensor:
        """Generator前向：全双向注意力，关注AR+DM"""
        # 跨注意力：DM查询，AR+DM作为K,V
        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
        ffn_out = self.generator_ffn(dm_hidden)
        dm_hidden = self.generator_norm2(dm_hidden + ffn_out)
        
        return dm_hidden


# ==================== 4. 世界模型推理管道 ====================

class Cosmos3WorldModel:
    """Cosmos 3世界模型推理管道"""
    def __init__(self, config: Cosmos3Config):
        self.config = config
        self.tokenizer = Cosmos3Tokenizer(config)
        self.rope = Multimodal3DRoPE(config.hidden_dim)
        
        # MoT层
        self.layers = torch.nn.ModuleList([
            MoTDecoderLayer(config, i) for i in range(config.num_layers)
        ])
        
        # 输出头
        self.video_decoder = self._init_video_decoder()
        self.action_decoder = self._init_action_decoder()
    
    def _init_video_decoder(self):
        """视频解码器（扩散模型）"""
        return {"type": "diffusion", "steps": self.config.diffusion_steps}
    
    def _init_action_decoder(self):
        """动作解码器"""
        return {"type": "mlp", "output_dim": self.config.action_dim}
    
    def generate_video(self,
                       text_prompt: str,
                       conditioning_video: Optional[torch.Tensor] = None,
                       action_sequence: Optional[torch.Tensor] = None,
                       num_frames: int = 16) -> torch.Tensor:
        """
        视频生成（世界模型仿真）
        
        Args:
            text_prompt: 文本描述
            conditioning_video: 条件视频（可选）
            action_sequence: 动作序列（可选）
            num_frames: 生成帧数
            
        Returns:
            生成的视频张量 [B, T, C, H, W]
        """
        print(f"\n[WorldModel] 开始世界模型推理")
        print(f"  文本提示: '{text_prompt}'")
        print(f"  生成帧数: {num_frames}")
        print(f"  条件视频: {'提供' if conditioning_video is not None else '无'}")
        print(f"  动作条件: {'提供' if action_sequence is not None else '无条件生成'}")
        
        # 1. 编码输入
        text_tokens = self.tokenizer.encode_text(text_prompt)
        print(f"  文本token数: {text_tokens.shape[1]}")
        
        # 2. 构建AR子序列（文本推理）
        ar_seq_len = text_tokens.shape[1]
        ar_hidden = torch.randn(1, ar_seq_len, self.config.hidden_dim)
        
        # 3. 生成DM子序列（视频生成）
        dm_seq_len = num_frames * 16 * 16  # 简化：每帧256个潜在token
        dm_hidden = torch.randn(1, dm_seq_len, self.config.hidden_dim)
        
        print(f"  AR序列长度: {ar_seq_len}")
        print(f"  DM序列长度: {dm_seq_len}")
        
        # 4. 通过MoT层
        causal_mask = torch.triu(
            torch.full((ar_seq_len, ar_seq_len), float('-inf')), diagonal=1
        )
        
        for i, layer in enumerate(self.layers):
            # Reasoner前向（因果）
            ar_hidden = layer.forward_reasoner(ar_hidden, causal_mask)
            # Generator前向（全双向）
            dm_hidden = layer.forward_generator(dm_hidden, ar_hidden)
            
            if i % 8 == 0:
                print(f"  MoT层 {i}/{len(self.layers)}: 推理完成")
        
        # 5. 解码视频（简化：模拟扩散去噪）
        generated_video = self._diffusion_decode(dm_hidden, num_frames)
        
        print(f"  [WorldModel] 视频生成完成: shape={generated_video.shape}")
        return generated_video
    
    def _diffusion_decode(self, 
                          latent: torch.Tensor, 
                          num_frames: int) -> torch.Tensor:
        """扩散解码（简化模拟）"""
        B = latent.shape[0]
        C, H, W = 3, 128, 128
        
        # 模拟扩散去噪过程
        noise = torch.randn(B, num_frames, C, H, W)
        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"  扩散去噪: step {step}/{self.config.diffusion_steps}")
        
        return video


# ==================== 5. 动作条件化世界模型示例 ====================

class ActionConditionedWorldModel:
    """
    动作条件化世界模型
    输入：机器人动作序列 → 输出：未来世界状态（视频）
    这是Cosmos 3的"前向动力学"能力
    """
    def __init__(self, config: Cosmos3Config):
        self.config = config
        self.world_model = Cosmos3WorldModel(config)
    
    def forward_dynamics(self,
                         current_observation: torch.Tensor,
                         action_sequence: torch.Tensor,
                         num_predict_frames: int = 8) -> torch.Tensor:
        """
        前向动力学预测
        
        Args:
            current_observation: 当前观测 [B, C, H, W]
            action_sequence: 未来动作序列 [B, T_action, D_action]
            num_predict_frames: 预测帧数
            
        Returns:
            预测的未来视频帧 [B, T_pred, C, H, W]
        """
        text_prompt = "基于当前观测和动作序列，预测未来的物理世界状态变化"
        
        predicted_video = self.world_model.generate_video(
            text_prompt=text_prompt,
            conditioning_video=current_observation.unsqueeze(1),
            action_sequence=action_sequence,
            num_frames=num_predict_frames
        )
        
        return predicted_video
    
    def inverse_dynamics(self,
                         video_sequence: torch.Tensor,
                         start_state: torch.Tensor) -> torch.Tensor:
        """
        逆动力学：从观测序列推断动作
        
        Args:
            video_sequence: 观测到的视频 [B, T, C, H, W]
            start_state: 初始状态
            
        Returns:
            推断的动作序列 [B, T, D_action]
        """
        print(f"\n[InverseDynamics] 从视频推断动作序列")
        print(f"  视频帧数: {video_sequence.shape[1]}")
        
        # 简化：用Cosmos 3的逆向模式
        # 实际实现需要将视频编码后通过Generator的反向传播
        B, T = video_sequence.shape[:2]
        inferred_actions = torch.randn(B, T, self.config.action_dim)
        
        print(f"  推断动作序列: shape={inferred_actions.shape}")
        return inferred_actions


# ==================== 6. 主程序演示 ====================

def demo_world_model():
    """Cosmos 3世界模型演示"""
    print("=" * 60)
    print("Cosmos 3 世界模型 - 物理AI推理演示")
    print("=" * 60)
    
    # 配置
    config = Cosmos3Config("nano")
    print(f"\n[Config] 模型规格: Nano (8B)")
    print(f"  隐藏维度: {config.hidden_dim}")
    print(f"  MoT层数: {config.num_layers}")
    print(f"  注意力头数: {config.num_heads}")
    
    # 1. 基本视频生成
    print("\n" + "-" * 50)
    print("场景1: 文本到视频生成")
    print("-" * 50)
    
    model = Cosmos3WorldModel(config)
    text_prompt = "一个机器人将蓝色箱子从货架第三层搬运到传送带上"
    video = model.generate_video(text_prompt, num_frames=16)
    print(f"  输出: {video.shape}")
    
    # 2. 前向动力学
    print("\n" + "-" * 50)
    print("场景2: 前向动力学 - 动作条件化世界预测")
    print("-" * 50)
    
    awm = ActionConditionedWorldModel(config)
    current_obs = torch.randn(1, 3, 256, 256)
    action_seq = torch.randn(1, 10, config.action_dim)
    
    predicted = awm.forward_dynamics(current_obs, action_seq, num_predict_frames=8)
    print(f"  输入动作序列: {action_seq.shape}")
    print(f"  预测未来帧: {predicted.shape}")
    
    # 3. 逆动力学
    print("\n" + "-" * 50)
    print("场景3: 逆动力学 - 从观测推断动作")
    print("-" * 50)
    
    video_seq = torch.randn(1, 16, 3, 256, 256)
    inferred_actions = awm.inverse_dynamics(video_seq, current_obs)
    
    # 4. AlpaGym闭环RL
    print("\n" + "-" * 50)
    print("场景4: AlpaGym 闭环RL训练")
    print("-" * 50)
    
    print("""
    AlpaGym训练循环伪代码:
    ========================
    for episode in range(num_episodes):
        obs = env.reset()
        done = False
        while not done:
            # 开环：当前策略输出动作
            action = policy(obs)
            
            # Cosmos 3世界模型预测结果
            predicted_next_obs = world_model.forward_dynamics(obs, action)
            
            # 环境执行（AlpaSim仿真器）
            actual_next_obs, reward, done = alpa_sim.step(action)
            
            # 对比预测与实际的差距
            prediction_error = mse(predicted_next_obs, actual_next_obs)
            
            # 强化学习更新
            policy.update(obs, action, reward, actual_next_obs, prediction_error)
            
            obs = actual_next_obs
    """)
    
    print("\n" + "=" * 60)
    print("演示完成")
    print("=" * 60)


if __name__ == "__main__":
    demo_world_model()