class ExperienceLearningParadigm:
    """体验学习范式框架"""

    def __init__(self):
        self.traditional_approaches = {
            "imitation_learning": "从人类示范中学习",
            "reinforcement_learning": "从奖励信号中学习",
            "self_supervised_learning": "从数据本身学习"
        }

        self.experience_learning = {
            "core_idea": "从完整的多模态体验中学习",
            "key_components": [
                "multi_modal_experience_encoding",
                "causal_understanding",
                "knowledge_isolation",
                "continuous_adaptation"
            ],
            "advantages": [
                "更丰富的学习信号",
                "更好的泛化能力",
                "持续改进能力",
                "知识保护机制"
            ]
        }

    def compare_approaches(self):
        """对比不同学习方法"""
        return {
            "Learning_Signal": {
                "Imitation": "人类动作序列",
                "RL": "稀疏奖励信号",
                "Experience": "完整的多模态体验"
            },
            "Data_Efficiency": {
                "Imitation": "中等",
                "RL": "低",
                "Experience": "高"
            },
            "Generalization": {
                "Imitation": "有限",
                "RL": "有限",
                "Experience": "强"
            },
            "Continual_Learning": {
                "Imitation": "困难",
                "RL": "困难",
                "Experience": "支持"
            }
        }

class Pi06Milestones:
    """Pi0.6技术里程碑"""

    def __init__(self):
        self.breakthroughs = {
            "v1.0": {
                "feature": "基础VLA架构",
                "capability": "多机器人任务控制",
                "limitation": "缺乏持续学习能力"
            },
            "v1.5": {
                "feature": "开放式世界泛化",
                "capability": "新环境快速适应",
                "limitation": "仍依赖预训练数据"
            },
            "v1.6": {
                "feature": "体验学习 + 知识隔离",
                "capability": "真实世界持续学习",
                "limitation": "计算复杂度较高"
            }
        }

    def get_evolution_path(self):
        """技术演进路径"""
        return [
            "Pi0: 基础VLA模型",
            "Pi0.5: 开放式泛化",
            "Pi0.6: 体验学习革命"
        ]

class Pi06Architecture(nn.Module):
    """Pi0.6的核心架构"""

    def __init__(self, config):
        super().__init__()

        # 1. 多模态体验编码器
        self.experience_encoder = MultiModalExperienceEncoder(
            config.experience_encoder
        )

        # 2. 因果理解模块
        self.causal_reasoner = CausalReasoner(config.causal)

        # 3. 知识隔离网络
        self.knowledge_isolation = KnowledgeIsolationNetwork(
            config.isolation
        )

        # 4. 体验学习引擎
        self.experience_engine = ExperienceLearningEngine(
            config.experience_engine
        )

        # 5. 动态规划器
        self.dynamic_planner = DynamicPlanner(config.planner)

        # 6. 连续适应模块
        self.continuous_adapter = ContinuousAdapter(config.adapter)

    def forward(self, experience_stream, task_context=None):
        """
        前向传播 - 体验学习模式
        Args:
            experience_stream: 多模态体验流
            task_context: 任务上下文（可选）
        """
        # 1. 多模态体验编码
        experience_features = self.experience_encoder(experience_stream)

        # 2. 因果理解
        causal_model = self.causal_reasoner(experience_features)

        # 3. 知识隔离与检索
        isolated_knowledge = self.knowledge_isolation(
            causal_model, task_context
        )

        # 4. 体验学习
        learned_policies = self.experience_engine(
            experience_features, causal_model, isolated_knowledge
        )

        # 5. 动态规划
        action_plans = self.dynamic_planner(
            learned_policies, task_context
        )

        # 6. 连续适应
        adapted_policies = self.continuous_adapter(
            action_plans, experience_stream
        )

        return adapted_policies, causal_model

class MultiModalExperienceEncoder(nn.Module):
    """多模态体验编码器"""

    def __init__(self, config):
        super().__init__()

        # 时序感知编码器
        self.temporal_encoder = TemporalPerceptionEncoder(
            config.temporal
        )

        # 跨模态关联学习器
        self.cross_modal_association = CrossModalAssociation(
            config.association
        )

        # 体验语义理解器
        self.experience_semantics = ExperienceSemantics(
            config.semantics
        )

        # 体验记忆网络
        self.experience_memory = ExperienceMemoryNetwork(
            config.memory
        )

    def encode_experience_stream(self, raw_experience):
        """编码体验流"""
        # 1. 时序特征提取
        temporal_features = self.temporal_encoder(raw_experience)

        # 2. 跨模态关联学习
        cross_modal_features = self.cross_modal_association(
            temporal_features
        )

        # 3. 语义理解
        semantic_features = self.experience_semantics(
            cross_modal_features
        )

        # 4. 体验记忆整合
        integrated_experience = self.experience_memory(
            semantic_features
        )

        return {
            'temporal_features': temporal_features,
            'cross_modal_features': cross_modal_features,
            'semantic_features': semantic_features,
            'integrated_experience': integrated_experience
        }

    def extract_causal_patterns(self, experience_features):
        """提取因果模式"""
        # 1. 因果图构建
        causal_graph = self.build_causal_graph(experience_features)

        # 2. 因果路径识别
        causal_paths = self.identify_causal_paths(causal_graph)

        # 3. 干预效果预测
        intervention_effects = self.predict_intervention_effects(
            causal_paths
        )

        return {
            'causal_graph': causal_graph,
            'causal_paths': causal_paths,
            'intervention_effects': intervention_effects
        }

class KnowledgeIsolationNetwork(nn.Module):
    """知识隔离网络"""

    def __init__(self, config):
        super().__init__()

        # 知识分块器
        self.knowledge_partitioner = KnowledgePartitioner(
            config.partition
        )

        # 隔离层网络
        self.isolation_layers = nn.ModuleDict({
            f'isolation_{domain}': IsolationLayer(config.layer_dim)
            for domain in config.knowledge_domains
        })

        # 知识检索器
        self.knowledge_retriever = KnowledgeRetriever(config.retrieval)

        # 干扰检测器
        self.interference_detector = InterferenceDetector(config.interference)

        # 知识整合器
        self.knowledge_integrator = KnowledgeIntegrator(config.integration)

    def isolate_knowledge(self, experience_features, task_context):
        """知识隔离处理"""
        # 1. 知识域识别
        knowledge_domains = self.knowledge_partitioner(
            experience_features, task_context
        )

        # 2. 域特定处理
        isolated_features = {}
        for domain in knowledge_domains:
            if domain in self.isolation_layers:
                isolated_features[domain] = self.isolation_layers[domain](
                    experience_features
                )

        # 3. 知识干扰检测
        interference_report = self.interference_detector(
            isolated_features
        )

        # 4. 干扰消除
        if interference_report['has_interference']:
            isolated_features = self.eliminate_interference(
                isolated_features, interference_report
            )

        return isolated_features

    def retrieve_relevant_knowledge(self, current_context, query):
        """检索相关知识"""
        # 1. 上下文编码
        context_embedding = self.encode_context(current_context)

        # 2. 查询编码
        query_embedding = self.encode_query(query)

        # 3. 相似度计算
        similarities = self.compute_similarities(
            context_embedding, query_embedding
        )

        # 4. 知识检索
        retrieved_knowledge = self.knowledge_retriever(
            similarities, top_k=10
        )

        return retrieved_knowledge

    def integrate_new_knowledge(self, new_knowledge, existing_knowledge):
        """整合新知识"""
        # 1. 兼容性检查
        compatibility_score = self.check_compatibility(
            new_knowledge, existing_knowledge
        )

        # 2. 知识融合
        if compatibility_score > self.fusion_threshold:
            integrated_knowledge = self.knowledge_integrator(
                new_knowledge, existing_knowledge
            )
        else:
            # 创建新的知识域
            integrated_knowledge = self.create_new_knowledge_domain(
                new_knowledge
            )

        return integrated_knowledge

class ExperienceLearningEngine(nn.Module):
    """体验学习引擎"""

    def __init__(self, config):
        super().__init__()

        # 体验采样器
        self.experience_sampler = ExperienceSampler(config.sampler)

        # 学习策略选择器
        self.learning_strategy_selector = LearningStrategySelector(
            config.strategy
        )

        # 元学习组件
        self.meta_learner = MetaLearner(config.meta_learning)

        # 自适应学习率调度器
        self.adaptive_scheduler = AdaptiveLearningRateScheduler(
            config.scheduler
        )

    def learn_from_experience(self, experience_stream, current_knowledge):
        """从体验中学习"""
        # 1. 体验采样
        sampled_experiences = self.experience_sampler(experience_stream)

        # 2. 学习策略选择
        learning_strategy = self.learning_strategy_selector(
            sampled_experiences, current_knowledge
        )

        # 3. 策略执行
        if learning_strategy == 'meta_learning':
            new_knowledge = self.meta_learner.learn(
                sampled_experiences, current_knowledge
            )
        elif learning_strategy == 'few_shot_adaptation':
            new_knowledge = self.few_shot_adapt(
                sampled_experiences, current_knowledge
            )
        else:
            new_knowledge = self.standard_learning(
                sampled_experiences, current_knowledge
            )

        return new_knowledge

    def continuous_learning_loop(self, experience_stream, initial_knowledge):
        """持续学习循环"""
        current_knowledge = initial_knowledge
        learning_history = []

        for experience_batch in experience_stream:
            # 1. 学习新知识
            new_knowledge = self.learn_from_experience(
                experience_batch, current_knowledge
            )

            # 2. 知识整合
            integrated_knowledge = self.integrate_knowledge(
                new_knowledge, current_knowledge
            )

            # 3. 学习评估
            learning_quality = self.assess_learning_quality(
                integrated_knowledge, experience_batch
            )

            # 4. 自适应调整
            if learning_quality < self.quality_threshold:
                self.adapt_learning_strategy(learning_quality)

            # 5. 更新知识库
            current_knowledge = integrated_knowledge
            learning_history.append({
                'experience_batch': experience_batch,
                'learning_quality': learning_quality,
                'knowledge_size': self.get_knowledge_size(current_knowledge)
            })

        return current_knowledge, learning_history

class CausalDiscovery(nn.Module):
    """因果发现模块"""

    def __init__(self, config):
        super().__init__()

        # 变量识别器
        self.variable_identifier = VariableIdentifier(config.variables)

        # 因果结构学习器
        self.causal_structure_learner = CausalStructureLearner(
            config.structure
        )

        # 因果强度估计器
        self.causal_strength_estimator = CausalStrengthEstimator(
            config.strength
        )

    def discover_causal_structure(self, experience_data):
        """发现因果结构"""
        # 1. 变量识别
        variables = self.variable_identifier(experience_data)

        # 2. 因果关系学习
        causal_relations = self.causal_structure_learner(
            variables, experience_data
        )

        # 3. 因果强度估计
        causal_strengths = self.causal_strength_estimator(
            causal_relations, experience_data
        )

        # 4. 因果图构建
        causal_graph = self.build_causal_graph(
            variables, causal_relations, causal_strengths
        )

        return causal_graph

    def simulate_interventions(self, causal_graph, interventions):
        """模拟干预效果"""
        # 1. 干预编码
        intervention_effects = self.encode_interventions(interventions)

        # 2. 因果传播
        propagated_effects = self.propagate_causal_effects(
            causal_graph, intervention_effects
        )

        # 3. 结果预测
        predicted_outcomes = self.predict_outcomes(propagated_effects)

        return predicted_outcomes

class CounterfactualReasoning:
    """反事实推理模块"""

    def __init__(self, config):
        self.causal_model = None
        self.world_model = WorldModel(config.world_model)

    def answer_counterfactual(self, factual_world, counterfactual_query):
        """回答反事实问题"""
        # 1. 构建实际世界模型
        actual_world_model = self.build_world_model(factual_world)

        # 2. 识别关键变量
        key_variables = self.identify_key_variables(
            factual_world, counterfactual_query
        )

        # 3. 执行反事实干预
        counterfactual_world = self.execute_intervention(
            actual_world_model, counterfactual_query, key_variables
        )

        # 4. 推理结果
        counterfactual_outcome = self.reason_about_outcome(
            counterfactual_world
        )

        return counterfactual_outcome

    def generate_explanations(self, factual, counterfactual, outcome):
        """生成解释"""
        # 1. 因果链分析
        causal_chain = self.analyze_causal_chain(factual, counterfactual)

        # 2. 关键差异识别
        key_differences = self.identify_key_differences(
            factual, counterfactual
        )

        # 3. 解释生成
        explanation = self.generate_natural_explanation(
            causal_chain, key_differences, outcome
        )

        return explanation

class ExperienceDrivenCurriculum:
    """体验驱动的课程学习"""

    def __init__(self, config):
        self.experience_complexity_analyzer = ExperienceComplexityAnalyzer()
        self.curriculum_generator = CurriculumGenerator()
        self.adaptive_scheduler = AdaptiveScheduler()

    def generate_experience_curriculum(self, experience_pool):
        """生成体验课程"""
        # 1. 体验复杂度分析
        complexity_scores = {}
        for experience in experience_pool:
            complexity = self.experience_complexity_analyzer.analyze(
                experience
            )
            complexity_scores[experience.id] = complexity

        # 2. 体验聚类
        experience_clusters = self.cluster_experiences(
            experience_pool, complexity_scores
        )

        # 3. 课程生成
        curriculum = self.curriculum_generator.generate(
            experience_clusters, complexity_scores
        )

        return curriculum

    def adaptive_curriculum_adjustment(self, learning_performance):
        """自适应课程调整"""
        # 1. 学习模式分析
        learning_pattern = self.analyze_learning_pattern(
            learning_performance
        )

        # 2. 课程难度调整
        if learning_pattern['struggling']:
            self.adjust_difficulty(decrease=True)
        elif learning_pattern['bored']:
            self.adjust_difficulty(increase=True)

        # 3. 体验重采样
        new_curriculum = self.resample_experiences(learning_pattern)

        return new_curriculum

class MultiObjectiveOptimization:
    """多目标优化框架"""

    def __init__(self, config):
        self.objectives = {
            'task_performance': TaskPerformanceObjective(),
            'knowledge_preservation': KnowledgePreservationObjective(),
            'adaptation_speed': AdaptationSpeedObjective(),
            'computational_efficiency': ComputationalEfficiencyObjective()
        }

        self.pareto_optimizer = ParetoOptimizer(config.pareto)

    def optimize_objectives(self, model, experience_data):
        """多目标优化"""
        # 1. 目标评估
        objective_scores = {}
        for obj_name, objective in self.objectives.items():
            score = objective.evaluate(model, experience_data)
            objective_scores[obj_name] = score

        # 2. 帕累托前沿计算
        pareto_solutions = self.pareto_optimizer.find_pareto_front(
            objective_scores
        )

        # 3. 最优解选择
        optimal_solution = self.select_optimal_solution(pareto_solutions)

        return optimal_solution, objective_scores

    def balance_tradeoffs(self, current_performance, target_requirements):
        """平衡权衡"""
        # 1. 差距分析
        performance_gaps = self.analyze_performance_gaps(
            current_performance, target_requirements
        )

        # 2. 权重调整
        adjusted_weights = self.adjust_objective_weights(performance_gaps)

        # 3. 优化方向确定
        optimization_direction = self.determine_optimization_direction(
            adjusted_weights
        )

        return optimization_direction

class ExperienceLearningEvaluator:
    """体验学习评估框架"""

    def __init__(self, config):
        self.experience_databases = ExperienceDatabases()
        self.evaluation_metrics = ExperienceLearningMetrics()
        self.baseline_comparators = BaselineComparators()

    def evaluate_experience_learning(self, model, test_scenarios):
        """评估体验学习能力"""
        results = {}

        for scenario in test_scenarios:
            # 1. 基线测试
            baseline_performance = self.baseline_test(model, scenario)

            # 2. 体验学习测试
            learning_curve = self.learning_curve_test(model, scenario)

            # 3. 泛化能力测试
            generalization_performance = self.generalization_test(
                model, scenario
            )

            # 4. 知识保留测试
            knowledge_retention = self.knowledge_retention_test(
                model, scenario
            )

            results[scenario.name] = {
                'baseline': baseline_performance,
                'learning_curve': learning_curve,
                'generalization': generalization_performance,
                'knowledge_retention': knowledge_retention
            }

        return results

    def measure_knowledge_isolation(self, model):
        """测量知识隔离效果"""
        # 1. 知识域独立性测试
        independence_scores = self.test_knowledge_independence(model)

        # 2. 干扰测试
        interference_resistance = self.test_interference_resistance(model)

        # 3. 知识整合测试
        integration_quality = self.test_knowledge_integration(model)

        return {
            'independence': independence_scores,
            'interference_resistance': interference_resistance,
            'integration_quality': integration_quality
        }

class AutonomousManufacturing:
    """基于Pi0.6的自主制造系统"""

    def __init__(self, pi06_model):
        self.pi06 = pi06_model
        self.manufacturing_line = ManufacturingLine()
        self.quality_system = QualitySystem()

    def continuous_process_improvement(self):
        """持续工艺改进"""
        # 1. 收集生产体验
        production_experiences = self.collect_production_experiences()

        # 2. 体验学习
        improved_knowledge = self.pi06.learn_from_experience(
            production_experiences
        )

        # 3. 工艺优化
        optimized_parameters = self.pi06.optimize_process(
            improved_knowledge
        )

        # 4. 实施改进
        improvement_results = self.implement_improvements(
            optimized_parameters
        )

        return improvement_results

    def adaptive_quality_control(self):
        """自适应质量控制"""
        # 1. 质量数据分析
        quality_experiences = self.analyze_quality_data()

        # 2. 因因分析
        causal_analysis = self.pi06.analyze_causes(
            quality_experiences
        )

        # 3. 预防性措施
        preventive_actions = self.pi06.generate_preventive_actions(
            causal_analysis
        )

        # 4. 实施监控
        monitoring_results = self.implement_monitoring(
            preventive_actions
        )

        return monitoring_results

class PersonalizedMedicalRobot:
    """基于Pi0.6的个性化医疗机器人"""

    def __init__(self, pi06_model):
        self.pi06 = pi06_model
        self.patient_monitor = PatientMonitor()
        self.safety_system = SafetySystem()

    def personalized_treatment_adaptation(self, patient_profile):
        """个性化治疗适应"""
        # 1. 患者体验收集
        patient_experiences = self.collect_patient_experiences(
            patient_profile
        )

        # 2. 个性化学习
        personalized_knowledge = self.pi06.learn_patient_specific(
            patient_experiences, patient_profile
        )

        # 3. 治疗方案调整
        adjusted_treatment = self.pi06.adjust_treatment(
            personalized_knowledge
        )

        # 4. 安全验证
        safety_verification = self.safety_system.verify(
            adjusted_treatment
        )

        if safety_verification['safe']:
            return adjusted_treatment
        else:
            return self.fallback_treatment(patient_profile)

    def continuous_skill_improvement(self, procedure_feedback):
        """持续技能改进"""
        # 1. 手术体验分析
        surgical_experiences = self.analyze_surgical_experiences(
            procedure_feedback
        )

        # 2. 技能学习
        improved_skills = self.pi06.improve_surgical_skills(
            surgical_experiences
        )

        # 3. 技能验证
        skill_validation = self.validate_surgical_skills(improved_skills)

        return skill_validation