class Pi05Evolution:
    """Pi0.5的技术演进路线"""

    def __init__(self):
        self.pi0_base = "Pi0基础架构"
        self.pi05_innovations = [
            "异构任务协同训练",
            "层次化推理模块",
            "开放式泛化机制",
            "知识隔离技术",
            "多模态融合增强"
        ]

    def get_architecture_evolution(self):
        """架构演进对比"""
        return {
            "Pi0": {
                "核心": "VLA + 流匹配",
                "训练": "大规模预训练 + 任务微调",
                "泛化": "有限的任务迁移"
            },
            "Pi0.5": {
                "核心": "VLA + 层次化推理 + 异构协同",
                "训练": "异构任务协同训练 + 开放式预训练",
                "泛化": "开放式世界泛化 + 快速适应"
            }
        }

class Pi05Architecture(nn.Module):
    """Pi0.5的核心架构"""

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

        # 1. 多模态感知模块
        self.perception_module = MultiModalPerception(config.perception)

        # 2. 语言理解与推理模块
        self.language_reasoner = LanguageReasoner(config.language)

        # 3. 层次化任务规划器
        self.hierarchical_planner = HierarchicalPlanner(config.planning)

        # 4. 异构任务协同模块
        self.task_collaboration = HeterogeneousTaskCollaboration(config.collaboration)

        # 5. 动作生成与执行模块
        self.action_generator = ActionGenerator(config.action)

        # 6. 开放式适应模块
        self.open_world_adapter = OpenWorldAdapter(config.adaptation)

    def forward(self, observations, instructions, task_context=None):
        """
        前向传播 - 支持开放式推理
        Args:
            observations: 多模态观测
            instructions: 自然语言指令
            task_context: 任务上下文信息（可选）
        """
        # 1. 多模态感知编码
        perception_features = self.perception_module(observations)

        # 2. 语言理解与高层推理
        language_features = self.language_reasoner(
            instructions, task_context
        )

        # 3. 层次化任务规划
        task_plan = self.hierarchical_planner(
            perception_features, language_features
        )

        # 4. 异构任务协同推理
        collaborative_features = self.task_collaboration(
            task_plan, perception_features
        )

        # 5. 动作序列生成
        action_sequences = self.action_generator(
            collaborative_features, task_plan
        )

        # 6. 开放式适应性调整
        adapted_actions = self.open_world_adapter(
            action_sequences, observations
        )

        return adapted_actions, task_plan

class MultiModalPerception(nn.Module):
    """增强的多模态感知模块"""

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

        # 视觉感知
        self.vision_encoder = VisionTransformer(
            patch_size=config.vision.patch_size,
            embed_dim=config.vision.embed_dim,
            depth=config.vision.depth
        )

        # 深度感知
        self.depth_encoder = DepthEncoder(config.depth)

        # 触觉感知
        self.tactile_encoder = TactileEncoder(config.tactile)

        # 音频感知
        self.audio_encoder = AudioEncoder(config.audio)

        # 语义地图构建
        self.semantic_mapper = SemanticMapper(config.semantic)

        # 多模态融合
        self.modality_fusion = CrossModalFusion(config.fusion)

    def forward(self, observations):
        """处理多模态观测"""
        features = {}

        # 1. 视觉特征提取
        if 'rgb' in observations:
            features['vision'] = self.vision_encoder(observations['rgb'])

        # 2. 深度特征提取
        if 'depth' in observations:
            features['depth'] = self.depth_encoder(observations['depth'])

        # 3. 触觉特征提取
        if 'tactile' in observations:
            features['tactile'] = self.tactile_encoder(observations['tactile'])

        # 4. 音频特征提取
        if 'audio' in observations:
            features['audio'] = self.audio_encoder(observations['audio'])

        # 5. 语义地图更新
        if 'pose' in observations:
            self.semantic_mapper.update_map(
                features, observations['pose']
            )

        # 6. 多模态特征融合
        fused_features = self.modality_fusion(features)

        return fused_features

class HierarchicalPlanner(nn.Module):
    """层次化任务规划器"""

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

        # 高层任务分解器
        self.task_decomposer = TaskDecomposer(config.decomposer)

        # 子任务规划器
        self.subtask_planner = SubtaskPlanner(config.subtask)

        # 动作序列生成器
        self.sequence_generator = SequenceGenerator(config.sequence)

        # 规划评估器
        self.planning_evaluator = PlanningEvaluator(config.evaluator)

        # 反馈修正模块
        self.feedback_corrector = FeedbackCorrector(config.corrector)

    def forward(self, perception_features, language_features):
        """
        层次化任务规划
        """
        # 1. 任务理解与分解
        task_structure = self.task_decomposer(
            language_features, perception_features
        )

        # 2. 子任务规划
        subtask_plans = []
        for subtask in task_structure['subtasks']:
            subtask_plan = self.subtask_planner(
                subtask, perception_features
            )
            subtask_plans.append(subtask_plan)

        # 3. 动作序列生成
        action_sequences = []
        for subtask_plan in subtask_plans:
            action_sequence = self.sequence_generator(
                subtask_plan, perception_features
            )
            action_sequences.append(action_sequence)

        # 4. 规划质量评估
        planning_score = self.planning_evaluator(
            action_sequences, task_structure
        )

        # 5. 反馈修正（如果需要）
        if planning_score < self.threshold:
            corrected_plans = self.feedback_corrector(
                action_sequences, planning_score
            )
            action_sequences = corrected_plans

        return {
            'task_structure': task_structure,
            'subtask_plans': subtask_plans,
            'action_sequences': action_sequences,
            'planning_score': planning_score
        }

class HeterogeneousTaskCollaboration(nn.Module):
    """异构任务协同训练模块"""

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

        # 任务表示学习器
        self.task_representation = TaskRepresentation(config.representation)

        # 知识共享网络
        self.knowledge_sharing = KnowledgeSharingNetwork(config.sharing)

        # 任务特定适配器
        self.task_adapters = nn.ModuleDict({
            f'adapter_{task_id}': TaskAdapter(config.adapter)
            for task_id in config.task_ids
        })

        # 跨任务迁移模块
        self.cross_task_transfer = CrossTaskTransfer(config.transfer)

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

    def forward(self, task_plan, perception_features):
        """异构任务协同推理"""
        # 1. 任务表示学习
        task_repr = self.task_representation(task_plan)

        # 2. 知识共享与迁移
        shared_knowledge = self.knowledge_sharing(task_repr)

        # 3. 任务特定适配
        task_id = task_plan.get('task_id', 'default')
        if task_id in self.task_adapters:
            adapted_features = self.task_adapters[task_id](
                perception_features, shared_knowledge
            )
        else:
            # 元学习适应新任务
            adapted_features = self.meta_learner(
                perception_features, shared_knowledge, task_repr
            )

        # 4. 跨任务知识迁移
        transferred_features = self.cross_task_transfer(
            adapted_features, task_repr
        )

        return transferred_features

    def train_collaborative(self, heterogeneous_datasets):
        """异构任务协同训练"""
        for epoch in range(self.num_epochs):
            # 1. 采样任务批次
            task_batch = self.sample_tasks(heterogeneous_datasets)

            # 2. 内循环：任务特定学习
            task_losses = {}
            for task_name, (data, adapter) in task_batch.items():
                task_loss = self.train_single_task(data, adapter)
                task_losses[task_name] = task_loss

            # 3. 外循环：知识共享学习
            shared_loss = self.compute_shared_loss(task_losses)

            # 4. 元学习更新
            meta_loss = self.compute_meta_loss(task_losses)

            # 5. 总体更新
            total_loss = shared_loss + meta_loss
            self.update_parameters(total_loss)

class KnowledgeIsolation:
    """知识隔离技术"""

    def __init__(self, config):
        self.isolation_layers = nn.ModuleDict({
            f'isolation_{domain}': IsolationLayer(config.layer_dim)
            for domain in config.domains
        })

        # 知识蒸馏教师模型
        self.teacher_models = nn.ModuleDict({
            f'teacher_{domain}': TeacherModel(config.model_dim)
            for domain in config.domains
        })

    def isolate_domain_knowledge(self, features, domain):
        """隔离特定域的知识"""
        if domain in self.isolation_layers:
            isolated_features = self.isolation_layers[domain](features)
            return isolated_features
        return features

    def preserve_old_knowledge(self, new_features, old_domains):
        """保护旧知识不被遗忘"""
        preservation_loss = 0

        for domain in old_domains:
            # 使用教师模型指导
            teacher_output = self.teacher_models[domain](new_features)
            student_output = self.isolation_layers[domain](new_features)

            # 计算知识蒸馏损失
            distill_loss = F.kl_div(
                F.log_softmax(student_output, dim=-1),
                F.softmax(teacher_output, dim=-1),
                reduction='batchmean'
            )
            preservation_loss += distill_loss

        return preservation_loss / len(old_domains)

    def adaptive_isolation(self, features, task_context):
        """自适应知识隔离"""
        domain_predictions = self.predict_domain(features, task_context)

        isolated_features = []
        for i, domain_pred in enumerate(domain_predictions):
            # 选择最合适的隔离层
            best_domain = self.select_best_isolation(domain_pred)
            isolated_feat = self.isolate_domain_knowledge(
                features[i], best_domain
            )
            isolated_features.append(isolated_feat)

        return torch.stack(isolated_features)

class FastAdaptation:
    """快速适应机制"""

    def __init__(self, config):
        # 少样本学习组件
        self.few_shot_learner = FewShotLearner(config.few_shot)

        # 在线微调模块
        self.online_adapter = OnlineAdapter(config.online)

        # 元初始化网络
        self.meta_initialization = MetaInitialization(config.meta_init)

    def adapt_to_new_task(self, support_data, query_data):
        """快速适应新任务"""
        # 1. 少样本学习
        adapted_model = self.few_shot_learner.adapt(
            support_data, self.base_model
        )

        # 2. 在线微调
        fine_tuned_model = self.online_adapter.fine_tune(
            adapted_model, query_data
        )

        return fine_tuned_model

    def meta_train(self, task_distribution):
        """元训练阶段"""
        for episode in task_distribution:
            # 1. 采样任务
            support_tasks, query_tasks = episode.sample_tasks()

            # 2. 快速适应
            adapted_models = []
            for support_task in support_tasks:
                adapted_model = self.adapt_to_new_task(
                    support_task, query_tasks[0]
                )
                adapted_models.append(adapted_model)

            # 3. 元优化
            meta_loss = self.compute_meta_loss(
                adapted_models, query_tasks
            )

            # 4. 更新元参数
            self.meta_update(meta_loss)

    def rapid_online_adaptation(self, current_context):
        """实时在线适应"""
        # 1. 上下文理解
        context_features = self.extract_context_features(current_context)

        # 2. 适应性参数生成
        adaptive_params = self.generate_adaptive_params(context_features)

        # 3. 模型参数调整
        adapted_model = self.apply_adaptation(
            self.base_model, adaptive_params
        )

        return adapted_model

class ProgressiveCurriculum:
    """渐进式课程学习"""

    def __init__(self, config):
        self.difficulty_levels = config.difficulty_levels
        self.task_complexity_scorer = TaskComplexityScorer()
        self.curriculum_scheduler = CurriculumScheduler()

    def generate_curriculum(self, task_pool):
        """生成训练课程"""
        # 1. 任务复杂度评估
        task_complexities = {}
        for task in task_pool:
            complexity = self.task_complexity_scorer.score(task)
            task_complexities[task.id] = complexity

        # 2. 按复杂度排序
        sorted_tasks = sorted(
            task_pool,
            key=lambda t: task_complexities[t.id]
        )

        # 3. 分层课程生成
        curriculum = {}
        tasks_per_level = len(sorted_tasks) // len(self.difficulty_levels)

        for i, level in enumerate(self.difficulty_levels):
            start_idx = i * tasks_per_level
            end_idx = start_idx + tasks_per_level
            curriculum[level] = sorted_tasks[start_idx:end_idx]

        return curriculum

    def adaptive_curriculum_adjustment(self, model_performance):
        """自适应课程调整"""
        # 1. 性能分析
        difficulty_analysis = self.analyze_difficulty_performance(
            model_performance
        )

        # 2. 调整策略
        if difficulty_analysis['too_easy']:
            # 增加难度
            self.increase_curriculum_difficulty()
        elif difficulty_analysis['too_hard']:
            # 降低难度
            self.decrease_curriculum_difficulty()
        else:
            # 保持当前难度
            pass

        # 3. 重新采样任务
        return self.resample_tasks()

class MultiTaskOptimization:
    """多任务协同训练优化"""

    def __init__(self, config):
        self.task_weights = nn.ParameterDict({
            task_id: nn.Parameter(torch.tensor(1.0))
            for task_id in config.task_ids
        })

        # 梯度操作模块
        self.gradient_manipulation = GradientManipulation()

        # 任务相关性建模
        self.task_correlation = TaskCorrelationModel()

    def compute_balanced_loss(self, task_losses):
        """计算平衡的多任务损失"""
        # 1. 动态权重调整
        weighted_losses = {}
        for task_id, loss in task_losses.items():
            weight = torch.sigmoid(self.task_weights[task_id])
            weighted_losses[task_id] = weight * loss

        # 2. 梯度冲突检测
        gradients = {}
        for task_id in task_losses.keys():
            grad = torch.autograd.grad(
                weighted_losses[task_id],
                self.model.parameters(),
                retain_graph=True
            )
            gradients[task_id] = grad

        # 3. 梯度操作
        if self.detect_gradient_conflict(gradients):
            # 投影梯度下降
            manipulated_gradients = self.gradient_manipulation.project(
                gradients
            )
        else:
            # 标准梯度平均
            manipulated_gradients = self.gradient_manipulation.average(
                gradients
            )

        # 4. 应用梯度
        for param, grad in zip(
            self.model.parameters(),
            manipulated_gradients
        ):
            param.grad = grad

        return weighted_losses

    def update_task_correlation(self, task_performances):
        """更新任务相关性模型"""
        # 1. 性能相关性分析
        perf_correlation = self.analyze_performance_correlation(
            task_performances
        )

        # 2. 更新相关性矩阵
        self.task_correlation.update_matrix(perf_correlation)

        # 3. 调整训练策略
        self.adjust_training_strategy(perf_correlation)

class OpenWorldEvaluator:
    """开放式世界评估框架"""

    def __init__(self, config):
        self.evaluation_domains = config.evaluation_domains
        self.generalization_metrics = GeneralizationMetrics()
        self.adaptation_metrics = AdaptationMetrics()

    def evaluate_open_world_performance(self, model, test_scenarios):
        """评估开放式世界性能"""
        results = {}

        for scenario in test_scenarios:
            scenario_results = self.evaluate_scenario(model, scenario)
            results[scenario.name] = scenario_results

        # 1. 零样本泛化评估
        zero_shot_performance = self.evaluate_zero_shot_generalization(
            results
        )

        # 2. 少样本适应评估
        few_shot_performance = self.evaluate_few_shot_adaptation(
            model, test_scenarios
        )

        # 3. 在线学习评估
        online_learning_performance = self.evaluate_online_learning(
            model, test_scenarios
        )

        return {
            'scenario_results': results,
            'zero_shot': zero_shot_performance,
            'few_shot': few_shot_performance,
            'online_learning': online_learning_performance
        }

    def evaluate_domain_shift(self, model, source_domain, target_domain):
        """评估域迁移性能"""
        # 1. 源域性能
        source_performance = self.evaluate_domain_performance(
            model, source_domain
        )

        # 2. 目标域性能
        target_performance = self.evaluate_domain_performance(
            model, target_domain
        )

        # 3. 域迁移指标
        domain_gap = self.compute_domain_gap(
            source_performance, target_performance
        )

        return {
            'source_performance': source_performance,
            'target_performance': target_performance,
            'domain_gap': domain_gap
        }

class IntelligentManufacturing:
    """基于Pi0.5的智能制造系统"""

    def __init__(self, pi05_model):
        self.pi05 = pi05_model
        self.manufacturing_cells = ManufacturingCells()
        self.quality_control = QualityControlSystem()

    def handle_unexpected_situation(self, situation):
        """处理异常情况"""
        # 1. 情况理解
        context = self.analyze_situation(situation)

        # 2. 快速适应
        adapted_model = self.pi05.adapt_to_context(context)

        # 3. 解决方案生成
        solution = adapted_model.generate_solution(
            situation, context
        )

        # 4. 执行与验证
        success = self.execute_solution(solution)
        if not success:
            # 学习反馈
            self.pi05.learn_from_feedback(solution, success)

        return success

    def collaborative_task_execution(self, complex_task):
        """协同任务执行"""
        # 1. 任务分解
        task_decomposition = self.pi05.decompose_task(complex_task)

        # 2. 多机器人协调
        coordination_plan = self.pi05.plan_coordination(
            task_decomposition
        )

        # 3. 分布式执行
        execution_results = []
        for subtask in task_decomposition:
            robot_id = coordination_plan[subtask.id]
            result = self.manufacturing_cells.execute_on_robot(
                robot_id, subtask
            )
            execution_results.append(result)

        # 4. 结果整合
        final_result = self.integrate_results(execution_results)
        return final_result

class ServiceRobot:
    """基于Pi0.5的服务机器人"""

    def __init__(self, pi05_model):
        self.pi05 = pi05_model
        self.mobility_system = MobilitySystem()
        self.manipulation_system = ManipulationSystem()
        self.human_interface = HumanInterface()

    def interactive_learning(self, human_demo):
        """交互式学习"""
        # 1. 示范理解
        demo_analysis = self.pi05.analyze_demo(human_demo)

        # 2. 技能提取
        new_skill = self.pi05.extract_skill(demo_analysis)

        # 3. 技能集成
        self.pi05.integrate_skill(new_skill)

        # 4. 快速测试
        test_result = self.pi05.test_skill(new_skill)
        return test_result

    def personalized_assistance(self, user_preferences, task):
        """个性化辅助"""
        # 1. 用户建模
        user_model = self.build_user_model(user_preferences)

        # 2. 个性化规划
        personalized_plan = self.pi05.plan_with_preferences(
            task, user_model
        )

        # 3. 自适应执行
        execution_result = self.adaptive_execution(
            personalized_plan, user_model
        )

        return execution_result