Add advanced robotics AGI feature modules

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Original prompt

Add Advanced Features to Robotics AGI System

Objective

Enhance the Agentic AGI robotics system with cutting-edge advanced features including meta-learning, multimodal fusion, hierarchical planning, symbolic reasoning, emotion recognition, swarm intelligence, self-diagnosis, explainability, and more state-of-the-art capabilities.

Technology Stack

• Python 3.10+
• PyTorch 2.0+ - Advanced deep learning
• Transformers - Vision-language models
• Ray/RLlib - Distributed learning
• Neo4j/NetworkX - Knowledge graphs
• ONNX/TensorRT - Model optimization
• Weights & Biases - Experiment tracking

Advanced Features to Implement

---

1. META-LEARNING & SELF-IMPROVEMENT

1.1 Learn-to-Learn System (learning/meta_learning/)

class MetaLearner:
    """
    Meta-learning: Learn how to learn new tasks faster
    
    Approaches:
    - MAML (Model-Agnostic Meta-Learning)
    - Reptile
    - Meta-RL
    - Few-shot learning
    - Zero-shot learning
    """
    
    def meta_train(self, task_distribution):
        """Train on distribution of tasks"""
        
    def few_shot_adapt(self, new_task, examples):
        """Adapt to new task with few examples (1-5 shots)"""
        
    def zero_shot_transfer(self, task_description):
        """Perform task from description only"""

Features:

• Learn new tasks from 1-5 demonstrations
• Transfer knowledge across tasks
• Continual learning without forgetting
• Self-curriculum generation

1.2 Curriculum Learning (learning/curriculum/)

class CurriculumGenerator:
    """
    Automatically generate learning curriculum
    - Start with easy tasks
    - Progressively increase difficulty
    - Adapt based on performance
    """
    
    def generate_curriculum(self, goal_task):
        """Generate sequence of tasks leading to goal"""
        
    def adapt_difficulty(self, performance):
        """Adjust difficulty based on success rate"""

1.3 Self-Supervised Learning (learning/self_supervised/)

class SelfSupervisedLearner:
    """
    Learn from unlabeled data:
    - Contrastive learning (SimCLR, MoCo)
    - Predictive learning
    - Auto-encoding
    - Self-prediction
    """
    
    def learn_from_exploration(self, environment):
        """Learn representations from exploration"""
        
    def predict_future_states(self, trajectory):
        """Learn world model by predicting future"""

---

2. MULTIMODAL FUSION & PERCEPTION

2.1 Advanced Sensor Fusion (perception/multimodal/)

class MultimodalFusion:
    """
    Fuse multiple sensory modalities:
    - Vision (RGB, depth, thermal)
    - Audio (sound localization, speech)
    - Tactile (force, pressure, texture)
    - Proprioception (joint angles, velocities)
    - IMU (acceleration, orientation)
    """
    
    def early_fusion(self, modalities):
        """Fuse at input level"""
        
    def late_fusion(self, modalities):
        """Fuse at decision level"""
        
    def attention_fusion(self, modalities):
        """Attention-based fusion (cross-modal attention)"""
        
    def transformer_fusion(self, modalities):
        """Transformer-based multimodal fusion"""

2.2 Vision-Language Models (perception/vlm/)

class VisionLanguageModel:
    """
    Advanced vision-language understanding:
    - CLIP, BLIP, LLaVA
    - Visual reasoning
    - Visual question answering
    - Image captioning with reasoning
    """
    
    def visual_reasoning(self, image, question):
        """Answer complex visual questions"""
        
    def generate_detailed_caption(self, image):
        """Generate rich scene descriptions"""
        
    def ground_language_to_vision(self, text, image):
        """Find objects/regions matching text"""

2.3 Active Perception (perception/active/)

class ActivePerception:
    """
    Actively control sensors to gather information:
    - Next-best-view planning
    - Attention-guided perception
    - Information gain maximization
    """
    
    def plan_next_view(self, current_belief):
        """Plan camera movement to gather info"""
        
    def focus_attention(self, scene):
        """Direct attention to important regions"""
        
    def minimize_uncertainty(self, belief_state):
        """Gather info to reduce uncertainty"""

---

3. HIERARCHICAL & NEURAL PLANNING

3.1 Hierarchical Planning (planning/hierarchical/)

class HierarchicalPlanner:
    """
    Multi-level planning:
    - High-level: Mission planning (days/hours)
    - Mid-level: Task planning (minutes)
    - Low-level: Motion planning (seconds)
    """
    
    def plan_mission(self, goal):
        """Long-term mission planning"""
        
    def decompose_into_tasks(self, mission):
        """Break mission into tasks"""
        
    def plan_motion(self, task):
        """Plan detailed motions"""
        
    def replan_online(self, exe...

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