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Embodied AI Evaluation

Benchmarking world models and embodied agents in closed-loop interactive environments

advanced5 / 8

World-In-World Benchmark Platform — Platform Architecture — Part 2

_state = self.get_state() reward = self.calculate_reward() done = self.check_termination() return new_state, reward, done def get_observation(self): # Multi-modal observation generation visual = self.sensor_suite.get_visual() audio = self.sensor_suite.get_audio() proprioceptive = self.sensor_suite.get_proprioceptive() return { 'visual': visual, 'audio': audio, 'proprioceptive': proprioceptive }
  1. Agent Interface

    class EmbodiedAgent:
        def __init__(self, architecture):
            self.perception_module = PerceptionModule()
            self.planning_module = PlanningModule()
            self.action_module = ActionModule()
            self.memory_system = MemorySystem()
    
        def act(self, observation):
    

Process multi-modal observation

       perception = self.perception_module.process(observation)
       plan = self.planning_module.generate_plan(perception)
       action = self.action_module.execute_action(plan)
       return action

   def update(self, experience):

Learning and adaptation

       self.memory_system.store(experience)
       self.update_models(experience)

### Data Collection and Analysis

1. **Experience Logging**
- State-action-reward sequences
- Multi-modal sensor data
- Internal agent states
- Environmental parameters

2. **Performance Analytics**
- Real-time performance monitoring
- Statistical analysis tools
- Visualization dashboards
- Comparative analysis frameworks

## Benchmark Categories

### Navigation and Exploration

1. **Spatial Navigation**
- Path planning and execution
- Obstacle avoidance
- Mapping and localization
- Goal-directed movement

2. **Exploration Strategies**
- Curiosity-driven exploration
- Information gathering
- Risk assessment and management
- Efficient coverage algorithms

### Object Manipulation

1. **Grasping and Manipulation**
- Object recognition and localization
- Grasp planning and execution
- Fine motor control
- Tool use and manipulation

2. **Physical Interaction**
- Force control and feedback
- Physical property understanding
- Cause-effect relationships
- Dynamic interaction handling

### Social Interaction

1. **Communication**
- Language understanding and generation
- Non-verbal communication
- Social cue recognition
- Collaborative behavior

2. **Collaboration**
- Team coordination
- Shared goal achievement
- Role allocation
- Conflict resolution

## Advanced Evaluation Concepts

### Meta-Learning Assessment

1. **Learning to Learn**
- Rapid adaptation capabilities
- Few-shot learning performance
- Meta-reasoning abilities
- Transfer efficiency

2. **Curriculum Learning**
- Progressive skill acquisition
- Self-directed learning
- Difficulty estimation
- Learning strategy optimizatio
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