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