AI Transparency in Humanoid Robotics
AI transparency is a fundamental requirement in humanoid robotics systems, ensuring that decision-making processes are interpretable and traceable. This transparency is essential for debugging, validation, and ethical compliance of autonomous behaviors, as mandated by the Physical-AI-Humanoid-Robotic Constitution.
The Need for AI Transparency
Humanoid robots operate in close proximity to humans, making their decision-making processes a matter of safety and trust. Transparency in AI systems ensures:
- Auditability: The ability to trace how decisions were made
- Explainability: Understanding why a particular action was taken
- Accountability: Clear attribution of actions to specific AI components
- Safety: The ability to understand and predict AI behavior
Transparency Architecture
Logging Framework
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from std_msgs.msg import String
import json
import datetime
from enum import Enum
class DecisionType(Enum):
PERCEPTION = "perception"
NAVIGATION = "navigation"
MANIPULATION = "manipulation"
INTERACTION = "interaction"
SAFETY = "safety"
class TransparencyLogger(Node):
def __init__(self):
super().__init__('transparency_logger')
# Publisher for transparency logs
self.log_pub = self.create_publisher(String, '/ai_transparency_log', 10)
# Initialize log storage
self.decision_log = []
self.log_file = open('/logs/ai_decisions.json', 'a')
self.get_logger().info('AI Transparency Logger initialized')
def log_decision(self, decision_type, decision_data, reasoning, node_id=None):
"""Log an AI decision with reasoning and context for transparency"""
timestamp = self.get_clock().now().to_msg()
log_entry = {
'timestamp': timestamp.sec + timestamp.nanosec / 1e9,
'decision_type': decision_type.value if isinstance(decision_type, DecisionType) else decision_type,
'decision_data': decision_data,
'reasoning': reasoning,
'node_id': node_id or self.get_name(),
'decision_id': len(self.decision_log)
}
# Store in memory
self.decision_log.append(log_entry)
# Write to persistent log
self.log_file.write(json.dumps(log_entry) + '\n')
self.log_file.flush()
# Publish to topic for external monitoring
log_msg = String()
log_msg.data = json.dumps(log_entry)
self.log_pub.publish(log_msg)
self.get_logger().info(f'Logged decision: {decision_type.value}')
def log_perception_decision(self, objects_detected, confidence_threshold, action_taken):
"""Log perception system decisions"""
decision_data = {
'objects_detected': objects_detected,
'confidence_threshold': confidence_threshold,
'action_taken': action_taken
}
reasoning = f'Detected {len(objects_detected)} objects with confidence above {confidence_threshold}, taking action: {action_taken}'
self.log_decision(
DecisionType.PERCEPTION,
decision_data,
reasoning
)
def log_navigation_decision(self, goal_pose, path, safety_check_passed):
"""Log navigation system decisions"""
decision_data = {
'goal_pose': goal_pose,
'path': path,
'safety_check_passed': safety_check_passed
}
reasoning = f'Calculated path to goal with safety check {"PASSED" if safety_check_passed else "FAILED"}'
self.log_decision(
DecisionType.NAVIGATION,
decision_data,
reasoning
)
def get_decision_history(self, decision_type=None, time_window=None):
"""Retrieve decision history with optional filters"""
if decision_type:
if isinstance(decision_type, DecisionType):
decision_type = decision_type.value
filtered = [entry for entry in self.decision_log
if entry['decision_type'] == decision_type]
else:
filtered = self.decision_log[:]
if time_window:
current_time = self.get_clock().now().seconds_nanoseconds()[0]
filtered = [entry for entry in filtered
if current_time - entry['timestamp'] <= time_window]
return filtered
def __del__(self):
"""Clean up log file"""
if hasattr(self, 'log_file') and self.log_file:
self.log_file.close()
Decision Explanation System
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from std_msgs.msg import String
from ai_transparency_msgs.msg import DecisionExplanation # hypothetical message type
class DecisionExplainer(Node):
def __init__(self):
super().__init__('decision_explainer')
# Publisher for explanations
self.explanation_pub = self.create_publisher(DecisionExplanation, '/decision_explanation', 10)
# Subscription to request explanations for specific decisions
self.request_sub = self.create_subscription(
String,
'/request_explanation',
self.request_callback,
10
)
self.transparency_logger = TransparencyLogger()
self.get_logger().info('Decision Explainer initialized')
def request_callback(self, msg):
"""Handle request for explanation of a specific decision"""
try:
# Parse the request message (assumes it contains decision ID)
request_data = json.loads(msg.data)
decision_id = request_data.get('decision_id')
# Retrieve the decision from logger
all_decisions = self.transparency_logger.decision_log
target_decision = next((d for d in all_decisions if d['decision_id'] == decision_id), None)
if target_decision:
explanation = self.generate_explanation(target_decision)
# Publish the explanation
explanation_msg = DecisionExplanation()
explanation_msg.decision_id = decision_id
explanation_msg.explanation = explanation
explanation_msg.confidence_score = 0.95 # Placeholder
self.explanation_pub.publish(explanation_msg)
else:
self.get_logger().error(f'Decision ID {decision_id} not found')
except Exception as e:
self.get_logger().error(f'Error processing explanation request: {e}')
def generate_explanation(self, decision):
"""Generate a human-readable explanation of a decision"""
decision_type = decision['decision_type']
decision_data = decision['decision_data']
reasoning = decision['reasoning']
explanation = f"Decision Type: {decision_type}\n"
explanation += f"Reasoning: {reasoning}\n"
explanation += f"Time: {datetime.datetime.fromtimestamp(decision['timestamp'])}\n"
explanation += f"Node: {decision['node_id']}\n"
explanation += f"Data: {json.dumps(decision_data, indent=2)}"
return explanation
Transparent Perception Systems
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image, LaserScan
from vision_msgs.msg import Detection2DArray
from geometry_msgs.msg import PointStamped
import numpy as np
from ai_transparency.logger import TransparencyLogger, DecisionType
class TransparentPerceptionNode(Node):
def __init__(self):
super().__init__('transparent_perception')
# Publishers and subscribers
self.image_sub = self.create_subscription(
Image,
'/camera/image',
self.image_callback,
10
)
self.scan_sub = self.create_subscription(
LaserScan,
'/scan',
self.scan_callback,
10
)
self.detection_pub = self.create_publisher(Detection2DArray, '/detections', 10)
# Initialize transparency logger
self.transparency_logger = TransparencyLogger(self)
# Perception parameters
self.detection_threshold = 0.5
self.min_confidence = 0.7
self.get_logger().info('Transparent Perception Node initialized')
def image_callback(self, msg):
"""Process image with transparency logging"""
try:
# Perform object detection
detections = self.perform_detection(msg)
# Filter by confidence
high_conf_detections = [d for d in detections if d.confidence > self.min_confidence]
# Log the perception decision
decision_data = {
'input_image_id': msg.header.stamp.sec,
'total_detections': len(detections),
'high_confidence_detections': len(high_conf_detections),
'confidence_threshold': self.min_confidence
}
reasoning = f'Detected {len(detections)} objects, {len(high_conf_detections)} with confidence > {self.min_confidence}'
self.transparency_logger.log_decision(
DecisionType.PERCEPTION,
decision_data,
reasoning
)
# Publish detections
detection_msg = Detection2DArray()
detection_msg.header = msg.header
detection_msg.detections = high_conf_detections
self.detection_pub.publish(detection_msg)
except Exception as e:
self.get_logger().error(f'Error in perception: {e}')
# Log the error decision
decision_data = {
'input_image_id': msg.header.stamp.sec,
'error': str(e)
}
self.transparency_logger.log_decision(
DecisionType.PERCEPTION,
decision_data,
f'Perception failed with error: {e}'
)
def scan_callback(self, msg):
"""Process laser scan data with transparency"""
try:
# Process scan for obstacles
obstacles = self.detect_obstacles_from_scan(msg)
# Log the decision
decision_data = {
'scan_id': msg.header.stamp.sec,
'obstacle_count': len(obstacles),
'min_distance': min([o.distance for o in obstacles]) if obstacles else float('inf')
}
reasoning = f'Detected {len(obstacles)} obstacles, minimum distance: {decision_data["min_distance"]:.2f}m'
self.transparency_logger.log_decision(
DecisionType.PERCEPTION,
decision_data,
reasoning
)
except Exception as e:
self.get_logger().error(f'Error processing scan: {e}')
decision_data = {
'scan_id': msg.header.stamp.sec,
'error': str(e)
}
self.transparency_logger.log_decision(
DecisionType.PERCEPTION,
decision_data,
f'Scan processing failed with error: {e}'
)
def perform_detection(self, image_msg):
"""Perform object detection (simplified placeholder)"""
# In a real implementation, this would run the actual detection algorithm
return []
def detect_obstacles_from_scan(self, scan_msg):
"""Detect obstacles from laser scan (simplified placeholder)"""
# In a real implementation, this would analyze the scan data
return []
Transparent Navigation System
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from nav2_msgs.action import NavigateToPose
from rclpy.action import ActionClient
from geometry_msgs.msg import PoseStamped, Point
from visualization_msgs.msg import Marker
from ai_transparency.logger import TransparencyLogger, DecisionType
class TransparentNavigation(Node):
def __init__(self):
super().__init__('transparent_navigation')
# Action client for navigation
self.nav_to_pose_client = ActionClient(
self,
NavigateToPose,
'navigate_to_pose'
)
# Initialize transparency logger
self.transparency_logger = TransparencyLogger(self)
# Initialize safety systems
self.safety_checker = SafetyValidator(self)
self.get_logger().info('Transparent Navigation initialized')
def navigate_to_pose_with_explanation(self, x, y, theta):
"""Navigate to pose with full transparency"""
goal_pose = self.create_pose_stamped(x, y, theta)
# Validate safety before navigation
is_safe, safety_reason = self.safety_checker.validate_navigation(goal_pose)
if not is_safe:
self.get_logger().error(f'Navigation unsafe: {safety_reason}')
# Log safety decision
decision_data = {
'goal_pose': {'x': x, 'y': y, 'theta': theta},
'safety_check': 'FAILED',
'reason': safety_reason
}
self.transparency_logger.log_decision(
DecisionType.SAFETY,
decision_data,
f'Navigation to ({x}, {y}) blocked due to safety concerns: {safety_reason}'
)
return False
# Plan path and check for obstacles
path = self.plan_path(goal_pose)
obstacle_check = self.check_path_for_obstacles(path)
# Log the navigation decision
decision_data = {
'goal_pose': {'x': x, 'y': y, 'theta': theta},
'path_length': len(path),
'obstacles_detected': obstacle_check['obstacles'],
'safety_check_passed': is_safe
}
reasoning = f'Planning to navigate to ({x}, {y}), path length: {len(path)}, obstacles: {obstacle_check["obstacles"]}'
self.transparency_logger.log_decision(
DecisionType.NAVIGATION,
decision_data,
reasoning
)
# Proceed with navigation if path is clear
if not obstacle_check['obstacles']:
return self.send_navigation_goal(goal_pose)
else:
self.get_logger().warn('Obstacles detected in path, replanning or aborting')
return False
def send_navigation_goal(self, pose_stamped):
"""Send navigation goal with transparency logging"""
goal_msg = NavigateToPose.Goal()
goal_msg.pose = pose_stamped
if not self.nav_to_pose_client.wait_for_server(timeout_sec=5.0):
self.get_logger().error('Navigation action server not available')
return False
# Log the actual navigation command
decision_data = {
'goal': pose_stamped.pose
}
self.transparency_logger.log_decision(
DecisionType.NAVIGATION,
decision_data,
f'Sending navigation command to ({pose_stamped.pose.position.x}, {pose_stamped.pose.position.y})'
)
future = self.nav_to_pose_client.send_goal_async(
goal_msg,
feedback_callback=self.nav_feedback_callback
)
future.add_done_callback(self.nav_result_callback)
return True
def nav_feedback_callback(self, feedback):
"""Handle navigation feedback with logging"""
current_pose = feedback.feedback.current_pose.pose
distance_remaining = feedback.feedback.distance_remaining
decision_data = {
'current_position': {
'x': current_pose.position.x,
'y': current_pose.position.y
},
'distance_remaining': distance_remaining
}
reasoning = f'Navigation in progress, {distance_remaining:.2f}m remaining'
self.transparency_logger.log_decision(
DecisionType.NAVIGATION,
decision_data,
reasoning
)
def nav_result_callback(self, future):
"""Handle navigation result with logging"""
result = future.result().result
decision_data = {
'success': result,
'result_code': result.result if hasattr(result, 'result') else 'unknown'
}
status = 'SUCCESS' if result else 'FAILED'
reasoning = f'Navigation completed with status: {status}'
self.transparency_logger.log_decision(
DecisionType.NAVIGATION,
decision_data,
reasoning
)
def create_pose_stamped(self, x, y, theta):
"""Create a PoseStamped message"""
pose_stamped = PoseStamped()
pose_stamped.header.stamp = self.get_clock().now().to_msg()
pose_stamped.header.frame_id = 'map'
pose_stamped.pose.position.x = float(x)
pose_stamped.pose.position.y = float(y)
pose_stamped.pose.position.z = 0.0
# Convert theta to quaternion
import math
pose_stamped.pose.orientation.z = math.sin(theta / 2.0)
pose_stamped.pose.orientation.w = math.cos(theta / 2.0)
return pose_stamped
def plan_path(self, goal_pose):
"""Plan a path to the goal (simplified placeholder)"""
# In a real implementation, this would use NavFn, A*, or other path planning
return [goal_pose.pose.position] # Simplified
def check_path_for_obstacles(self, path):
"""Check the planned path for obstacles (simplified placeholder)"""
# In a real implementation, this would check against costmap
return {'obstacles': False, 'obstacle_positions': []}
Safety Validator with Transparency
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from geometry_msgs.msg import Pose
from ai_transparency.logger import TransparencyLogger, DecisionType
class SafetyValidator(Node):
def __init__(self, parent_node):
super().__init__('safety_validator')
# Store reference to parent node for logging
self.parent_node = parent_node
self.transparency_logger = TransparencyLogger(parent_node)
# Initialize safety parameters
self.min_distance_to_human = 0.5 # meters
self.max_navigation_speed = 0.5 # m/s
self.emergency_stop_enabled = True
self.get_logger().info('Safety Validator initialized')
def validate_navigation(self, goal_pose):
"""Validate if navigation to goal pose is safe"""
try:
# Check if goal is in human safety zone
is_in_safe_zone = self.check_human_proximity(goal_pose.pose.position)
if not is_in_safe_zone:
return False, "Goal is too close to humans"
# Check if path is obstacle-free
path_clear = self.check_path_clear(goal_pose)
if not path_clear:
return False, "Path to goal has obstacles"
# Check if goal is in designated safe areas
is_in_safe_area = self.check_safe_area(goal_pose.pose.position)
if not is_in_safe_area:
return False, "Goal is in restricted area"
# Log successful safety check
decision_data = {
'goal_position': {
'x': goal_pose.pose.position.x,
'y': goal_pose.pose.position.y
},
'human_proximity_check': True,
'path_clear_check': True,
'safe_area_check': True
}
self.transparency_logger.log_decision(
DecisionType.SAFETY,
decision_data,
'Navigation safety check passed for all criteria'
)
return True, "All safety checks passed"
except Exception as e:
self.get_logger().error(f'Safety validation error: {e}')
# Log the error
decision_data = {
'goal_pose': goal_pose,
'error': str(e)
}
self.transparency_logger.log_decision(
DecisionType.SAFETY,
decision_data,
f'Safety validation failed with error: {e}'
)
return False, f"Validation error: {e}"
def check_human_proximity(self, position):
"""Check if position is near humans"""
# In a real implementation, this would check against human tracker
# For this example, we'll just return True (safe)
return True
def check_path_clear(self, goal_pose):
"""Check if path to goal is clear of obstacles"""
# In a real implementation, this would check the costmap
# For this example, we'll just return True (clear)
return True
def check_safe_area(self, position):
"""Check if position is in a safe area"""
# In a real implementation, this would check against a safe area map
# For this example, we'll just return True (safe)
return True
Human-in-the-Loop Override System
#!/usr/bin/env python3
import rclpy
from rclpy.node import Node
from std_msgs.msg import Bool, String
from ai_transparency.logger import TransparencyLogger, DecisionType
class HumanOverrideSystem(Node):
def __init__(self):
super().__init__('human_override_system')
# Publishers for override control
self.emergency_stop_pub = self.create_publisher(Bool, '/emergency_stop', 10)
self.override_status_pub = self.create_publisher(String, '/override_status', 10)
# Subscription for human override commands
self.override_sub = self.create_subscription(
String,
'/human_override',
self.override_callback,
10
)
# Initialize transparency logger
self.transparency_logger = TransparencyLogger(self)
# Override state
self.override_active = False
self.override_reason = ""
self.get_logger().info('Human Override System initialized')
def override_callback(self, msg):
"""Handle human override commands"""
try:
command_data = json.loads(msg.data)
command = command_data.get('command')
reason = command_data.get('reason', 'Manual override')
if command == 'EMERGENCY_STOP':
self.activate_emergency_stop(reason)
elif command == 'RESUME':
self.deactivate_override(reason)
elif command == 'PAUSE':
self.pause_operations(reason)
else:
self.get_logger().warn(f'Unknown override command: {command}')
return
# Log the override decision
decision_data = {
'command': command,
'reason': reason,
'timestamp': self.get_clock().now().seconds_nanoseconds()[0]
}
self.transparency_logger.log_decision(
DecisionType.INTERACTION,
decision_data,
f'Human override executed: {command} - {reason}'
)
except Exception as e:
self.get_logger().error(f'Error processing override: {e}')
decision_data = {
'raw_command': msg.data,
'error': str(e)
}
self.transparency_logger.log_decision(
DecisionType.INTERACTION,
decision_data,
f'Override processing failed with error: {e}'
)
def activate_emergency_stop(self, reason):
"""Activate emergency stop with human override"""
self.override_active = True
self.override_reason = reason
stop_msg = Bool()
stop_msg.data = True
self.emergency_stop_pub.publish(stop_msg)
status_msg = String()
status_msg.data = f'EMERGENCY_STOP: {reason}'
self.override_status_pub.publish(status_msg)
self.get_logger().error(f'EMERGENCY STOP ACTIVATED: {reason}')
def deactivate_override(self, reason):
"""Deactivate override with human confirmation"""
self.override_active = False
self.override_reason = ""
stop_msg = Bool()
stop_msg.data = False
self.emergency_stop_pub.publish(stop_msg)
status_msg = String()
status_msg.data = f'RESUMED: {reason}'
self.override_status_pub.publish(status_msg)
self.get_logger().info(f'Operations resumed: {reason}')
def pause_operations(self, reason):
"""Pause operations without full emergency stop"""
# Implementation would send pause commands to various systems
status_msg = String()
status_msg.data = f'PAUSED: {reason}'
self.override_status_pub.publish(status_msg)
self.get_logger().info(f'Operations paused: {reason}')
AI transparency in humanoid robotics is not just a nice-to-have feature—it's a constitutional requirement for safe and ethical operation. By implementing comprehensive logging, explanation systems, and human oversight capabilities, we ensure that these complex AI systems remain accountable and trustworthy in human environments. The transparency mechanisms described in this section provide the foundation for building humanoid robots that operate safely and ethically alongside humans.