🤖🧠 Deep mind AI blog series

Distance-Based Perception System (Outdated)

Oct 06, 20257 min read

📋 Summary

Objective: Transform the robot navigation system from binary obstacle detection to continuous distance-based perception, mimicking real-world sensors (LIDAR, radar) for improved generalization and real-world transfer capability.

Key Innovation: Replace discrete obstacle labels with continuous distance measurements to nearest obstacles, creating a sensor-realistic training environment.

Expected Outcome: 85%+ accuracy with superior generalization to novel environments and direct transferability to real robots.

🔍 Problem Analysis

Current System Limitations

Binary Perception Issues

# Current Implementation (Binary)
┌─────┬─────┬─────┐
│ 0.0 │ 1.0 │ 0.0 │ ← Binary obstacle detection
├─────┼─────┼─────┤
│ 0.0 │ R   │ 0.0 │ ← Robot position
├─────┼─────┼─────┤
│ 0.0 │ 1.0 │ 0.0 │ ← No distance information
└─────┴─────┴─────┘
Problems:
❌ No distance information (obstacle at distance 1 vs 10)
❌ No boundary type distinction (wall vs obstacle)
❌ Poor generalization to novel environments
❌ Not transferable to real robot sensors

Information Loss

  • Missing Proximity: Can’t distinguish near vs far obstacles
  • No Gradient: No spatial gradient information for navigation
  • Binary Decisions: Limited to obstacle/no-obstacle decisions
  • Training-Specific: Only works in simulated grid environments

Real-World Requirements

Real robots use sensors that provide:

  • ✅ Distance measurements (LIDAR: 0.1-100m range)
  • ✅ Continuous values (not discrete categories)
  • ✅ Spatial gradients (closer = more dangerous)
  • ✅ Boundary-agnostic (doesn’t matter if it’s a wall or obstacle)

🎯 Solution Design: Distance-Based Perception

Core Concept

Transform the perception system from semantic labeling to distance sensing:

# New Implementation (Distance-Based)
┌─────┬─────┬─────┐
│ 0.8 │ 0.0 │ 0.9 │ ← Distance to nearest obstacle
├─────┼─────┼─────┤
│ 1.0 │ R   │ 1.0 │ ← Normalized [0,1]: 1.0=far, 0.0=close
├─────┼─────┼─────┤
│ 0.7 │ 0.0 │ 0.6 │ ← Continuous distance field
└─────┴─────┴─────┘
Benefits:
✅ Rich distance information
✅ Natural gradient for navigation
✅ Generalizes to any environment
✅ Maps directly to real sensors

Training Data

🧠 Feature Breakdown: ✅ Enhanced 5×5 Mode: 37 features • Perception: 25 features (5×5 grid) • History: 12 features (3 actions × 4 one-hot)

Mathematical Foundation

Distance Field Calculation

 
def distance_field(environment, position, max_distance):
 
"""
 
Calculate distance to nearest obstacle in each direction
 
Distance metric: Manhattan distance (for grid navigation)
 
Normalization: d_norm = min(d_actual / d_max, 1.0)
 
Returns:
 
Normalized distance field where:
 
- 0.0 = Immediate obstacle
 
- 1.0 = No obstacle within max_distance
 
- 0.5 = Obstacle at medium distance
 
"""
 
for each cell in perception_window:
 
if cell_has_obstacle:
 
distance_value = 0.0
 
else:
 
distance_value = bfs_distance_to_nearest_obstacle(cell)
 
distance_value = min(distance_value / max_distance, 1.0)
 
return distance_field

BFS Distance Algorithm

 
def bfs_distance_to_nearest_obstacle(environment, start_position):
 
"""
 
Breadth-First Search to find nearest obstacle
 
Time Complexity: O(V + E) where V = grid cells, E = connections
 
Space Complexity: O(V) for visited set
 
Returns: Manhattan distance to nearest obstacle
 
"""
 
queue = [(start_x, start_y, 0)] # (x, y, distance)
 
visited = {start_position}
 
while queue:
 
x, y, dist = queue.pop(0)
 
# Check 4-connected neighbors
 
for dx, dy in [(0,1), (1,0), (0,-1), (-1,0)]:
 
nx, ny = x + dx, y + dy
 
# Out of bounds = boundary obstacle
 
if not in_bounds(nx, ny):
 
return dist + 1
 
# Found obstacle
 
if environment[nx, ny] == 1:
 
return dist + 1
 
# Continue search
 
if (nx, ny) not in visited:
 
visited.add((nx, ny))
 
queue.append((nx, ny, dist + 1))
 
return max_distance # No obstacle found

🏗️ System Architecture Design

1. Enhanced Perception Extractor

📊 Training Data Generation

Role of A Pathfinding*

The A* algorithm’s role remains unchanged but becomes more critical:

Why A is Still Essential*

  1. Optimal Demonstrations: Provides ground truth for navigation decisions

  2. Distance-Aware Paths: A* naturally considers distance to obstacles

  3. Consistent Training: Same optimal actions regardless of perception encoding

  4. Generalization Base: Neural network learns to replicate A* decisions

Enhanced A Integration*

Distance-Based Training Data Properties

Feature Statistics

 
# Binary Perception (Current)
 
Feature Distribution:
 
- 0.0: 60% (obstacles/boundaries)
 
- 1.0: 40% (free space)
 
  
 
# Distance-Based Perception (New)
 
Feature Distribution:
 
- 0.0: 15% (immediate obstacles)
 
- 0.1-0.9: 70% (various distances)
 
- 1.0: 15% (far from obstacles)
 
  
 
# Information Content
 
Binary: 1 bit per cell
 
Distance: ~3-4 bits per cell (continuous values)

Training Data Advantages

  1. Richer Information: 3-4x more information per feature

  2. Spatial Gradients: Natural navigation cues

  3. Boundary Detection: Emergent wall detection from distance gradients

  4. Dead End Recognition: Low distances in multiple directions

🔬 Implementation Strategy

Phase 1: Drop-in Replacement (Quick Test)

Phase 2: Full Distance Field Implementation

Phase 3: Configuration Integration

 
# data_config.yaml additions
 
perception_settings:
 
perception_size: 5 # 5×5 grid
 
use_distance_field: true # Enable distance-based sensing
 
max_sensing_distance: 5 # Normalization factor
 
distance_metric: "manhattan" # or "euclidean"
 
# nn_config.yaml updates
 
model:
 
input_size: 37 # Same as before (25 perception + 12 history)
 
perception_size: 25 # 5×5 distance field
 
history_size: 12 # Unchanged
 
# No architecture changes needed!

📈 Expected Performance Improvements

Quantitative Predictions

| Metric | Binary (Current) | Distance-Based | Improvement |

|--------|-----------------|----------------|-------------|

| Training Accuracy | 85.6% | 87-90% | +2-4% |

| Validation Accuracy | 79.5% | 82-85% | +3-5% |

| Overfitting Gap | 6.1% | 3-5% | -1-3% |

| Novel Environment | ~50% | 75-80% | +25-30% |

| Training Time | Baseline | +20-30% | Acceptable |

Qualitative Improvements

  • Wall Following: Emergent from distance gradients

  • Corner Navigation: Smooth turns using distance information

  • Dead End Avoidance: Automatic detection from low distances

  • Obstacle Avoidance: More nuanced than binary collision detection

Generalization Benefits

  • Unseen Obstacle Patterns: Better handling of novel layouts

  • Scale Invariance: Works with different environment sizes

  • Sensor Transfer: Direct mapping to LIDAR/radar data

🎯 Success Criteria

Primary Goals

  1. Maintain Current Performance: ≥79% validation accuracy

  2. Improve Generalization: +20% accuracy on novel environments

  3. Reduce Overfitting: <5% training-validation gap

  4. Enable Real Transfer: Compatible with LIDAR data format

Secondary Goals

  1. Faster Convergence: Fewer epochs to reach target accuracy

  2. Better Corner Navigation: Smooth turning behaviors

  3. Automatic Dead End Detection: No hard-coded logic needed

  4. Scalable Architecture: Works with different environment sizes

📚 Biological and Engineering Foundations

Biological Inspiration

  • 🦇 Echolocation: Bats use time-delay to measure distances

  • 🐀 Whisker Sensing: Rats measure proximity continuously

  • 👁️ Visual Depth: Human vision provides continuous depth perception

  • 🧠 Spatial Memory: Hippocampus stores distance-based spatial maps

Engineering Principles

  • 📡 LIDAR: Time-of-flight distance measurement

  • 📻 Radar: Electromagnetic wave reflection timing

  • 🔊 Sonar: Acoustic wave travel time

  • 📷 Depth Cameras: Stereo vision or time-of-flight

Mathematical Foundation

  • Distance Fields: Level-set methods in computational geometry

  • BFS Algorithms: Graph traversal for nearest neighbor search

  • Normalization: Standard practice in sensor data processing

  • Feature Engineering: Converting raw measurements to useful features

🎓 Key Insights and Takeaways

Why Distance-Based Perception Works

  1. Information Richness: Continuous values contain more information than binary

  2. Natural Gradients: Distance fields provide natural navigation cues

  3. Emergent Behaviors: Complex behaviors emerge from simple distance rules

  4. Sensor Compatibility: Direct mapping to real robot sensors

  5. Generalization: Works across different environments and scales

Implementation Philosophy

“The best neural networks learn from data that resembles the real world. Real sensors provide distances, not semantic labels. By mimicking sensor physics, we create models that generalize.”

Future Extensions

  • Multi-Sensor Fusion: Combine distance with other sensor modalities

  • Temporal Dynamics: Add velocity and acceleration information

  • 3D Navigation: Extend to three-dimensional environments

  • Dynamic Obstacles: Handle moving obstacles using distance predictions

🚀 Conclusion

The distance-based perception system represents a fundamental shift from semantic labeling to sensor-realistic measurement. This approach:

  • ✅ Maintains current performance levels

  • ✅ Improves generalization to novel environments

  • ✅ Enables direct transfer to real robots

  • ✅ Provides richer information for navigation decisions

  • ✅ Eliminates the need for hard-coded environment types

Graph View

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