Robots are getting smarter, but how? The old ways of explicit programming are giving way to machines that learn. I’ve been exploring the frontiers beyond traditional robotics - where supervised learning and reinforcement learning open up entirely new possibilities.

What happens when robots start to teach themselves? Using a PCB testing arm as my experimental ground, I’m finding fascinating contrasts between programming exact movements and letting systems discover solutions on their own.

Traditional Robotics Approach

The classical approach to robotics relies on explicit programming and mathematical models. It’s served us well for decades but shows clear limitations when environments become less structured

How it works for PCB testing:

1. Manually define coordinates for test points or use CAD data
2. Program inverse kinematics for arm movements
3. Implement PID controllers for precise positioning
4. Create explicit error handling routines

Key characteristics:

• Deterministic behavior
• Requires extensive manual programming
• No learning from experience
• Limited adaptability to variations

This approach would require significant time programming movement paths, tuning PID controllers, and handling edge cases. The system would work precisely as programmed - both its strength and limitation.

Supervised ML Approach

Supervised learning introduces an important shift: instead of programming explicit rules, we show the system examples of correct behavior.

How it works for PCB testing:

1. Collect images of PCBs with labeled test points
2. Train object detection models to identify solder points
3. Use detected points to guide traditional motion planning
4. Fixed control algorithms execute the movements

Key characteristics:

• Learns patterns from labeled data
• Adapts to visual variations
• Still requires programmed motion control
• Depends on quality and quantity of training data

This hybrid approach excels at perception tasks. The system learns to recognize visual patterns from labeled data and adapts to visual variations without explicit programming. However, it still relies on programmed motion control and depends heavily on the quality and quantity of training data. It would require a substantial dataset of labeled images and significant training time. It would likely handle visual variations well but still need explicit programming for the arm movements.

Reinforcement Learning Approach

Reinforcement learning represents the most radical departure from traditional robotics. Instead of explicit instructions or labeled examples, we provide a reward system and let the machine learn through trial and error.

How it works for PCB testing:

1. Define state representation (camera images, arm position)
2. Define action space (arm movements, probe actions)
3. Create reward function (successful tests, avoiding damage)
4. Let the system learn through trial and error

Key characteristics:

• Creates its own datasets through experience
• Learns both perception and control together
• Can discover novel movement strategies
• Requires careful reward design

What makes this approach revolutionary is that the system creates its own datasets through experience, learns both perception and control together, and can discover novel movement strategies that human programmers might never consider. This approach would take longer to develop initially but require less explicit programming. After many training iterations (mostly in simulation), it might outperform the other approaches in handling previously unseen PCB layouts.

Practical Comparison for PCB Testing

Here’s how the approaches might compare:

The traditional approach would provide high precision but lack adaptability. Supervised ML would improve perception but still rely on explicit motion programming. RL would integrate perception and control learning but require more development time and computing resources.

When To Use Each Approach

Based on my research, here’s when each approach might make the most sense:

Traditional robotics is best when:

• The environment is highly structured and predictable
• Path planning can be explicitly defined
• Computing resources are limited
• Deterministic behavior is required

Supervised ML is best when:

• Visual perception is the main challenge
• Large datasets of labeled examples are available
• You want to enhance existing control systems
• Partial adaptability is sufficient

Reinforcement learning is best when:

• Both perception and control need to adapt
• The task involves complex decision sequences
• Novel solutions might outperform human programming
• Computing resources are available for training

The Power of Hybrid Implementation

For a production PCB testing system, a hybrid approach might be most effective:

1. Traditional inverse kinematics for basic arm movement (reliable foundation)
2. Supervised ML for initial solder point detection (excellent at pattern recognition)
3. Reinforcement learning for fine positioning and probe contact (adaptive to variations)

This combination promises higher test accuracy with significantly less programming time than a pure traditional approach, while avoiding the complexity and training requirements of a full RL implementation.

The Integration Challenge

The most difficult aspect of this hybrid approach isn’t implementing any single technique - it’s integrating them effectively. Creating clear interfaces between the traditional motion control, the supervised learning perception system, and the reinforcement learning fine-control requires careful architectural design.

In a previous automation project, we found that defining clear state transitions between different control regimes was critical for system stability. The same principle applies here - explicitly defining when control passes from one subsystem to another prevents conflicting commands and ensures smooth operation.

Beyond the Technical Comparison

The core insight I’ve gained through this exploration isn’t just which approach works best technically. It’s understanding that these aren’t competing approaches but complementary tools in our automation toolkit.

Reinforcement learning isn’t magic - it’s particularly good at discovering efficient strategies through trial and error in complex environments. Traditional robotics provides safety guarantees and deterministic behavior in structured environments. Supervised ML excels at perception tasks with clear right answers.

Understanding these fundamental differences allows us to apply the right technique to each aspect of the problem rather than forcing a one-size-fits-all solution. The future of robotics isn’t about choosing between programming and learning - it’s about knowing exactly where each approach delivers maximum value.