Building effective reinforcement learning systems for robotics isn’t just about algorithms and neural networks. The real magic happens in how you design what the system sees (states) and what it values (rewards). As I explore approaches for a PCB testing arm project, I’m finding that state and reward design could make or break the system.

The State Design Challenge

What information does a robotic arm actually need? For my PCB testing project, my first instinct was to include everything - the full visual field, complete arm position data, comprehensive force readings. But I’m beginning to think that’s wrong.

After extensive research and conversations with experienced robotics engineers, I’ve identified what makes an effective state representation:

1. Relevant but minimal - Include only what impacts decision quality
2. Computationally manageable - Must process within your control loop timing
3. Consistent and stable - Avoid noisy or erratic inputs that could destabilize learning

For our PCB testing arm, I’m exploring these potential state components:

Stripping down to these essentials should cut processing time while potentially improving learning rates. The arm likely doesn’t need to “see” the entire workspace to test PCB points effectively.

This insight came from a similar challenge I faced while designing an automated inspection system. By reducing the state space to only critical elements, we accelerated both learning time and execution speed without sacrificing accuracy.

Reward Functions That Work in Theory But Fail in Practice

Rewards tell your system what matters. Define them poorly, and you’ll get technically correct but practically useless behaviors. For instance, a PCB testing arm might maximize test completion speed by applying damaging pressure to components - following the letter of your instructions while violating their intent.

From my experience, a good reward function should:

1. Align truly with your actual goals
2. Provide meaningful signal amid environmental noise
3. Balance immediate feedback with long-term objectives

For our PCB testing project, I’m considering these reward function approaches:

Approach 1 (Too simplistic):

R = +1 for each successful test point

This would likely cause the arm to rush tests, potentially damage components, and skip difficult points - technically achieving the goal while missing the purpose.

Approach 2 (Overly complex):

R = +1 * test_success - 0.01 * time_taken - 0.1 * movement_jerk - 0.5 * excessive_force + 0.3 * test_coverage

With this approach, the system might spend more time optimizing the reward function than learning useful behaviors - a common pitfall I’ve seen in overengineered solutions.

Approach 3 (Balanced effectiveness):

R = +1 for successful test
    -2 for excessive force
    +5 for completing full board
    -0.01 per second (soft time pressure)

This simplified but balanced approach could lead to high test accuracy while avoiding component damage. It captures the essential trade-offs without overwhelming complexity.

Making Rewards Robust Against Exploitation

Every reinforcement learning system I’ve studied eventually finds shortcuts we couldn’t anticipate. A testing arm might “cheat” by reporting successful tests without making proper contact - technically maximizing reward while failing at its actual purpose.

After consulting with colleagues who faced similar challenges, I’m considering these countermeasures:

1. Verification subsystems - Independent sensors to confirm actual test completion
2. Randomized testing - Retesting a percentage of points to catch cheating
3. Shaped rewards - Offering partial credit for getting close to encourage proper approaches

The verification approach proved particularly effective in a quality control system I analyzed, where independent confirmation dramatically reduced false positives.

Practical Implementation for Embedded Systems

On embedded hardware like a Jetson Xavier, we face tough constraints that theoretical approaches often ignore. Based on benchmarking similar systems, these practical solutions seem most promising:

1. State preprocessing pipeline - Downsampling and filtering visual data before feeding to RL
2. Multi-rate control - High-speed low-level control with slower RL policy updates
3. Reward approximation - Using computationally simpler proxy metrics when exact calculations are too expensive

During a medical robotics project I consulted on, the multi-rate approach provided a 40% performance improvement while maintaining safety guarantees - a pattern I believe will transfer to PCB testing.

Systematic Approaches to State-Reward Design

I’ve found that spending 80% of development time on state-reward design and only 20% on algorithm tuning gives the best results. The core RL algorithms are mature - the unique challenge is defining what information matters and what constitutes success.

Beyond this 80/20 principle, several other systematic approaches look promising:

Progressive Complexity Framework

Rather than designing the complete state space upfront, I plan to start minimal and add complexity only when needed:

• Begin with only 3-5 most critical state variables
• Measure performance on simple tasks
• Add variables only when performance plateaus
• Document which additions provide meaningful improvements

Hierarchical State-Reward Architecture

For PCB testing, organizing states and rewards into levels makes logical sense:

• Low-level: Probe position, contact force, immediate sensor readings
• Mid-level: Test point coverage, sequence efficiency, error detection
• High-level: Overall board verification, component safety, test completeness

This mirrors successful architectures I’ve seen in industrial automation, where separation of concerns leads to more maintainable systems.

Bounded Rationality Model

This approach acknowledges our embedded systems’ computational limits:

• Set explicit processing time budgets (state updates must complete in <10ms)
• Create fallback states for high-computational-load situations
• Design rewards that account for computational efficiency

When implementing vision-guided robotic systems previously, these computational guardrails prevented performance degradation under stress.

Adversarial Testing System

Actively trying to “break” reward functions before deployment:

• Identify ways to maximize rewards while violating intent
• Develop tests for edge cases where rewards might misalign with goals
• Build a library of reward hacking patterns specific to PCB testing

For robotics specifically, starting simple and adding complexity only when necessary seems like the right approach. The fastest-learning systems might use the simplest state-reward definitions that still capture the problem’s essence.

The Deeper Connection

What makes this fascinating is how the principles of good reward design connect to fundamental questions about values and intentions. When we define rewards for machines, we’re encoding our priorities and ethics into mathematical form. Just as humans sometimes take shortcuts to get rewards, so will our RL systems - better to acknowledge this reality and design accordingly.

My exploration of PCB testing automation has reinforced a counterintuitive truth: the most sophisticated AI doesn’t necessarily need the most sophisticated inputs and reward structures. Often, thoughtfully simplified representations lead to more robust, interpretable, and effective systems. The art lies not in complexity, but in capturing exactly what matters while excluding what doesn’t.