All classes Reinforcement Learning for Robot Manipulator Syllabus · Episode 6 of 6

Class Reinforcement Learning Robot Manipulator #6: Adaptive Testing against Load Changes & Disturbances, Performance Comparison

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Reinforcement learning robot manipulator control testing tutorial: PID, computed torque, and PD + SAC on load changes and disturbances. Episode 6, closing the series on reinforcement learning robot manipulator control.

Software: Python (NumPy, Gymnasium, Stable-Baselines3)
Level: Advanced
Duration: ± 7 min
Series total: 49 min · 6 videos
Published: October 25, 2026

Episode 6 of 6 · view syllabus

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Class Reinforcement Learning Robot Manipulator #6: Adaptive Testing against Load Changes & Disturbances, Performance Comparison: Cover: Class Reinforcement Learning Robot Manipulator #6: Adaptive Testing against Load Changes & Disturbances, Performance Comparison

Class Reinforcement Learning Robot Manipulator #6: Adaptive Testing against Load Changes & Disturbances, Performance Comparison: Before you start, steps list, and Step 1

Class Reinforcement Learning Robot Manipulator #6: Adaptive Testing against Load Changes & Disturbances, Performance Comparison: Step 2: Four controllers, four conditions

Class Reinforcement Learning Robot Manipulator #6: Adaptive Testing against Load Changes & Disturbances, Performance Comparison: Step 3: Accuracy vs robustness

Class Reinforcement Learning Robot Manipulator #6: Adaptive Testing against Load Changes & Disturbances, Performance Comparison: Done: Final Result and Further Practice

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Introduction

Here is the final result, four controllers are tested on unknown loads and disturbances. The perfect competitor in nominal conditions is actually the most affected when carrying a load, while our reinforcement learning agent remains accurate. Let's start. Hello, welcome to the final episode of the reinforcement learning robot manipulator control series. In the real world, robots lift objects of different weights and face unexpected disturbances. A controller that is only good in ideal conditions is not enough. Today, we will fairly test the four controllers under changing conditions.

Step 1: Agent as Controller

Create a test file named underscore adaptive.py The SAC controller class wraps the agent into a regular controller, calculating PD torque, and if RL is used, it adds correction from the agent with exactly the same observations as during training. In this way, the four controllers are tested through the same simulation function from episode 3.

Step 2: Four Controllers, Four Conditions

We load the SAC model from episode 5 and prepare 4 methods and 4 conditions. Nominal, 1.5 kg load, torque disturbance of 6 Nm starting from the fourth second, as well as load and disturbance occurring simultaneously. Run. In nominal conditions, the champion competitor has an error of 0.09 mm, and PD plus SAC is about 6 mm, much better than PID at 17 mm and PD alone at about 39 mm. But with an unknown 1.5 kg load, the competitor's error jumps to about 27 mm, worse than PID. PD plus SAC remains around 10 mm, because the agent has already learned to handle random loads during training.

Step 3: Accuracy vs Robustness

Let's look at the graph. In the bar chart, the competitor's bar is almost invisible in nominal conditions but suddenly spikes when there is a load, whereas the PD plus SAC bar remains low and stable in all four conditions. The right graph shows the error when load and disturbance occur simultaneously, PD plus SAC has the smallest error throughout the time, including after the disturbance arrives at the fourth second. In conclusion, the correct model provides the highest accuracy, but reinforcement learning trained with randomization provides robustness when the model is incorrect.

Done!

That's it, and that's the end of this series. We have built a complete research on robot control, kinematics, Lagrange dynamics, PID and competitors, a gym environment with residual RL, soft actor-critic training, up to adaptive testing against load and disturbance. For further research, try combining the competitor with residual RL, three-joint manipulator, friction and nice sensors, or simulate it on a real robot. Thank you for following this series. Currently working on a thesis, dissertation, or research in the field of robotics and reinforcement learning? Cerdas Inovasi Digital helps with robot control implementation, From kinematics and dynamics of manipulators, PID and competitors, gym environment, up to soft actor-critic agent training, as well as opening Python programming courses for electrical engineering and mechatronics. Want to master the entire series, from robotic arm kinematics up to adaptive testing of reinforcement learning agents? All six episodes, complete with step-by-step PDF guides and source code, are available for premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or one-time payment of Rp999.000 for lifetime access. Want just one class? Buy single classes with lifetime access. Subscribe now on the premium page cerdasinovasidigital.com Contact our WhatsApp, or visit cerdasinovasidigital.com Thank you for watching, see you in the next episode.

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Transcript

0:03Introduction

0:43Step 1: Agent as Controller

2:12Step 2: Four Controllers, Four Conditions

3:52Step 3: Accuracy vs Robustness

5:30Done!

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Transcript

0:03Introduction

0:43Step 1: Agent as Controller

2:12Step 2: Four Controllers, Four Conditions

3:52Step 3: Accuracy vs Robustness

5:30Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Agent as Controller

Step 2: Four Controllers, Four Conditions

Step 3: Accuracy vs Robustness

Done!

Introduction

Step 1: Agent as Controller

Step 2: Four Controllers, Four Conditions

Step 3: Accuracy vs Robustness

Done!