COLLECT AND PREPARE DATASET FROM HUMAN-DEMONSTRATIONS OF SIMPLE MANIPULATION TASKS USING XARM7 ROBOTIC ARM FOR TRAINING BEHAVIOUR CLONING ALGORITHM - KRYVENCHUK, YURII; SHAVARSKYI, MAKSYM (Herald of Khmelnytskyi National University. Technical sciences, 2024)
The goal of the study is to understand what data structure is necessary to teach a robot to perform simple manipulation tasks. Training will take place thanks to Imitation learning, using the behavior cloning algorithm. Imitation learning is extremely useful for training any robotic system, especially with multi-jointed robots. This approach allows you to avoid writing complex and cumbersome heuristic algorithms. Heuristic algorithms often contain many hyperparameters that must be chosen empirically. Imitation learning algorithms allow you to develop an End-to-End policy from the collected data, which simplifies the system and increases its reliability. In the realm of training robotic systems through Imitation Learning, the criticality of data collection cannot be overstated. The efficacy of the Behavior Cloning algorithm hinges on the quality and speed at which data is gathered. Properly curated and swiftly acquired datasets serve as the bedrock for training algorithms that emulate human behavior, allowing the robot to seamlessly replicate intricate manipulations. The efficiency of data gathering is paramount in addressing the challenges posed by the dynamic nature of real-world environments. Swift adaptation to varying conditions and unforeseen scenarios necessitates a data collection process that is not only rapid but also comprehensive. A well-curated dataset not only expedites the training process but also enhances the model’s adaptability and robustness in real-world applications. In conclusion, the success of Imitation Learning algorithms, particularly those underpinned by the Behavior Cloning approach, is contingent on the meticulous and expeditious gathering of data. This underscores the importance of streamlined data collection processes, ensuring that the resultant models are not only accurate but also capable of navigating the complexities of diverse and dynamic environments. Fastidious attention to data gathering is imperative to ensure a diverse and representative dataset. The xArm7 robot, in collaboration with the Realsense D435 camera, serves as an ideal synergy for capturing a myriad of movements and scenarios. The precision of data collection directly influences the algorithm’s ability to generalize, making it adept at handling a spectrum of manipulation tasks.