Robot Trajectory

Robot trajectory optimization focuses on generating efficient and safe robot movements, addressing challenges like collision avoidance, task completion, and human-robot interaction. Current research emphasizes learning-based approaches, employing models such as Dynamic Movement Primitives, Gaussian Processes, and neural networks (including transformers and VAEs) to generate and adapt trajectories based on diverse data sources (e.g., human demonstrations, sensor data). These advancements are crucial for improving robot performance in various applications, from manufacturing and warehouse automation to assistive robotics and autonomous driving, by enabling more robust, adaptable, and efficient robot control.