The field of robotics is witnessing significant developments in autonomous and teleoperated systems, with a focus on enhancing precision, safety, and efficiency. Researchers are exploring innovative approaches to improve the performance of robotic arms, teleoperation interfaces, and control algorithms. Notably, the introduction of machine learning and artificial intelligence techniques is enabling robots to learn from human operators and adapt to complex environments. Furthermore, the development of flexible and configurable user interfaces is facilitating the deployment of robotic systems in various applications, including surgery, food production, and rescue operations.

Some noteworthy papers in this area include: The paper on Fuzzy-RRT for Obstacle Avoidance in a 2-DOF Semi-Autonomous Surgical Robotic Arm, which presents a novel adaptation of the Fuzzy Rapidly-exploring Random Tree algorithm for obstacle avoidance and collaborative control. The paper on Confidence-based Intent Prediction for Teleoperation in Bimanual Robotic Suturing, which proposes an interactive control strategy that assists the human operator by predicting their motion plan at both high and low levels.