The recent developments in the field of robotics and automation, particularly in the areas of continuum robots, manipulator control, and soft tissue manipulation, showcase a significant shift towards integrating advanced computational models and innovative control strategies to enhance precision, adaptability, and safety. A notable trend is the application of neural networks and machine learning techniques to improve the accuracy and reliability of robotic systems in complex tasks such as shape estimation, trajectory tracking, and force feedback. Additionally, there is a growing emphasis on developing low-contact or contactless manipulation technologies for delicate operations, especially in medical applications, to minimize tissue damage and improve surgical outcomes.

• A Synergistic Framework for Learning Shape Estimation and Shape-Aware Whole-Body Control Policy for Continuum Robots: This paper introduces a novel approach combining shape estimation and control policies for continuum robots, significantly advancing the field's capabilities in handling complex dynamics.
• Development of an Adaptive Sliding Mode Controller using Neural Networks for Trajectory Tracking of a Cylindrical Manipulator: Presents an innovative controller that enhances trajectory tracking accuracy for cylindrical manipulators, promising for industrial applications like 3D printing.
• Dexterous Manipulation of Deformable Objects via Pneumatic Gripping: Lifting by One End: Offers a new method for lifting deformable objects with minimal force, improving accessibility and efficiency in robotic manipulation.
• Virtual-Work Based Shape-Force Sensing for Continuum Instruments with Tension-Feedback Actuation: Introduces a compact actuation unit with real-time tension feedback, improving shape and force sensing in surgical instruments.
• Low-Contact Grasping of Soft Tissue with Complex Geometry using a Vortex Gripper: Explores the use of vortex technology for soft tissue manipulation, presenting a potential alternative to traditional surgical graspers.
• Image-to-Force Estimation for Soft Tissue Interaction in Robotic-Assisted Surgery Using Structured Light: Develops a vision-based scheme for accurate force estimation in soft tissue interaction, enhancing safety in robotic-assisted surgeries.