We present a survey of recent work on robot manipulation and sensing of deformable objects, a field with relevant applications in diverse industries such as medicine (e.g. surgical assistance), food handling, manufacturing, and domestic chores (e.g. folding clothes). We classify the reviewed approaches into four categories based on the type of object they manipulate. Furthermore, within this object classification, we divide the approaches based on the particular task they perform on the deformable object. Finally, we conclude this survey with a discussion of the current state-of-the-art approaches and propose future directions within the proposed classification.

This study is focused on developing a new dexterous soft robotic gripper with three fingers and an active palm capable of performing in-hand manipulation purposes. This innovative design meets all the dexterous manipulation requirements without any increase in mechanical complexity. In each finger, the bending position can be modified and controlled by moving a stiff rod inserted inside the center hole of the finger. In this way, the effective length of the manipulation can be changed. As a result, these reconfigurable fingers provide a more accessible workspace than conventional soft grippers. Besides, a large diversity of the fingers shape configurations results in more dexterity and in-hand manipulation capability. Workspace analysis is accomplished to characterize the advantages of the proposed design. The effectiveness of this soft robotic gripper is validated by different in-hand manipulation experimental tests, including rotation, regrasping, and rolling. The results suggest a promising solution to bridge the design gap between hard and soft robots for dexterous manipulation tasks. The hybrid design carries advantages of these two classes, such as reconfigurability, position, and shape control from hard robots, with large degrees of freedom (DOFs), complex deformations, and lightweight from soft robots. Like human manipulation, the palm plays a major role in stable grasping, especially for enhancing the in-hand manipulation capability. Therefore, we also investigate two types of vacuum palms (suction cup and granular particles) to guarantee a wide range of object manipulation tasks that cannot be completely performed by previously suggested soft grippers.

The precise localization of human operators in robotic workplaces is an important requirement to be satisfied in order to develop human-robot interaction tasks. Human tracking provides not only safety for human operators, but also context information for intelligent human-robot collaboration. This paper evaluates an inertial motion capture system which registers full-body movements of an user in a robotic manipulator workplace. However, the presence of errors in the global translational measurements returned by this system has led to the need of using another localization system, based on Ultra-WideBand (UWB) technology. A Kalman filter fusion algorithm which combines the measurements of these systems is developed. This algorithm unifies the advantages of both technologies: high data rates from the motion capture system and global translational precision from the UWB localization system. The developed hybrid system not only tracks the movements of all limbs of the user as previous motion capture systems, but is also able to position precisely the user in the environment.