Augmenting the body with artificial limbs controlled concurrently to one’s natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this possible. By allowing individuals to achieve otherwise impossible actions, movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with the environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning as well as application scenarios, and propose a vision for the field.

This paper presents a new generalized ray-based approach to the generation and representation of the wrench-closure workspace (WCW) for cable-driven robots (CDRs). Existing WCW studies have yet to address two significant problems, first is the lack of a generalized approach to generate the WCW with continuity information for all degrees-of-freedom (DoFs) and arbitrary CDRs, and second is a workspace representation for higher DoF robots. The proposed work addresses these issues using a generalized ray-based lattice WCW generation approach that can be applied in all DoFs. Furthermore, a new graph workspace representation is introduced with a range of advantages in the way CDR workspace can be visualized and studied. Such a representation is powerful since it: can be used for any other type of workspace beyond the WCW; can be visualized in two-dimensions regardless of the number of DoFs of the system; allows metric information to be included; and opens up the use of well-established graph theory techniques to study the workspace. Through three different CDR examples, a 3-DoF planar cable-driven parallel robot (CDPR), a 4-DoF multilink robot, and a 6-DoF CDPR, the characteristics and advantages of the proposed method are highlighted.

Objectives. The last decades have seen a surge of robots for physical training/work assistance. How to best control these interfaces is unknown, although arguably the interaction should be similar to human movement assistance. Here we compare the behaviour and assessment of subjects tracking a moving target with assistance from (i) trajectory guidance (as typically used in robots for physical training), (ii) a human partner, and (iii) the reactive robot partner of [1]. Results. Trajectory guidance was recognised as robotic, while the robot partner was felt as human-like. However, trajectory guidance was preferred to assistance from a human partner, which was recognised as less predictable. The robot partner also was felt more predictable and helpful than a human partner, and was preferred. Conclusions. While subjects like to rely on predictable interaction, such as in trajectory guidance, the control reactivity of the robot partner is essential for perceiving an interaction as human-like.

Haptic communication, the exchange of force and tactile information during dancing or moving a table together, has been shown to benefit the performance of human partners. Similarly, it could also be used to improve the performance of robots working in contact with a human operator. As we move to more robot integrated workspaces, how common network features such as delay or jitter impact haptic communication need to be better understood. Here using a human-like interactive robotic controller, that has been found to be indistinguishable by humans to human interaction, we evaluate how subjects' performance and perception is altered by varying levels of transmission delay. We find that subjects are able to recognise haptic delay at very small levels within haptic interaction. However, while they are consciously aware of the delay they can only compensate for it up until a certain point, after which they perceive it as the addition of noise/impedance into the system.

Teleoperation is increasingly recognized as a viable solution for deploying robots in hazardous environments. Controlling a robot to perform a complex or demanding task may overload operators resulting in poor performance. To design a robot controller to assist the human in executing such challenging tasks, a comprehensive understanding of the interplay between the robot's autonomous behavior and the operator's internal state is essential. In this letter, we investigate the relationships between robot autonomy and both the human user's cognitive load and trust levels, and the potential existence of three-way interactions in the robot-assisted execution of the task. Our user study (N = 24) results indicate that while the autonomy level influences the teleoperator's perceived cognitive load and trust, there is no clear interaction between these factors. Instead, these elements appear to operate independently, thus highlighting the need to consider both cognitive load and trust as distinct but interrelated factors in varying the robot autonomy level in shared-control settings. This insight is crucial for the development of more effective and adaptable assistive robotic systems.

This paper introduces a sensory augmentation technique enabling a contact robot to understand its human user's control in real-time and integrate their reference trajectory information into its own sensory feedback to improve its tracking performance. The human's control is formulated as a feedback controller with unknown control gains and desired trajectory. An unscented Kalman filter is used to estimate first the control gains and then the desired trajectory. The estimated human's desired trajectory is used as augmented sensory information about the system and combined with the robot's measurement to estimate a reference trajectory. Simulations and an implementation on a robotic interface demonstrate that the reactive control can robustly identify the human user's control, and that the sensory augmentation improves the robot's tracking performance.