Humans have the amazing ability to learn the dynamics of the body and environment to develop motor skills. Traditional motor studies using arm reaching paradigms have viewed this ability as the process of 'internal model adaptation'. However, the behaviors have not been fully explored in the case when reaches fail to attain the intended target. Here we examined human reaching under two force fields types; one that induces failures (i.e., target errors), and the other that does not. Our results show the presence of a distinct failure-driven adaptation process that enables quick task success after failures, and before completion of internal model adaptation, but that can result in persistent changes to the undisturbed trajectory. These behaviors can be explained by considering a hierarchical interaction between internal model adaptation and the failure-driven adaptation of reach direction. Our findings suggest that movement failure is negotiated using hierarchical motor adaptations by humans.

Shared control is an increasingly popular approach to facilitate control and communication between humans and intelligent machines. However, there is little consensus in guidelines for design and evaluation of shared control, or even in a definition of what constitutes shared control. This lack of consensus complicates cross fertilization of shared control research between different application domains. This paper provides a definition for shared control in context with previous definitions, and a set of general axioms for design and evaluation of shared control solutions. The utility of the definition and axioms are demonstrated by applying them to four application domains: automotive, robot-assisted surgery, brain-machine interfaces, and learning. Literature is discussed for each of these four domains in light of the proposed definition and axioms. Finally, to facilitate design choices for other applications, we propose a hierarchical framework for shared control that links the shared control literature with traded control, co-operative control, and other human-automation interaction methods. Future work should reveal the generalizability and utility of the proposed shared control framework in designing useful, safe, and comfortable interaction between humans and intelligent machines.

During operation of slow dynamic systems such as heavy machinery, users must account for inherent lag in the system dynamics, often via the less intuitive rate control mode. The slow response of these systems requires predictive control based on an understanding of the input-output relationship of system dynamics. In practical applications, such as learning to control an excavator, training can be a long and therefore costly process. This paper investigates the use of haptic shared control (HSC) to support learning of a system with slow dynamics. Previous work has failed to reach a consensus on the effectiveness of training with HSC, although a few recent studies have demonstrated improvements in tasks with time-critical components. Here, subjects learned to perform a pursuit task while controlling a linear system with slow dynamics using a 1-DOF haptic manipulator, either with or without HSC during training. To prevent reliance on the guidance forces, HSC was only present on intermittent trials and decreased in strength over time. Both groups quickly learned the task and showed similar performance after training, regardless of whether or not they trained with HSC.

Haptic virtual fixtures have been shown to improve user performance and increase the safety of robot-assisted tasks, particularly for surgical applications. However, little research has studied virtual fixtures that provide moving force constraints based on motion of the environment, e.g., organ movement due to heartbeat or respiration. This work discusses design considerations of moving forbidden-region virtual fixtures and presents two methods of implementation: predicted-position and current-position virtual fixtures. Human subject experiments were performed to determine the effectiveness of moving virtual fixtures when interacting with an object in motion using a teleoperator. Results show that moving virtual fixtures can help improve user precision and decrease the amount of force applied.