Task-based hybrid shared control for training through forceful interaction

Abstract

Despite the fact that robotic platforms can provide both consistent practice and objective assessments of users over the course of their training, there are relatively few instances where physical human–robot interaction has been significantly more effective than unassisted practice or human-mediated training. This article describes a hybrid shared control robot, which enhances task learning through kinesthetic feedback. The assistance assesses user actions using a task-specific evaluation criterion and selectively accepts or rejects them at each time instant. Through two human subject studies (total [Formula: see text]), we show that this hybrid approach of switching between full transparency and full rejection of user inputs leads to increased skill acquisition and short-term retention compared with unassisted practice. Moreover, we show that the shared control paradigm exhibits features previously shown to promote successful training. It avoids user passivity by only rejecting user actions and allowing failure at the task. It improves performance during assistance, providing meaningful task-specific feedback. It is sensitive to initial skill of the user and behaves as an “assist-as-needed” control scheme, adapting its engagement in real time based on the performance and needs of the user. Unlike other successful algorithms, it does not require explicit modulation of the level of impedance or error amplification during training and it is permissive to a range of strategies because of its evaluation criterion. We demonstrate that the proposed hybrid shared control paradigm with a task-based minimal intervention criterion significantly enhances task-specific training.

Collaborative robots can augment human cognition in regret-sensitive tasks

“…The only control paradigm that resulted in a significantly different game score compared to no robots is shared coverage control ( , ), consistent with prior work showing the performance benefits of shared control paradigms ( 20 , 59–63 ). Likewise, we find that level of autonomy has a statistically significant affect on game score due to superior performance using shared coverage control, as explained in Table S4 .…”

“…By discovering that collaborative robots affect human cognition, this work introduces an unexplored research area at the intersection of robotics, computational neuroscience, and cognitive load theory that could impact the experience of all future users of autonomous systems. Although it is known that robots can influence human capability through facilitating the motor learning of prespecified tasks ( 20 ), augmenting human cognition allows the robot to improve human performance without knowing the task the human is attempting to accomplish. In many settings, we expect humans to possess knowledge about the task and how the task should be completed that is unavailable to the robot.…”

Embodied Communication: How Robots and People Communicate Through Physical Interaction

“…Interestingly, human-human physical coupling can reliably lead to improved performance even if one of the partners is less adept at the task (120). Studies on human-robot coupling show that forceful interaction can lead to improved task performance even after the robot coupling is removed and the person performs the task independently (121). These pieces of evidence illustrate the potential of communication through physically coupled motion.…”

Effectiveness of Intelligent Control Strategies in Robot-Assisted Rehabilitation—A Systematic Review

“…Adaptive Iterative Learning Control is presented in [33], while [64] presents adaptive state-space control, and [71] develops a wholly novel adaptive controller based on motor primitives. The hybrid shared controller developed in [89] uses both a Mode Insertion Gradient algorithm and Optimal Controller Inner Product to adaptively adjust impedance parameters in response to user input. Another novel adaptive controller is presented in [90], with a Minimal Assist As Needed (mAAN) control scheme.…”