A review on interaction control for contact robots through intent detection

Abstract

Interaction control can take opportunities offered by contact robots physically interacting with their human user, such as assistance targeted to each human user, communication of goals to enable effective teamwork, and task-directed motion resistance in physical training and rehabilitation contexts. Here we review the burgeoning field of interaction control in the control theory and machine learning communities, by analysing the exchange of haptic information between the robot and its human user, and how they share the task effort. We first review the estimation and learning methods to predict the human user intent with the large uncertainty, variability and noise and limited observation of human motion. Based on this motion intent core, typical interaction control strategies are described using a homotopy of shared control parameters. Recent methods of haptic communication and game theory are then presented to consider the co-adaptation of human and robot control and yield versatile interactive control as observed between humans. Finally, the limitations of the presented state of the art are discussed and directions for future research are outlined.

The Effects of Different Motor Teaching Strategies on Learning a Complex Motor Task

“…Human–robot interaction during sensorimotor teaching is complicated due to the complex interaction between the human, the robot, and the environment. As a consequence, knowledge acquisition is not merely determined by prior information and skills [ 28 , 37 ]. There was only haptic assistance without any other visual or audio feedback during the intermittent assistance phase.…”

“…Similarly, the efficacy of spatially separated cutaneous haptic guidance in motor skill training highlights the potential of multi-modal feedback in enhancing training outcomes in humans [ 35 ] as well as robot sensing technologies [ 36 ]. Liu et al [ 37 ] have extended this exploration to the realm of virtual reality, examining how haptic devices facilitate the learning of dexterous object manipulation in a virtual setup. Moreover, the work by He et al [ 38 ] on medical simulators for tissue examination training underscores the importance of multi-modal sensory feedback.…”

A Trade-Off between Complexity and Interaction Quality for Upper Limb Exoskeleton Interfaces

“…In future works, the inclusion of passive degrees of freedom at the arm interface will be performed on the basis of the present study to further minimize unwanted interaction efforts while implementing a relatively simple design. The performances of this improved design will also need to be assessed with more complex controllers, for example, adaptive ones [69].…”

Human-exoskeleton interaction force estimation in Indego exoskeleton

“…The efficacy of these robotic systems depends on their control strategy, particularly, for partially impaired users as the exoskeleton has to encourage exploitation of the user's residual motor capacity while providing the minimum assistance required for locomotion [4][5][6]. This requires the exoskeleton to softly switch between the follower and leader roles depending on the user's performance [7,8]. Otherwise, considerable physical disagreement emerges in the human-exoskeleton interaction causing the user to yield motion to the exoskeleton to maintain comfort at the cost of hindering their motor recovery [5].…”