Assistive robotic manipulation through shared autonomy and a Body-Machine Interface

Abstract

Assistive robotic manipulators have the potential to improve the lives of people with motor impairments. They can enable individuals to perform activities such as pick-and-place tasks, opening doors, pushing buttons, and can even provide assistance in personal hygiene and feeding. However, robotic arms often have more degrees of freedom (DoF) than the dimensionality of their control interface, making them challenging to use—especially for those with impaired motor abilities. Our research focuses on enabling the control of high-DoF manipulators to motor-impaired individuals for performing daily tasks. We make use of an individual’s residual motion capabilities, captured through a Body-Machine Interface (BMI), to generate control signals for the robotic arm. These low-dimensional controls are then utilized in a shared-control framework that shares control between the human user and robot autonomy. We evaluated the system by conducting a user study in which 6 participants performed 144 trials of a manipulation task using the BMI interface and the proposed shared-control framework. The 100% success rate on task performance demonstrates the effectiveness of the proposed system for individuals with motor impairments to control assistive robotic manipulators.

Controlling a robotic arm for functional tasks using a wireless head-joystick: A case study of a child with congenital absence of upper and lower limbs

“…This may be especially challenging for children, who show deficits relative to adults in learning such interfaces [20], [22]. One way to improve this is to use either an adaptive interface that adjusts to the user [23], [24], or use a shared control framework so that the autonomy of control can be shared between the human and the machine [25]. Second, for the sake of simplicity, we relied only on head movements (i.e.…”

Probabilistic Human Intent Recognition for Shared Autonomy in Assistive Robotics

“…In shared autonomy, many systems simplify the intent inference problem by focusing on predefined tasks and behaviors or by assuming that the robot has access to the user's intent a priori [19,23]. One approach to infer the user's intent could be to have the user communicate the intended goal explicitly, such as via verbal commands as observations.…”

“…Human-robot teams are common in many application areas, such as domestic service, search and rescue, surgery, and driving vehicles. In the area of assistive robotics, shared autonomy is utilized to aid the human in the operation of an assistive robot through the augmentation of human control signals with robotics autonomy [13,19,23,30]. One fundamental requirement for effective humanrobot collaboration in shared autonomy is human intent recognition.…”

A Human–Robot Interaction Perspective on Assistive and Rehabilitation Robotics

“…One way to achieve this is to employ learning methods that incrementally adapt to the subject, situation, and environment (Castellini et al, 2015). Another possibility is to introduce shared autonomy to traditionally human-operated assistive machines (Argall, 2014, 2015; Jain et al, 2015) since semiautonomous operation can off-load cognitive and physical load from subjects to the machine and thereby increase user independence. Furthermore, the literature suggests the use of force and tactile information (Dahiya et al, 2010) for contact modeling and recognition as well as for motion analysis and planning, since these robotic devices will be in contact with the user (Cannata et al, 2010; Menguc et al, 2014).…”