Autonomy in Rehabilitation Robotics: An Intersection

By Brenna D. Argall. 2018

62 citations recorded by the source corpus.

Within the field of human rehabilitation, robotic machines are used both to rehabilitate the body and to perform functional tasks. Robotics autonomy able to perceive the external world and reason about high-level control decisions, however, seldom is present in these machines. For functional tasks in particular, autonomy could help to decrease the operational burden on the human and perhaps even to increase access—and this potential only grows as human motor impairments become more severe. There are however serious, and often subtle, considerations to introducing clinically-feasible robotics autonomy to rehabilitation robots and machines. Today the fields of robotics autonomy and rehabilitation robotics are largely separate. The topic of this article is at the intersection of these fields: the introduction of clinically-feasible autonomy solutions to rehabilitation robots, and opportunities for autonomy within the rehabilitation domain.

Subfields: Biomedical Engineering, Rehabilitation, Robotics & Control

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Citations in the corpus

LILA: Language-Informed Latent Actions (2021, background): “Paradigms for efﬁcient human-robot collaboration that strike a balance between robot autonomy and human control such as shared autonomy [2, 3, 4, 5] present a promising path towards building such assistive systems.”
Learning Visually Guided Latent Actions for Assistive Teleoperation (2021, background): “, joysticks, sip-and-puff devices) (Argall, 2018).”
Learning latent actions to control assistive robots (2021, unknown): “We emphasize that these devices are high-dimensional in order to reach and manipulate food items in 3D space [2].”
Controlling Assistive Robots with Learned Latent Actions (2019, methodology): “Under shared autonomy, the robot combines user inputs with autonomous assistance: this method has been applied to settings where the human’s input is lowdimensional [4]–[6].”
Shared Autonomy with Learned Latent Actions (2020, background): “, through joysticks [12], sip-and-puffs [1], or brain-computer interfaces [22]) to seamlessly perform complex and dexterous eating tasks.”
Learning User-Preferred Mappings for Intuitive Robot Control (2020, background): “User-friendliness and comfort are crucial in many application domains including surgical and assistive robots [2], [3], mobile robots [4] and even more abstract smart home systems [5].”