An empirical study with simulated ADL tasks using a vision-guided assistive robot arm

Kim, Dae‐Jin; Lovelett, Ryan; Behal, Aman

doi:10.1109/icorr.2009.5209527

Cited by 26 publications

(14 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As with robotic wheelchairs, for wheelchair-mounted robotic arms there is a question of how to design paradigms for control sharing. Earlier work saw a similar high/low-level control partition, where the user selected the task or goal, which then was executed by the autonomy system (29,138). In some works the human additionally intervened to provide pose corrections (68,78,139) or assist the autonomy (83).…”

Section: Shared Controlmentioning

confidence: 99%

Autonomy in Rehabilitation Robotics: An Intersection

Argall

2018

Annu. Rev. Control Robot. Auton. Syst.

View full text Add to dashboard Cite

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

show abstract

Section: Shared Controlmentioning

confidence: 99%

Autonomy in Rehabilitation Robotics: An Intersection

Argall

2018

Annu. Rev. Control Robot. Auton. Syst.

View full text Add to dashboard Cite

show abstract

“…The value of speech as an input modality for persons with severe motor impairments to operate assistive machines has been leveraged for use with robotic arms (Busnel et al, 1999; Kim et al, 2009), robotic wheelchairs (Mandel and Frese, 2007) and mobile manipulators (Volosyak et al, 2005). The human typically uses speech to provide high-level goals or teleoperation commands for the autonomy.…”

Section: Related Workmentioning

confidence: 99%

Real-time natural language corrections for assistive robotic manipulators

Broad

Arkin

Ratliff³

et al. 2017

The International Journal of Robotics Research

View full text Add to dashboard Cite

We propose a generalizable natural language interface that allows users to provide corrective instructions to an assistive robotic manipulator in real-time. This work is motivated by the desire to improve collaboration between humans and robots in a home environment. Allowing human operators to modify properties of how their robotic counterpart achieves a goal on-the-fly increases the utility of the system by incorporating the strengths of the human partner (e.g. visual acuity and environmental knowledge). This work is applicable to users with and without disability. Our natural language interface is based on the distributed correspondence graph, a probabilistic graphical model that assigns semantic meaning to user utterances in the context of the robot’s environment and current behavior. We then use the desired corrections to alter the behavior of the robotic manipulator by treating the modifications as constraints on the motion generation (planning) paradigm. In this paper, we highlight four dimensions along which a user may wish to correct the behavior of his or her assistive manipulator. We develop our language model using data collected from Amazon Mechanical Turk to capture a comprehensive sample of terminology that people use to describe desired corrections. We then develop an end-to-end system using open-source speech-to-text software and a Kinova Robotics MICO robotic arm. To demonstrate the efficacy of our approach, we run a pilot study with users unfamiliar with robotic systems and analyze points of failure and future directions.

show abstract

“…These control modalities also have benefits for controlling robotic arms for WMRM systems, though the positioning of the robotic gripper in three-dimensional Cartesian space and prehensile manipulating of objects provide unique challenges. However, existing HCI modalities (Kim et al, 2012) as well as emerging brain computer interfaces (BCI) (Palankar et al, 2009) and state-of-the-art computer vision systems have been shown to be capable controllers for WMRM systems (Kim et al, 2009;Tsui et al, 2007). This latter work has shown that a camera mounted in the hand of the robotic manipulator provides an effective visual interface for WMRM control (Chung and Cooper, 2012;Kim et al, 2012;Tsui et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Integrated vision-based system for efficient, semi-automated control of a robotic manipulator

Jiang

Wachs

Duerstock

2014

International Journal of Intelligent Computing and Cybernetics

View full text Add to dashboard Cite

Purpose-Develop an integrated, computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In addition, a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries (SCIs) including object tracking and face recognition to function as an efficient, hands-free WMRM controller. Design/Methodology/Approach-Two Kinect cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures and locate the operator's face for object positioning, and then send those as commands to control the WMRM. The other sensor was used to automatically recognize different daily living objects selected by the subjects. An object recognition module employing the Speeded Up Robust Features (SURF) algorithm was implemented and recognition results were sent as a commands for "coarse positioning" of the robotic arm near the selected object. Automatic face detection was provided as a shortcut enabling the positing the objects close by the subject's face. Findings-The gesture recognition interface incorporated hand detection, tracking and recognition algorithms, and yielded a recognition accuracy of 97.5% for an eight-gesture lexicon. Tasks' completion time were conducted to compare manual (gestures only) and semi-manual (gestures, automatic face detection and object recognition) WMRM control modes. The use of automatic face and object detection significantly reduced the completion times for retrieving a variety of daily living objects. Originality/value-Integration of three computer vision modules were used to construct an effective and handfree interface for individuals with upper-limb mobility impairments to control a WMRM.

show abstract

An empirical study with simulated ADL tasks using a vision-guided assistive robot arm

Cited by 26 publications

References 13 publications

Autonomy in Rehabilitation Robotics: An Intersection

Autonomy in Rehabilitation Robotics: An Intersection

Real-time natural language corrections for assistive robotic manipulators

Integrated vision-based system for efficient, semi-automated control of a robotic manipulator

Contact Info

Product

Resources

About