Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously Unseen Food Items

Gordon, Ethan K.; Meng, Xiang; Bhattacharjee, Tapomayukh; Barnes, Matt; Srinivasa, Siddhartha S.

doi:10.1109/iros45743.2020.9341359

Cited by 21 publications

(13 citation statements)

References 30 publications

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“…Currently deployed arms are, as mentioned before, joystick operated, while most research is on autonomous movement, e.g. autonomously delivering a piece of food once the system has detected an open mouth [48], [49]. Sheridan's conventional scale from tele-operation to autonomy [50], has been redefined by Goodrich to have human-robot collaboration as the goal rather than robot autonomy [24].…”

Section: Discussionmentioning

confidence: 99%

Assistive arm and hand manipulation: How does current research intersect with actual healthcare needs?

Petrich,

Jin,

Dehghan

et al. 2021

Preprint

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Human assistive robotics have the potential to help the elderly and individuals living with disabilities with their Activities of Daily Living (ADL). Robotics researchers present bottom up solutions using various control methods for different types of movements. Health research on the other hand focuses on clinical assessment and rehabilitation leaving arguably important differences between the two domains. In particular, little is known quantitatively on what ADLs humans perform in their everyday environment -at home, work etc. This information can help guide development and prioritization of robotic technology for in-home assistive robotic deployment. This study targets several lifelogging databases, where we compute (i) ADL task frequency from long-term low sampling frequency video and Internet of Things (IoT) sensor data, and (ii) short term arm and hand movement data from 30 fps video data of domestic tasks. Robotics and health care communities have different terms and taxonomies for representing tasks and motions. We derive and discuss a robotics-relevant taxonomy from this quantitative ADL task and ICF motion data in attempt to ameliorate these taxonomic differences. Our statistics quantify that humans reach, open drawers, doors, and retrieve and use objects hundreds of times a day. Commercial wheelchair mounted robot arms can help 150,000 upper body disabled in the USA alone, but only a few hundred robots are deployed. Better user interfaces, and more capable robots can increase the potential user base and number of ADL tasks solved significantly.

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Section: Discussionmentioning

confidence: 99%

Assistive arm and hand manipulation: How does current research intersect with actual healthcare needs?

Petrich,

Jin,

Dehghan

et al. 2021

Preprint

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“…The Jaco 2 is widely used in research for assisting with drinking and eating tasks as well as with manipulation tasks. Gordon et al [72] developed an adaptive robot-assisted feeding system. The system consists of a six-DOF Jaco 2 robotic arm with a two-finger gripper.…”

Section: Assistive Robotsmentioning

confidence: 99%

A Survey of Robots in Healthcare

et al. 2021

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In recent years, with the current advancements in Robotics and Artificial Intelligence (AI), robots have the potential to support the field of healthcare. Robotic systems are often introduced in the care of the elderly, children, and persons with disabilities, in hospitals, in rehabilitation and walking assistance, and other healthcare situations. In this survey paper, the recent advances in robotic technology applied in the healthcare domain are discussed. The paper provides detailed information about state-of-the-art research in care, hospital, assistive, rehabilitation, and walking assisting robots. The paper also discusses the open challenges healthcare robots face to be integrated into our society.

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“…When considering how to control these devices, prior works break the assistive eating task into three parts: i) reaching for the human's desired food item, ii) manipulating the food item to get a bite, and then iii) returning that bite back to the human's mouth. Recent research on assistive eating has explored automating this process: here the human indicates what type of food they would like using a visual or audio interface, and then the robot autonomously reaches, manipulates, and returns a bite of the desired food to the user [18,41,19,21,9]. However, designing a fully autonomous system to handle a task as variable and personalized as eating is exceedingly challenging: consider aspects like bite size or motion timing.…”

Section: Related Workmentioning

confidence: 99%

Learning Latent Actions to Control Assistive Robots

Losey¹,

Jeon²,

Li³

et al. 2021

Preprint

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Assistive robot arms enable people with disabilities to conduct everyday tasks on their own. These arms are dexterous and high-dimensional ; however, the interfaces people must use to control their robots are low-dimensional. Consider teleoperating a 7-DoF robot arm with a 2-DoF joystick. The robot is helping you eat dinner, and currently you want to cut a piece of tofu. Today's robots assume a pre-defined mapping between joystick inputs and robot actions: in one mode the joystick controls the robot's motion in the x-y plane, in another mode the joystick controls the robot's z-yaw motion, and so on. But this mapping misses out on the task you are trying to perform! Ideally, one joystick axis should control how the robot stabs the tofu, and the other axis should control different cutting motions. Our insight is that we can achieve intuitive, userfriendly control of assistive robots by embedding the robot's high-dimensional actions into low-dimensional and human-controllable latent actions. We divide this process into three parts. First, we explore models for learning latent actions from offline task demonstrations, and formalize the properties that latent actions should satisfy. Next, we combine learned latent actions with autonomous robot assistance to help the user reach and maintain their high-level goals. Finally, we learn a personalized alignment model between joystick inputs and latent actions. We evaluate our resulting approach in four user studies where non-disabled participants reach

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Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously Unseen Food Items

Cited by 21 publications

References 30 publications

Assistive arm and hand manipulation: How does current research intersect with actual healthcare needs?

Assistive arm and hand manipulation: How does current research intersect with actual healthcare needs?

A Survey of Robots in Healthcare

Learning Latent Actions to Control Assistive Robots

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