Active robot-assisted feeding with a general-purpose mobile manipulator: Design, evaluation, and lessons learned

Park, Daehyung; Hoshi, Yuuna; Mahajan, Harshal P.; Kim, Hokeun; Erickson, Zackory; Rogers, Wendy A.; Kemp, Charles C.

doi:10.1016/j.robot.2019.103344

Cited by 79 publications

(39 citation statements)

References 53 publications

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“…Assistive mobile robots hold great promise for promoting independence, home healthcare, and aging in place for people across the life course (Haak et al, 2007;Smarr et al 2013;Pino et al, 2015). For example, mobile manipulators are robots that can physically manipulate objects and navigate through environments (Park et al 2020). Assistive robots are not yet widely commercially available for home use (Wiczorek et al, 2020) perhaps due to their size and cost (e.g., PR2, Beer et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A human factors analysis of the Stretch mobile manipulator robot

Kadylak

Bayles

Galoso

et al. 2021

Proceedings of the Human Factors and Ergonomics Society Annual

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Assistive and mobile robots have potential to support everyday domestic tasks and enable independence for persons in the home. As a first step to evaluating this potential, we assessed the initial unboxing and setup of Hello Robot’s Stretch RE1– a novel mobile manipulator designed for domestic settings. All study procedures took place in the McKechnie Family LIFE Home, which is a smart home research facility on the University of Illinois Urbana-Champaign campus. We used subject matter experts (SMEs) and followed human factors principles to consider obstacles users with diverse needs and capabilities (e.g., older adults, persons with mobility disabilities) might encounter during the unboxing process. We then conducted 50 trials of user testing and critical task analyses in the LIFE home to assess the feasibility and usability for different use cases. Research team members controlled Stretch by using a game controller. We used Stretch to manipulate 15 different types of objects that would be part of domestic activities needed to live independently, such as tasks needed for meal preparation. We documented the frequency of errors, time spent manipulating the object, and informal qualitative feedback from teleoperators during and after each trial (using a think-aloud protocol). Implications for future domestic robot design using human factors approaches are discussed.

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Section: Introductionmentioning

confidence: 99%

A human factors analysis of the Stretch mobile manipulator robot

Kadylak

Bayles

Galoso

et al. 2021

Proceedings of the Human Factors and Ergonomics Society Annual

Self Cite

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“…2019 ; Park et al. 2020 ). Determining which objects are potential goals is simplified in eating settings, since the target items are largely consistent (e.g., food items, cups, plates, and bowls).…”

Section: Algorithmmentioning

confidence: 99%

Learning latent actions to control assistive robots

Losey

Jeon

et al. 2021

Auton Robot

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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, user-friendly 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 marshmallows, cook apple pie, cut tofu, and assemble dessert. We then test our approach with two disabled adults who leverage assistive devices on a daily basis.

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“…x dc , y dc , and z dc indicate the translational distance from the midpoint of the driving axis to the body-fixed frame along the three axes. According to the above Equations ( 6)-( 8), the position and posture of the end of the mobile manipulator can be specifically expressed with the homogeneous transformation matrix in Equation (2). For the designated task, the position and posture of the robot's end are mainly described as follows: The position and posture of the manipulator's end are mainly described as six variables in the Cartesian coordinate system.…”

Section: Optimized Kinematic Modeling Of the Entire Systemmentioning

confidence: 99%

“…The mobile manipulator robot fixes the traditional manipulator on the mobile platform, thereby extending the substantial potential applications of the fixed manipulator [1,2]. Due to the combination of the high mobility of the mobile platform and the dexterous maneuverability of the manipulator, it can be widely used in current life and work, such as in planetary exploration, nuclear reactor maintenance, landmine detection, agriculture missions, and fire rescue operations [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Kinematic Modeling and Self-Collision Detection of a Mobile Manipulator Robot by Considering the Actual Physical Structure

Qiao

Luo

et al. 2021

Applied Sciences

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In this paper, an optimized kinematic modeling method to accurately describe the actual structure of a mobile manipulator robot with a manipulator similar to the universal robot (UR5) is developed, and an improved self-collision detection technology realized for improving the description accuracy of each component and reducing the time required for approximating the whole robot is introduced. As the primary foundation for trajectory tracking and automatic navigation, the kinematic modeling technology of the mobile manipulator has been the subject of much interest and research for many years. However, the kinematic model established by various methods is different from the actual physical model due to the fact that researchers have mainly focused on the relationship between driving joints and the end positions while ignoring the physical structure. To improve the accuracy of the kinematic model, we present a kinematic modeling method with the addition of key points and coordinate systems to some components that failed to model the physical structure based on the classical method. Moreover, self-collision detection is also a primary problem for successfully completing the specified task of the mobile manipulator. In traditional self-collision detection technology, the description of each approximation is determined by the spatial transformation of each corresponding component in the mobile manipulator robot. Unlike the traditional technology, each approximation in the paper is directly established by the physical structure used in the kinematic modeling method, which significantly reduces the complicated analysis and shortens the required time. The numerical simulations prove that the kinematic model with the addition of key point technology is similar to the actual structure of mobile manipulator robots, and the self-collision detection technology proposed in the article effectively improves the performance of self-collision detection. Additionally, the experimental results prove that the kinematic modeling method and self-collision detection technology outlined in this paper can optimize the inverse kinematics solution.

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Active robot-assisted feeding with a general-purpose mobile manipulator: Design, evaluation, and lessons learned

Cited by 79 publications

References 53 publications

A human factors analysis of the Stretch mobile manipulator robot

A human factors analysis of the Stretch mobile manipulator robot

Learning latent actions to control assistive robots

Optimizing Kinematic Modeling and Self-Collision Detection of a Mobile Manipulator Robot by Considering the Actual Physical Structure

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