Eating is an essential activity of daily living (ADL) for staying healthy and living at home independently. Although numerous assistive devices have been introduced, many people with disabilities are still restricted from independent eating due to the devices' physical or perceptual limitations. In this work, we introduce a new meal-assistance system using a general-purpose mobile manipulator, a Willow Garage PR2, which has the potential to serve as a versatile form of assistive technology. Our active feeding framework enables the robot to autonomously deliver food to the user's mouth. In detail, our web-based user interface, visually-guided behaviors, and safety tools allow people with severe motor impairments to benefit from the robotic assistance. We evaluated our system with 10 able-bodied participants and 9 people with motor impairments. Both groups of participants successfully ate various foods using the system and reported high rates of success for the system's autonomous behaviors in a laboratory environment. Then, we performed in-home evaluation with Henry Evans, a person with quadriplegia, at his house in California, USA. In general, Henry and the other people who operated the system reported that it was comfortable, safe, and easy-to-use. We discuss learned lessons and design insights through user evaluations.
The absence of mobility is an important issue in robotic assistance. found that movement of a mobile manipulator's base was needed to provide assistance with a shaving task, since the PR2 that they used could not otherwise reach the relevant locations @cite_26 . In feeding, the fixed robot base often requires the relocation of the robot or user by caregivers in the beginning or during the task. A fixed base restricts the scope of assistive tasks @cite_64 . Without mobility, robots are restricted to a narrow set of tasks and are unable to leave the immediate vicinity of the human to provide assistance elsewhere. Recent studies have introduced general-purpose mobile manipulators for various assistive robotic tasks, including shaving @cite_46 @cite_39 , dressing @cite_66 @cite_2 @cite_40 , fetch-and-carry @cite_19 @cite_12 @cite_59 @cite_50 , and guiding tasks @cite_4 . Our meal-assistance system has a mobile base that has the potential to enhance the quality of feeding assistance.
@cite_26 @cite_64 @cite_4 @cite_39 @cite_19 @cite_40 @cite_59 @cite_2 @cite_50 @cite_46 @cite_66 @cite_12
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Human-scale mobile robots with arms have the potential to assist people with a variety of tasks. We present a proof-of-concept system that has enabled a person with severe quadriplegia named Henry Evans to shave himself in his own home using a general purpose mobile manipulator (PR2 from Willow Garage). The robot primarily provides assistance by holding a tool (e.g., an electric shaver) at user-specified locations around the user's head, while he she moves his her head against it. If the robot detects forces inappropriate for the task (e.g., shaving), it withdraws the tool. The robot also holds a mirror with its other arm, so that the user can see what he she is doing. For all aspects of the task, the robot and the human work together. The robot uses a series of distinct semi-autonomous subsystems during the task to navigate to poses next to the wheelchair, attain initial arm configurations, register a 3D model of the person's head, move the tool to coarse semantically-labeled tool poses (e.g, “Cheek”), and finely position the tool via incremental movements. Notably, while moving the tool near the user's head, the robot uses an ellipsoidal coordinate system attached to the 3D head model. In addition to describing the complete robotic system, we report results from Henry Evans using it to shave both sides of his face while sitting in his wheelchair at home. He found the process to be long (54 minutes) and the interface unintuitive. Yet, he also found the system to be comfortable to use, felt safe while using it, was satisfied with it, and preferred it to a human caregiver. Mobility assistance robots provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the drift-diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide (a) cognitive assistance to help the user following a desired path towards a predefined destination as well as (b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of (c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots. When a mobile manipulator functions as an assistive device, the robot's initial configuration and the configuration of the environment can impact the robot's ability to provide effective assistance. Selecting initial configurations for assistive tasks can be challenging due to the high number of degrees of freedom of the robot, the environment, and the person, as well as the complexity of the task. In addition, rapid selection of initial conditions can be important, so that the system will be responsive to the user and will not require the user to wait a long time while the robot makes a decision. To address these challenges, we present Task-centric initial Configuration Selection (TCS), which unlike previous work uses a measure of task-centric manipulability to accommodate state estimation error, considers various environmental degrees of freedom, and can find a set of configurations from which a robot can perform a task. TCS performs substantial offline computation, so that it can rapidly provide solutions at run time. At run time, the system performs an optimization over candidate initial configurations using a utility function that can include factors such as movement costs for the robot's mobile base. To evaluate TCS, we created models of 11 activities of daily living (ADLs) and evaluated TCS's performance with these 11 assistive tasks in a computer simulation of a PR2, a robotic bed, and a model of a human body. TCS performed as well or better than a baseline algorithm in all of our tests against state estimation error. Many activities of daily living, such as picking up glasses, holding a fork or opening a door, which most people do without thinking, can become insurmountable for people who have upper extremity disabilities. The alternative to asking for human help is to use some assistive devices to compensate their loss of mobility; however, many of those devices are limited in terms of functionality. Robotics may provide a better approach for the development of assistive devices, by allowing greater functionality. In this paper, we present results of a study (n=31) which objectives were to evaluate the efficacy of a new joystick-controlled seven-degree of freedom robotic manipulator and assess its potential economic benefits. Results show that JACO is easy to use as the majority of the participants were able to accomplish the testing tasks on their first attempt. The economic model results inferred that the use of the JACO arm system could potentially reduce caregiving time by 41 . These study results are expected to provide valuable data for interested parties, such as individuals with disabilities, their family or caregivers.