Assistive mobile manipulators have the potential to one day serve as surrogates and helpers for people with disabilities, giving them the freedom to perform tasks such as scratching an itch, picking up a cup, or socializing with their families. This article introduces a collaborative project with the goal of putting assistive mobile manipulators into real homes to work with people with disabilities. Through a participatory design process in which users have been actively involved from day one, we are identifying and developing assistive capabilities for the PR2 robot. Our approach is to develop a diverse suite of open source software tools that blend the capabilities of the user and the robot. Within this article, we introduce the project, describe our progress, and discuss lessons we have learned.
A novel representation for the human component of multi-step, human-robot collaborative activity is presented. The goal of the system is to predict in a probabilistic manner when the human will perform different subtasks that may require robot assistance. The representation is a graphical model where the start and end of each subtask is explicitly represented as a probabilistic variable conditioned upon prior intervals. This formulation allows the inclusion of uncertain perceptual detections as evidence to drive the predictions. Next, given a cost function that describes the penalty for different wait times, we develop a planning algorithm which selects robot-actions that minimize the expected cost based upon the distribution over predicted human-action timings. We demonstrate the approach in assembly tasks where the robot must provide the right part at the right time depending upon the choices made by the human operator during the assembly.
A representation for structured activities is developed that allows a robot to probabilistically infer which task actions a human is currently performing and to predict which future actions will be executed and when they will occur. The goal is to enable a robot to anticipate collaborative actions in the presence of uncertain sensing and task ambiguity. The system can represent multi-path tasks where the task variations may contain partially ordered actions or even optional actions that may be skipped altogether. The task is represented by an AND-OR tree structure from which a probabilistic graphical model is constructed. Inference methods for that model are derived that support a planning and execution system for the robot which attempts to minimize a cost function based upon expected human idle time. We demonstrate the theory in both simulation and actual human-robot performance of a two-waybranch assembly task. In particular we show that the inference model can robustly anticipate the actions of the human even in the presence of unreliable or noisy detections because of its integration of all its sensing information along with knowledge of task structure.
Introduction Many older adults wish to age-in-place. Robot assistance at home may be beneficial for older adults who are experiencing limitations in performing home activities. In this study we investigate older Americans’ robot acceptance before and after exposure to a domestic mobile manipulator, with an emphasis on understanding trialability (i.e., “trying out” a robot for a short time period) and result demonstrability (i.e., observing the results of the robot’s functionality). Method Older adult participants observed a mobile manipulator robot autonomously demonstrating three tasks: delivering medication, learning to turn off a light switch, and organizing home objects. We administered pre and post exposure questionnaires about participants’ opinions and attitudes toward the robot, as well as a semi-structured interview about each demonstration. Results We found that demonstration of a mobile manipulator assistive robot did, in fact, influence older adults’ acceptance. There was a significant increase, pre vs. post, in positive perceptions of robot usefulness and ease of use for 8 of the 12 Robot Opinions Questionnaire items. Furthermore, in the Assistance Preference Checklist, eighteen tasks significantly differed between pre and post exposure, with older adults showing a greater openness to robot assistance after exposure to the robot. Conclusion Thus, demonstration of robot capability positively affected older adults’ preferences for robot assistance for tasks in the home. Interview data suggest that the robot’s capability and reliability influenced older adults’ first impressions of the robot.
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.
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