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-way-branch 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.

The Robots for Humanity project aims to enable people with severe motor impairments to interact with their own bodies and their environment through the use of an assistive mobile manipulator, thereby improving their quality of life. Assistive mobile manipulators (AMMs) are mobile robots that physically manipulate the world in order to provide assistance to people with disabilities. They present an exciting frontier for assistive technology, as they can operate away from the user, have a large dexterous workspace (due to their mobility), and not directly encumber their users. The cornerstone of this project is an ongoing, interactive design process with a quadriplegic user, Henry Evans, and his wife and primary caregiver, Jane Evans. Henry has been enabled, through the use of a PR2 robot, to scratch his own face, shave, fetch a towel from his kitchen, and hand out Halloween candy to trick-ortreating children at a local mall.

Hygiene and feeding are activities of daily living (ADLs) that often involve contact with a person's face. Robots can assist people with motor impairments to perform these tasks by holding a tool that makes contact with the care receiver's face. By sensing the forces applied to the face with the tool, robots could potentially provide assistance that is more comfortable, safe, and effective. In order to inform the design of robotic controllers and assistive robots, we investigated the forces able-bodied people apply to themselves when wiping and shaving their faces. We present our methods for capturing and modeling these forces, results from a study with 9 participants, and recommendations for assistive robots. Our contributions include a trapezoidal force model that assumes participants have a target force they attempt to achieve for each stroke of the tool. We discuss advantages of this 3 parameter model and show that it fits our data well relative to other candidate models. We also provide statistics of the models' rise rates, fall rates, and target forces for the 9 participants in our study. In addition, we illustrate how the target forces varied based on the task, participant, and location on the face.