In-home and remote use of robotic body surrogates by people with profound motor deficits

By controlling robots comparable to the human body, people with profound motor deficits could potentially perform a variety of physical tasks for themselves, improving their quality of life. The extent to which this is achievable has been unclear due to the lack of suitable interfaces by which to control robotic body surrogates and a dearth of studies involving substantial numbers of people with profound motor deficits. We developed a novel, web-based augmented reality interface that enables people with profound motor deficits to remotely control a PR2 mobile manipulator from Willow Garage, which is a human-scale, wheeled robot with two arms. We then conducted two studies to investigate the use of robotic body surrogates. In the first study, 15 novice users with profound motor deficits from across the United States controlled a PR2 in Atlanta, GA to perform a modified Action Research Arm Test (ARAT) and a simulated self-care task. Participants achieved clinically meaningful improvements on the ARAT and 12 of 15 participants (80%) successfully completed the simulated self-care task. Participants agreed that the robotic system was easy to use, was useful, and would provide a meaningful improvement in their lives. In the second study, one expert user with profound motor deficits had free use of a PR2 in his home for seven days. He performed a variety of self-care and household tasks, and also used the robot in novel ways. Taking both studies together, our results suggest that people with profound motor deficits can improve their quality of life using robotic body surrogates, and that they can gain benefit with only low-level robot autonomy and without invasive interfaces. However, methods to reduce the rate of errors and increase operational speed merit further investigation.Comment: 43 Pages, 13 Figure

Include 2011 : The role of inclusive design in making social innovation happen.

Include is the biennial conference held at the RCA and hosted by the Helen Hamlyn Centre for Design. The event is directed by Jo-Anne Bichard and attracts an international delegation

Prediction of Human Trajectory Following a Haptic Robotic Guide Using Recurrent Neural Networks

Social intelligence is an important requirement for enabling robots to collaborate with people. In particular, human path prediction is an essential capability for robots in that it prevents potential collision with a human and allows the robot to safely make larger movements. In this paper, we present a method for predicting the trajectory of a human who follows a haptic robotic guide without using sight, which is valuable for assistive robots that aid the visually impaired. We apply a deep learning method based on recurrent neural networks using multimodal data: (1) human trajectory, (2) movement of the robotic guide, (3) haptic input data measured from the physical interaction between the human and the robot, (4) human depth data. We collected actual human trajectory and multimodal response data through indoor experiments. Our model outperformed the baseline result while using only the robot data with the observed human trajectory, and it shows even better results when using additional haptic and depth data.Comment: 6 pages, Submitted to IEEE World Haptics Conference 201

Augmenting the Spatial Perception Capabilities of Users Who Are Blind

People who are blind face a series of challenges and limitations resulting from their lack of being able to see, forcing them to either seek the assistance of a sighted individual or work around the challenge by way of a inefficient adaptation (e.g. following the walls in a room in order to reach a door rather than walking in a straight line to the door). These challenges are directly related to blind users' lack of the spatial perception capabilities normally provided by the human vision system. In order to overcome these spatial perception related challenges, modern technologies can be used to convey spatial perception data through sensory substitution interfaces. This work is the culmination of several projects which address varying spatial perception problems for blind users. First we consider the development of non-visual natural user interfaces for interacting with large displays. This work explores the haptic interaction space in order to find useful and efficient haptic encodings for the spatial layout of items on large displays. Multiple interaction techniques are presented which build on prior research (Folmer et al. 2012), and the efficiency and usability of the most efficient of these encodings is evaluated with blind children. Next we evaluate the use of wearable technology in aiding navigation of blind individuals through large open spaces lacking tactile landmarks used during traditional white cane navigation. We explore the design of a computer vision application with an unobtrusive aural interface to minimize veering of the user while crossing a large open space. Together, these projects represent an exploration into the use of modern technology in augmenting the spatial perception capabilities of blind users