Learning to Navigate Cloth using Haptics

We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates force information from a number of haptic-sensing spheres all along the manipulator for guidance. Based on haptic forces, each individual sphere updates its target location, and the conflicts that arise between this set of desired positions is resolved by solving an inverse kinematic problem with constraints. Reinforcement learning is used to train the controller for a single haptic-sensing sphere, where a training run is terminated (and thus penalized) when large forces are detected due to contact between the sphere and a simplified model of the cloth. In simulation, we demonstrate successful navigation of a robotic arm through a variety of garments, including an isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two baseline controllers: one without haptics and another that was trained based on large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A. Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm

Deep Haptic Model Predictive Control for Robot-Assisted Dressing

Robot-assisted dressing offers an opportunity to benefit the lives of many people with disabilities, such as some older adults. However, robots currently lack common sense about the physical implications of their actions on people. The physical implications of dressing are complicated by non-rigid garments, which can result in a robot indirectly applying high forces to a person's body. We present a deep recurrent model that, when given a proposed action by the robot, predicts the forces a garment will apply to a person's body. We also show that a robot can provide better dressing assistance by using this model with model predictive control. The predictions made by our model only use haptic and kinematic observations from the robot's end effector, which are readily attainable. Collecting training data from real world physical human-robot interaction can be time consuming, costly, and put people at risk. Instead, we train our predictive model using data collected in an entirely self-supervised fashion from a physics-based simulation. We evaluated our approach with a PR2 robot that attempted to pull a hospital gown onto the arms of 10 human participants. With a 0.2s prediction horizon, our controller succeeded at high rates and lowered applied force while navigating the garment around a persons fist and elbow without getting caught. Shorter prediction horizons resulted in significantly reduced performance with the sleeve catching on the participants' fists and elbows, demonstrating the value of our model's predictions. These behaviors of mitigating catches emerged from our deep predictive model and the controller objective function, which primarily penalizes high forces.Comment: 8 pages, 12 figures, 1 table, 2018 IEEE International Conference on Robotics and Automation (ICRA

Multidimensional Capacitive Sensing for Robot-Assisted Dressing and Bathing

Robotic assistance presents an opportunity to benefit the lives of many people with physical disabilities, yet accurately sensing the human body and tracking human motion remain difficult for robots. We present a multidimensional capacitive sensing technique that estimates the local pose of a human limb in real time. A key benefit of this sensing method is that it can sense the limb through opaque materials, including fabrics and wet cloth. Our method uses a multielectrode capacitive sensor mounted to a robot's end effector. A neural network model estimates the position of the closest point on a person's limb and the orientation of the limb's central axis relative to the sensor's frame of reference. These pose estimates enable the robot to move its end effector with respect to the limb using feedback control. We demonstrate that a PR2 robot can use this approach with a custom six electrode capacitive sensor to assist with two activities of daily living-dressing and bathing. The robot pulled the sleeve of a hospital gown onto able-bodied participants' right arms, while tracking human motion. When assisting with bathing, the robot moved a soft wet washcloth to follow the contours of able-bodied participants' limbs, cleaning their surfaces. Overall, we found that multidimensional capacitive sensing presents a promising approach for robots to sense and track the human body during assistive tasks that require physical human-robot interaction.Comment: 8 pages, 16 figures, International Conference on Rehabilitation Robotics 201

The Profiles of Students With Significant Cognitive Disabilities and Known Hearing Loss

The present study describes the characteristics of students in Grades 3-12 with significant cognitive disabilities (SCD) and known hearing loss. The study analyzed results of a survey of teachers of students with SCD (n = 38,367) who were slated to participate in an alternate assessment based on alternate achievement standards in 14 states in the United States. Analysis revealed similar profiles in academic achievement and symbolic language use combined with an increased incidence of additional sensory impairments among students with SCD and known hearing loss compared to their peers without known hearing loss. Results suggest that hearing loss may be underidentified and underserved among students with SCD and point to the need for improved hearing screenings and evaluations combined with services delivered by teams that follow a model of interprofessional practice

POD Faculty Development Conference, October 17-19, 1976 -- Airlie House

The idea for this booklet came from the Lilly Endowment Incorporated\u27s Faculty Development Conference in Indianapolis earlier this year. Before that conference, we each received a booklet which included the program schedule, a list of participants and single paragraph bios, and a one-page description of each program represented at the conference. I was fascinated by the diversity of faculty development programs, and by the varied backgrounds and interests of their staffs. We decided, therefore, to put together a similar booklet for participants in this POD Conference as a part of our Information Fair. This booklet includes all of the program descriptions (generally in the order received) which I received as of Tuesday, October 12, the names and addresses of participants, and the conference program. It does not include the single paragraph bios. My apologies to all of you who prepared and sent them in. When all of the duplicating equipment at the University of Rhode Island broke down, the expense of including that information became prohibitive. Fortunately, Steve Scholl was able to get most of the other material copied at Ohio Wesleyan. My apologies too, if I mislaid any of your program descriptions and left them out of the booklet. Otherwise, I hope you find the booklet interesting and helpful. I have enjoyed reading your materials