Person:

Kemp, Charles C.

Permanent Link

https://hdl.handle.net/1853/72201

Associated Organization(s)

Organizational Unit

Wallace H. Coulter Department of Biomedical Engineering

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Publication Search Results

Now showing 1 - 10 of 75

Visually Estimating Contact Pressure for Humans and Robots

(Georgia Institute of Technology, 2022-08-24) Kemp, Charles C.

IRIM hosts each semester a symposium to feature presentations from faculty and presentations of research that has been funded by our IRIM seed grant program in the last year. The symposium is a chance for faculty to meet new PhD students on campus, as well as a chance to get a better idea of what IRIM colleagues are up to these days. The goal of the symposium is to spark new ideas, new collaborations, and even new friends!
From One to Many: My Personal Quest for Meaningful Mobile Manipulation

(Georgia Institute of Technology, 2021-08-25) Kemp, Charles C.
Multidimensional Capacitive Sensing for Robot-Assisted Dressing and Bathing

(Georgia Institute of Technology, 2019-05-24) Erickson, Zackory ; Clever, Henry M. ; Gangaram, Vamsee ; Turk, Greg ; Liu, C. Karen ; Kemp, Charles C.

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

(PLOS One, 2019-03) Grice, Phillip M. ; Kemp, Charles C.

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
3D Human Pose Estimation on a Configurable Bed from a Pressure Image

( 2018) Clever, Henry M. ; Kapusta, Ariel ; Park, Daehyung ; Erickson, Zackory ; Chitalia, Yash ; Kemp, Charles C.

Robots have the potential to assist people in bed, such as in healthcare settings, yet bedding materials like sheets and blankets can make observation of the human body difficult for robots. A pressure-sensing mat on a bed can provide pressure images that are relatively insensitive to bedding materials. However, prior work on estimating human pose from pressure images has been restricted to 2D pose estimates and flat beds. In this work, we present two convolutional neural networks to estimate the 3D joint positions of a person in a configurable bed from a single pressure image. The first network directly outputs 3D joint positions, while the second outputs a kinematic model that includes estimated joint angles and limb lengths. We evaluated our networks on data from 17 human participants with two bed configurations: supine and seated. Our networks achieved a mean joint position error of 77 mm when tested with data from people outside the training set, outperforming several baselines. We also present a simple mechanical model that provides insight into ambiguity associated with limbs raised off of the pressure mat, and demonstrate that Monte Carlo dropout can be used to estimate pose confidence in these situations. Finally, we provide a demonstration in which a mobile manipulator uses our network’s estimated kinematic model to reach a location on a person’s body in spite of the person being seated in a bed and covered by a blanket.
Haptic Simulation for Robot-Assisted Dressing

(Georgia Institute of Technology, 2017) Yu, Wenhao ; Kapusta, Ariel ; Tan, Jie ; Kemp, Charles C. ; Turk, Greg ; Liu, C. Karen

There is a considerable need for assistive dressing among people with disabilities, and robots have the potential to fulfill this need. However, training such a robot would require extensive trials in order to learn the skills of assistive dressing. Such training would be time-consuming and require considerable effort to recruit participants and conduct trials. In addition, for some cases that might cause injury to the person being dressed, it is impractical and unethical to perform such trials. In this work, we focus on a representative dressing task of pulling the sleeve of a hospital gown onto a person’s arm. We present a system that learns a haptic classifier for the outcome of the task given few (2-3) real-world trials with one person. Our system first optimizes the parameters of a physics simulator using real-world data. Using the optimized simulator, the system then simulates more haptic sensory data with noise models that account for randomness in the experiment. We then train hidden Markov Models (HMMs) on the simulated haptic data. The trained HMMs can then be used to classify and predict the outcome of the assistive dressing task based on haptic signals measured by a real robot’s end effector. This system achieves 92.83% accuracy in classifying the outcome of the robot-assisted dressing task with people not included in simulation optimization. We compare our classifiers to those trained on real-world data. We show that the classifiers from our system can categorize the dressing task outcomes more accurately than classifiers trained on ten times more real data.
Data-Driven Haptic Perception for Robot-Assisted Dressing

(Georgia Institute of Technology, 2016-08) Kapusta, Ariel ; Yu, Wenhao ; Bhattacharjee, Tapomayukh ; Liu, C. Karen ; Turk, Greg ; Kemp, Charles C.

Dressing is an important activity of daily living (ADL) with which many people require assistance due to impairments. Robots have the potential to provide dressing assistance, but physical interactions between clothing and the human body can be complex and difficult to visually observe. We provide evidence that data-driven haptic perception can be used to infer relationships between clothing and the human body during robot-assisted dressing. We conducted a carefully controlled experiment with 12 human participants during which a robot pulled a hospital gown along the length of each person’s forearm 30 times. This representative task resulted in one of the following three outcomes: the hand missed the opening to the sleeve; the hand or forearm became caught on the sleeve; or the full forearm successfully entered the sleeve. We found that hidden Markov models (HMMs) using only forces measured at the robot’s end effector classified these outcomes with high accuracy. The HMMs’ performance generalized well to participants (98.61% accuracy) and velocities (98.61% accuracy) outside of the training data. They also performed well when we limited the force applied by the robot (95.8% accuracy with a 2N threshold), and could predict the outcome early in the process. Despite the lightweight hospital gown, HMMs that used forces in the direction of gravity substantially outperformed those that did not. The best performing HMMs used forces in the direction of motion and the direction of gravity.
Collaboration Between a Robotic Bed and a Mobile Manipulator May Improve Physical Assistance for People with Disabilities

(Georgia Institute of Technology, 2016-08) Kapusta, Ariel ; Chitalia, Yash ; Park, Daehyung ; Kemp, Charles C.

We present a robotic system designed to provide physical assistance to a person in bed. The system consists of a robotic bed (Autobed) and a mobile manipulator (PR2) that work together. The 3 degree-of-freedom (DoF) robotic bed moves the person’s body and uses a pressure sensing mat to estimate the body’s position. The mobile manipulator positions itself with respect to the bed and compliantly moves a lightweight object with one of its 7-DoF arms. The system optimizes its motions with respect to a task model and a model of the human’s body. The user provides high-level supervision to the system via a web-based interface. We first evaluated the ability of the robotic bed to estimate the location of the head of a person in a supine configuration via a study with 7 able-bodied participants. This estimation was robust to bedding, including a pillow under the person’s head. We then evaluated the ability of the full system to autonomously reach task-relevant poses on a medical mannequin placed in a supine position on the bed. We found that the robotic bed’s motion and perception each improved the overall system’s performance. Our results suggest that this type of multi-robot system could more effectively bring objects to desired locations with respect to the user’s body than a mobile manipulator working alone. This may in turn lead to improved physical assistance for people with disabilities at home and in healthcare facilities, since many assistive tasks involve an object being moved with respect to a person’s body.
Multimodal Execution Monitoring for Anomaly Detection During Robot Manipulation

(Georgia Institute of Technology, 2016-05) Park, Daehyung ; Erickson, Zackory ; Bhattacharjee, Tapomayukh ; Kemp, Charles C.

Online detection of anomalous execution can be valuable for robot manipulation, enabling robots to operate more safely, determine when a behavior is inappropriate, and otherwise exhibit more common sense. By using multiple complementary sensory modalities, robots could potentially detect a wider variety of anomalies, such as anomalous contact or a loud utterance by a human. However, task variability and the potential for false positives make online anomaly detection challenging, especially for long-duration manipulation behaviors. In this paper, we provide evidence for the value of multimodal execution monitoring and the use of a detection threshold that varies based on the progress of execution. Using a data-driven approach, we train an execution monitor that runs in parallel to a manipulation behavior. Like previous methods for anomaly detection, our method trains a hidden Markov model (HMM) using multimodal observations from non-anomalous executions. In contrast to prior work, our system also uses a detection threshold that changes based on the execution progress. We evaluated our approach with haptic, visual, auditory, and kinematic sensing during a variety of manipulation tasks performed by a PR2 robot. The tasks included pushing doors closed, operating switches, and assisting ablebodied participants with eating yogurt. In our evaluations, our anomaly detection method performed substantially better with multimodal monitoring than single modality monitoring. It also resulted in more desirable ROC curves when compared with other detection threshold methods from the literature, obtaining higher true positive rates for comparable false positive rates.
Towards Assistive Feeding with a General-Purpose Mobile Manipulator

(Georgia Institute of Technology, 2016-05) Park, Daehyung ; Kim, You Keun ; Erickson, Zackory ; Kemp, Charles C.

General-purpose mobile manipulators have the potential to serve as a versatile form of assistive technology. However, their complexity creates challenges, including the risk of being too difficult to use. We present a proof-of-concept robotic system for assistive feeding that consists of a Willow Garage PR2, a high-level web-based interface, and specialized autonomous behaviors for scooping and feeding yogurt. As a step towards use by people with disabilities, we evaluated our system with 5 able-bodied participants. All 5 successfully ate yogurt using the system and reported high rates of success for the system’s autonomous behaviors. Also, Henry Evans, a person with severe quadriplegia, operated the system remotely to feed an able-bodied person. In general, people who operated the system reported that it was easy to use, including Henry. The feeding system also incorporates corrective actions designed to be triggered either autonomously or by the user. In an offline evaluation using data collected with the feeding system, a new version of our multimodal anomaly detection system outperformed prior versions.