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Healthcare Robotics Lab
Healthcare Robotics Lab
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ItemMultimodal 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.
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ItemMaterial Recognition from Heat Transfer given Varying Initial Conditions and Short-Duration Contact(Georgia Institute of Technology, 2015) Bhattacharjee, Tapomayukh ; Wade, Joshua ; Kemp, Charles C.When making contact with an object, a robot can use a tactile sensor consisting of a heating element and a temperature sensor to recognize the object’s material based on conductive heat transfer from the tactile sensor to the object. When this type of tactile sensor has time to fully reheat prior to contact and the duration of contact is long enough to achieve a thermal steady state, numerous methods have been shown to perform well. In order to enable robots to more efficiently sense their environments and take advantage of brief contact events over which they lack control, we focus on the problem of material recognition from heat transfer given varying initial conditions and short-duration contact. We present both modelbased and data-driven methods. For the model-based method, we modeled the thermodynamics of the sensor in contact with a material as contact between two semi-infinite solids. For the data-driven methods, we used three machine learning algorithms (SVM+PCA, k-NN+PCA, HMMs) with time series of raw temperature measurements and temperature change estimates. When recognizing 11 materials with varying initial conditions and 3- fold cross-validation, SVM+PCA outperformed all other methods, achieving 84% accuracy
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ItemInferring Object Properties from Incidental Contact with a Tactile-Sensing Forearm(Georgia Institute of Technology, 2014-09) Bhattacharjee, Tapomayukh ; Rehg, James M. ; Kemp, Charles C.Whole-arm tactile sensing enables a robot to sense properties of contact across its entire arm. By using this large sensing area, a robot has the potential to acquire useful information from incidental contact that occurs while performing a task. Within this paper, we demonstrate that data-driven methods can be used to infer mechanical properties of objects from incidental contact with a robot’s forearm. We collected data from a tactile-sensing forearm as it made contact with various objects during a simple reaching motion. We then used hidden Markov models (HMMs) to infer two object properties (rigid vs. soft and fixed vs. movable) based on low-dimensional features of time-varying tactile sensor data (maximum force, contact area, and contact motion). A key issue is the extent to which data-driven methods can generalize to robot actions that differ from those used during training. To investigate this issue, we developed an idealized mechanical model of a robot with a compliant joint making contact with an object. This model provides intuition for the classification problem. We also conducted tests in which we varied the robot arm’s velocity and joint stiffness. We found that, in contrast to our previous methods [1], multivariate HMMs achieved high cross-validation accuracy and successfully generalized what they had learned to new robot motions with distinct velocities and joint stiffnesses.
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ItemA Robotic System for Reaching in Dense Clutter that Integrates Model Predictive Control, Learning, Haptic Mapping, and Planning(Georgia Institute of Technology, 2014-09) Bhattacharjee, Tapomayukh ; Grice, Phillip M. ; Kapusta, Ariel ; Killpack, Marc D. ; Park, Daehyung ; Kemp, Charles C.We present a system that enables a robot to reach locations in dense clutter using only haptic sensing. Our system integrates model predictive control [1], learned initial conditions [2], tactile recognition of object types [3], haptic mapping, and geometric planning to efficiently reach locations using whole- arm tactile sensing [4]. We motivate our work, present a system architecture, summarize each component of the system, and present results from our evaluation of the system reaching to target locations in dense artificial foliage.