901 research outputs found
Towards Assistive Feeding with a General-Purpose Mobile Manipulator
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.Comment: This short 4-page paper was accepted and presented as a poster on
May. 16, 2016 in ICRA 2016 workshop on 'Human-Robot Interfaces for Enhanced
Physical Interactions' organized by Arash Ajoudani, Barkan Ugurlu, Panagiotis
Artemiadis, Jun Morimoto. It was peer reviewed by one reviewe
HMMs for Anomaly Detection in Autonomous Robots
Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distri- bution. We also present a method for onine anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our online method to discriminate anomalous behaviors in real-world applications are statistically proved
HMMs for Anomaly Detection in Autonomous Robots
Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distribution. We also present a method for o!ine anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our o!ine method to discriminate anomalous behaviors in real-world applications are statistically proved
Towards safety in physically assistive robots: eating assistance
Safety is one of the base elements to build trust in robots. This paper studies remedies to unavoidable collisions using robotics assistive feeding as an example task. Firstly, we propose an attention mechanism so the user can control the robot using gestures and thus prevent collisions. Secondly, when unwanted contacts are unavoidable we compare two safety strategies: active safety, using a force sensor to monitor maximum allowed forces; and passive safety using compliant controllers. Experimental evaluation shows that the gesture mechanism is effective to control the robot. Also, the impact forces obtained with both methods are similar and thus can be used independently. Additionally, users experimenting on purpose impacts declared that the impact was not harmful.Peer ReviewedPostprint (author's final draft
HMM-based anomaly interpretation for intelligent robots in Industry 4.0
We apply an anomaly detection method based on Hidden Markov Models and Hellinger distance to a Kairos mobile robot operating in the ICE lab, a research laboratory for Industry 4.0. Two main contributions are proposed: i) a decomposition of the Hellinger distance which allows to identify the causes of anomalous behaviours detected, ii) a graphical user interface that synchronously shows the robot movements in a map and the evolution of the Hellinger distance components, allowing a quick investigation of the causes of the detected anomalies. The tools are applied to a real-world dataset allowing to discover that an anomalous movement of the Kairos robot is caused by a wrong reading of the lidar from a window in the environment
IoT driven ambient intelligence architecture for indoor intelligent mobility
Personal robots are set to assist humans in their daily tasks. Assisted living is one of the major applications of personal assistive robots, where the robots will support health and wellbeing of the humans in need, especially elderly and disabled. Indoor environments are extremely challenging from a robot perception and navigation point of view, because of the ever-changing decorations, internal organizations and clutter. Furthermore, human-robot-interaction in personal assistive robots demands intuitive and human-like intelligence and interactions. Above challenges are aggravated by stringent and often tacit requirements surrounding personal privacy that may be invaded by continuous monitoring through sensors. Towards addressing the above problems, in this paper we present an architecture for "Ambient Intelligence" for indoor
intelligent mobility by leveraging IoTs within a framework of Scalable Multi-layered Context Mapping Framework. Our
objective is to utilize sensors in home settings in the least invasive manner for the robot to learn about its dynamic surroundings and interact in a human-like manner. The paper takes a semi-survey approach to presenting and illustrating preliminary results from our in-house built fully autonomous electric quadbike
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