A soft, sensorized gripper for delicate harvesting of small fruits

Harvesting fruits and vegetables is a complex task worth to be fully automated with robotic systems. It involves several precision tasks that have to be performed with accuracy and the appropriate amount of force. Classical mechanical grippers, due to the complex control and stiffness, cannot always be used to harvest fruits and vegetables. Instead, the use of soft materials could provide a visible advancement. In this work, we propose a soft, sensorized gripper for harvesting applications. The sensing is performed by tracking a set of markers integrated into the soft part of the gripper. Different machine learning-based approaches have been used to map the markers’ position and dimensions into forces in order to perform a close-loop control of the gripper. Results show that force can be measured with an error of 2.6% in a range from 0 to 4 N. The gripper was integrated into a robotic arm having an external vision system used to detect plants and fruits (strawberries in our case scenario). As a proof of concept, we evaluated the performance of the robotic system in a laboratory scenario. Plant and fruit identification reached a positive rate of 98.2% and 92.4%, respectively, while the correct picking of the fruits, by removing it from the stalk without a direct cut, achieved an 82% of successful rate

Binge and Emotional Eating in obese subjects seeking weight loss treatment

Objective: Binge Eating Disorder (BED) is highly prevalent among individuals seeking weight loss treatment. Considering the possible trigger factors for BED, different studies focused on the role of emotional eating. The present study compared threshold, subthreshold BED, and subjects without BED in a population of overweight/obese individuals seeking weight loss treatment, considering the anamnesis, the eating disorder specific and general psychopathology, the organic and psychiatric comorbidity, the emotional eating as a trigger factor for binge eating, and the quality of life. Design: cross-sectional survey.Subjects: Four hundred thirty eight overweight subjects seeking weight loss treatment have been enrolled in the study. Measurements: Subjects have been evaluated by means of a clinical interview (SCID I) and different self-reported questionnaires (Eating Disorder Examination Questionnaire, Binge Eating Scale, Beck Depression Inventory, Spielberg's State-Trait Anxiety Inventory, Symptom Checklist 90, Emotional Eating Scale, and Obesity Related Well-Being questionnaire). Results: One hundred and five subjects (24% of the sample) fulfilled the DSM-IV criteria of lifetime BED, 146 (33.3%) fulfilled the criteria of lifetime subthreshold BED, and 187 (42.7%) subjects were diagnosed overweight non-BED. No correlations between the binges frequencies and the overweight levels were found. All the three groups showed high psychiatric comorbidities, and the three groups significantly differed in terms of emotional eating, which was positively correlated to the binge eating frequencies. Conclusions: Threshold and subthreshold BED deserve a careful psychopathological investigation and emotional eating seems to play a key role as trigger factor for binge eating. Obesity is associated with a high psychiatric comorbidity and a low quality of life, independently from the specific and general eating disorder psychopathology

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

POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments

In this paper we focus on the problem of learning an optimal policy for Active Visual Search (AVS) of objects in known indoor environments with an online setup. Our POMP method uses as input the current pose of an agent (e.g. a robot) and a RGB-D frame. The task is to plan the next move that brings the agent closer to the target object. We model this problem as a Partially Observable Markov Decision Process solved by a Monte-Carlo planning approach. This allows us to make decisions on the next moves by iterating over the known scenario at hand, exploring the environment and searching for the object at the same time. Differently from the current state of the art in Reinforcement Learning, POMP does not require extensive and expensive (in time and computation) labelled data so being very agile in solving AVS in small and medium real scenarios. We only require the information of the floormap of the environment, an information usually available or that can be easily extracted from an a priori single exploration run. We validate our method on the publicly available AVD benchmark, achieving an average success rate of 0.76 with an average path length of 17.1, performing close to the state of the art but without any training needed. Additionally, we show experimentally the robustness of our method when the quality of the object detection goes from ideal to faulty

Multivariate sensor signals collected by aquatic drones involved in water monitoring: A complete dataset

Sensor data generated by intelligent systems, such as autonomous robots, smart buildings and other systems based on artificial intelligence, represent valuable sources of knowledge in today\u2019s data-driven society, since they contain information about the situations these systems face during their operation. These data are usually multivariate time series since modern technologies enable the simultaneous acquisition of multiple signals during long periods of time. In this paper we present a dataset containing sensor traces of six data acquisition campaigns performed by autonomous aquatic drones involved in water monitoring. A total of 5.6 hours of navigation are available, with data coming from both lakes and rivers, and from different locations in Italy and Spain. The monitored variables concern both the internal state of the drone (e.g., battery voltage, GPS position and signals to propellers) and the state of the water (e.g., temperature, dissolved oxygen and electrical conductivity). Data were collected in the context of the EU-funded Horizon 2020 project INTCATCH (http://www.intcatch.eu) which aims to develop a new paradigm for monitoring water quality of catchments. The aquatic drones used for data acquisition are Platypus Lutra boats. Both autonomous and manual drive is used in different parts of the navigation. The dataset is analyzed in the paper \u201cTime series segmentation for state-model generation of autonomous aquatic drones: A systematic framework\u201d [1] by means of recent time series clustering/segmentation techniques to extract data-driven models of the situations faced by the drones in the data acquisition campaigns. These data have strong potential for reuse in other kinds of data analysis and evaluation of machine learning methods on real-world datasets [2]. Moreover, we consider this dataset valuable also for the variety of situations faced by the drone, from which machine learning techniques can learn behavioural patterns or detect anomalous activities. We also provide manual labeling for some known states of the drones, such as, drone inside/outside the water, upstream/downstream navigation, manual/autonomous drive, and drone turning, that represent a ground truth for validation purposes. Finally, the real-world nature of the dataset makes it more challenging for machine learning methods because it contains noisy samples collected while the drone was exposed to atmospheric agents and uncertain water flow conditions

POMP++: Pomcp-based Active Visual Search in unknown indoor environments

In this paper we focus on the problem of learning online an optimal policy for Active Visual Search (AVS) of objects in unknown indoor environments. We propose POMP++, a planning strategy that introduces a novel formulation on top of the classic Partially Observable Monte Carlo Planning (POMCP) framework, to allow training-free online policy learning in unknown environments. We present a new belief reinvigoration strategy which allows to use POMCP with a dynamically growing state space to address the online generation of the floor map. We evaluate our method on two public benchmark datasets, AVD that is acquired by real robotic platforms and Habitat ObjectNav that is rendered from real 3D scene scans, achieving the best success rate with an improvement of >10% over the state-of-the-art methods

Subspace clustering for situation assessment in aquatic drones

We propose a novel methodology based on subspace clustering for detecting, modeling and interpreting aquatic drone states in the context of autonomous water monitoring. It enables both more informative and focused analysis of the large amounts of data collected by the drone, and enhanced situation awareness, which can be exploited by operators and drones to improve decision making and autonomy. The approach is completely data-driven and unsupervised. It takes unlabeled sensor traces from several water monitoring missions and returns both a set of sparse drone state models and a clustering of data samples according to these models. We tested the methodology on a real dataset containing data of six different missions, two rivers and four lakes in different countries, for about 5.5 hours of navigation. Results show that the methodology is able to recognize known states “in/out of the water”, “up- stream/downstream navigation” and “manual/autonomous drive”, and to discover meaningful unknown states from their data-based properties, enabling novelty detection

Safe and Efficient Reinforcement Learning for Environmental Monitoring

This paper discusses the challenges of applying reinforcement techniques to real-world environmental monitoring problems and proposes innovative solutions to overcome them. In particular, we focus on safety, a fundamental problem in RL that arises when it is applied to domains involving humans or hazardous uncertain situations. We propose to use deep neural networks, formal verification, and online refinement of domain knowledge to improve the transparency and efficiency of the learning process, as well as the quality of the final policies. We present two case studies, specifically (i) autonomous water monitoring and (ii) smart control of air quality indoors. In particular, we discuss the challenges and solutions to these problems, addressing crucial issues such as anomaly detection and prevention, real-time control, and online learning. We believe that the proposed techniques can be used to overcome some limitations of RL, providing safe and efficient solutions to complex and urgent problems