Function detection is a well-known problem in binary analysis. While prior work has focused on Linux/ELF, Windows/PE binaries have only partially been considered. This paper introduces FuncPEval, a dataset for Windows x86 and x64 PE files, featuring Chromium and the Conti ransomware, along with ground truth data for 1,092,820 function starts. Utilizing FuncPEval, we evaluate five heuristics-based (Ghidra, IDA, Nucleus, rev.ng, SMDA) and three machine-learning-based (DeepDi, RNN, XDA) function start detection tools. Among these, IDA achieves the highest F1-score (98.44%) for Chromium x64, while DeepDi closely follows (97%) but stands out as the fastest. Towards explainability, we examine the impact of padding between functions on the detection results, finding all tested tools, except rev.ng, are susceptible to randomized padding. The randomized padding significantly diminishes the effectiveness of the RNN, XDA, and Nucleus. Among the learning-based tools, DeepDi exhibits the least sensitivity, while Nucleus is the most adversely affected among the non-learning-based tools.

To achieve a desired grasping posture (including object position and orientation), multi-finger motions need to be conducted according to the the current touch state. Specifically, when subtle changes happen during correcting the object state, not only proprioception but also tactile information from the entire hand can be beneficial. However, switching motions with high-DOFs of multiple fingers and abundant tactile information is still challenging. In this study, we propose a loss function with constraints of touch states and an attention mechanism for focusing on important modalities depending on the touch states. The policy model is AE-LSTM which consists of Autoencoder (AE) which compresses abundant tactile information and Long Short-Term Memory (LSTM) which switches the motion depending on the touch states. Motion for cap-opening was chosen as a target task which consists of sub tasks of sliding an object and opening its cap. As a result, the proposed method achieved the best success rates with a variety of objects for real time cap-opening manipulation. Furthermore, we could confirm that the proposed model acquired the features of each subtask and attention on specific modalities.

Direct teaching is a straightforward way of teaching new motion to robots. Active methods with torque sensors, for example, can be used so that the robot can follow the movements of the human, but such methods introduce delays. Alternatively, series clutch actuators are easily backdrivable without delay. However, vertical joints are subject to gravity torques, which need to be compensated when disengaging the clutch. We implemented passive gravity compensation to counteract the robot’s weight, but this mechanism cannot compensate for varying payloads, as adjustable passive gravity compensation is relatively slow and mechanically complex. The varying payload causes an unintended joint movement, i.e. the arm falls down on its own, which is unacceptable during direct teaching. Therefore, this paper demonstrates how the torque output controlled with series clutch actuators can be used to compensate for varying payloads while maintaining high backdrivability. The proposed method is evaluated on a collaborative robot with a clutch in series for each actuator. Real-world experiments with payloads from 0 to 3 kg are conducted. During the experiments, the operator force is measured to evaluate the proposed method.

Multi-fingered hands can be suitable for stable object manipulation. Furthermore, abundant tactile information can be acquired with multi-fingered hands, useful to recognize the object’s properties, which is beneficial to adapt the motion to the object. However, generating dexterous manipulation motions with multi-fingered hands with high density tactile sensors is challenging due to complex touch states. Hence, tasks that conventionally require a high level of active tactile sensing simultaneously with motion generation, such as pulling in the hand while recognizing the posture of an object are difficult to accomplish. In this letter, we propose a novel deep predictive learning approach using Vision-Transformer (ViT) and Long-Short Term Memory (LSTM). The ViT’s attention mechanism can spatially focus on specific fingers represented by distributed 3-axis tactile sensors (uSkin). The LSTM can preserve long time-series information of the manipulation which can realize changing the desired motion according to the initial touching position and orientation for the target object. Results showed that the ViT-LSTM is effective in performing adaptive finger movements according to the properties of the object, i.e. its hardness and relative posture.

Intelligent robots are expected to do manipulation tasks relying on real-time sensing feedback. Especially, tactile sensing plays a more and more important role in precise manipulation tasks. For example, a 1 mm error while inserting a USB stick, which is hard to perceive visually, will result in a failed insertion or even break the USB stick. In this paper, to estimate and compensate residual position uncertainties during robotic insertion tasks, an exploration motion is introduced to acquire environment information by tactile sensing and a state-of-the-art transformer-based neural network is proposed to estimate the error distance from long-duration tactile sensing data. Our system is trained on over 2000 insertion trials with basic geometry shaped 3D printed objects. Without any prior knowledge, we achieve an 85% insertion success rate with average 5 attempts on 4 unseen daily objects relying only on tactile feedback acquired from our proposed exploratory motion. It is noteworthy that our designed exploration motion can provide insightful information about extrinsic contact information and our proposed learning model exceeds previous baselines in extracting useful information regarding the contact interaction between the grasped object and the environment.

Multifingered robot hands can be extremely effective in physically exploring and recognizing objects, especially if they are extensively covered with distributed tactile sensors. Convolutional neural networks (CNNs) have been proven successful in processing high dimensional data, such as camera images, and are, therefore, very well suited to analyze distributed tactile information as well. However, a major challenge is to organize tactile inputs coming from different locations on the hand in a coherent structure that could leverage the computational properties of the CNN. Therefore, we introduce a morphology-specific CNN (MS-CNN), in which hierarchical convolutional layers are formed following the physical configuration of the tactile sensors on the robot. We equipped a four-fingered Allegro robot hand with several uSkin tactile sensors; overall, the hand is covered with 240 sensitive elements, each one measuring three-axis contact force. The MS-CNN layers process the tactile data hierarchically: at the level of small local clusters first, then each finger, and then the entire hand. We show experimentally that, after training, the robot hand can successfully recognize objects by a single touch, with a recognition rate of over 95%. Interestingly, the learned MS-CNN representation transfers well to novel tasks: by adding a limited amount of data about new objects, the network can recognize nine types of physical properties.

Skill transfer from humans to robots is challenging. Presently, many researchers focus on capturing only position or joint angle data from humans to teach the robots. Even though this approach has yielded impressive results for grasping applications, reconstructing motion for object handling or fine manipulation from a human hand to a robot hand has been sparsely explored. Humans use tactile feedback to adjust their motion to various objects, but capturing and reproducing the applied forces is an open research question. In this paper we introduce a wearable fingertip tactile sensor, which captures the distributed 3-axis force vectors on the fingertip. The fingertip tactile sensor is interchangeable between the human hand and the robot hand, meaning that it can also be assembled to fit on a robot hand such as the Allegro hand. This paper presents the structural aspects of the sensor as well as the methodology and approach used to design, manufacture, and calibrate the sensor. The sensor is able to measure forces accurately with a mean absolute error of 0.21, 0.16, and 0.44 Newtons in X, Y, and Z directions, respectively.

Detecting the onset/ongoing of slip, i.e. if a grasped object is slipping or will slip from the gripper while being lifted, is crucial. Conventionally, it is regarded as a tactile sensing related problem. However, recently multi-modal robotic learning has become popular and is expected to boost the performance. In this paper we propose a novel CNN-TCN model to fuse tactile and visual information for detecting the onset/ongoing of slip. In our experiments, two uSkin tactile sensors and one Realsense435i camera are used. Data is collected by randomly grasping and lifting 35 daily objects 1050 times in total. Furthermore, we compare our CNN-TCN model with the widely used CNN-LSTM model. As a result, our proposed model achieves a 88.75% detection accuracy and outperforms the CNN-LSTM model combined with different pretrained vision networks.

Recently, tactile sensing has attracted great interest for robotic manipulation. Predicting if a grasp will be stable or not, i.e. if the grasped object will drop out of the gripper while being lifted, can aid robust robotic grasping. Previous methods paid equal attention to all regions of the tactile data matrix or all time-steps in the tactile sequence, which may include irrelevant or redundant information. In this paper, we propose to equip Convolutional Neural Networks with spatial-channel and temporal attention mechanisms (SCT attention CNN) to predict future grasp stability. To the best of our knowledge, this is the first time to use attention mechanisms for predicting grasp stability only relying on tactile information. We implement our experiments with 52 daily objects. Moreover, we compare different spatio-temporal models and attention mechanisms as an empirical study. We found a significant accuracy improvement of up to 5% when using SCT attention. We believe that attention mechanisms can also improve the performance of other tactile learning tasks in the future, such as slip detection and hardness perception.

This paper presents the design and evaluation of the low-cost capacitive proximity-force-fusion sensor "safe skin", which can measure simultaneously the proximity of humans as well as the contact force. It was designed such that the force and proximity sensing functions can work concurrently without interfering with each other. Moreover, active shielding, on-chip digitization and ground isolation are implemented for the sensor to minimize the influence from stray capacitance and electromagnetic interference (EMI) from the environment, which ensures that the sensor has a high system robustness for industrial applications. The prototype version has the capability of detecting a grounded human hand sized object from a distance of 400 mm. Moreover, forces in the range of 5 to 40 N can be measured, with 43.7% hysteresis and 6.7% nonlinearity. Due to its sensing characteristics, when used on a robot, the sensor could be used to ensure the safety of nearby humans in the future. The sensor could also potentially be used as an interface for human-robot interaction (HRI).

Synthetic fibre ropes have high tensile strength, a lower friction coefficient and are more flexible than steel ropes, and are therefore increasingly used in robotics. However, their characteristics are not well studied. In particular, previous work investigated the long-term behaviour only under static loading. In this paper, we investigate the elongation behaviour of synthetic fibre ropes under cyclic loading. In particular, we use ropes made from Dyneema DM20 (UHMWPE) and Zylon AS (PBO), which according to prior work have low creep. While Dyneema is more widely used, Zylon has higher tensile strength. We could show that under cyclic loading the Dyneema DM20 rope elongated more than 9% and kept on extending even after 500 cycles. Zylon exhibited a more stable and lower elongation of less than 3%.

The use of tactile information is one of the most important factors for achieving stable in-grasp manipulation. Especially with low-cost robotic hands that provide low-precision control, robust in-grasp manipulation is challenging. Abundant tactile information could provide the required feed-back to achieve reliable in-grasp manipulation also in such cases. In this research, soft distributed 3-axis skin sensors ("uSkin") and 6-axis F/T (force/torque) sensors were mounted on each fingertip of an Allegro Hand to provide rich tactile information. These sensors yielded 78 measurements for each fingertip (72 measurements from the uSkin and 6 measurements from the 6-axis F/T sensor). However, such high-dimensional tactile information can be difficult to process because of the complex contact states between the grasped object and the fingertips. Therefore, a convolutional neural network (CNN) was employed to process the tactile information. In this paper, we explored the importance of the different sensors for achieving in-grasp manipulation. Successful in-grasp manipulation with untrained daily objects was achieved when both 3-axis uSkin and 6-axis F/T information was provided and when the information was processed using a CNN.

Performing various in-hand manipulation tasks, without learning each individual task, would enable robots to act more versatile, while reducing the effort for training. However, in general it is difficult to achieve stable in-hand manipulation, because the contact state between the fingertips becomes difficult to model, especially for a robot hand with anthropomorphically shaped fingertips. Rich tactile feedback can aid the robust task execution, but on the other hand it is challenging to process high-dimensional tactile information. In the current paper we use two fingers of the Allegro hand, and each fingertip is anthropomorphically shaped and equipped not only with 6-axis force-torque (F/T) sensors, but also with uSkin tactile sensors, which provide 24 tri-axial measurements per fingertip. A convolutional neural network is used to process the high dimensional uSkin information, and a long short-term memory (LSTM) handles the time-series information. The network is trained to generate two different motions ("twist" and "push"). The desired motion is provided as a task-parameter to the network, with twist defined as -1 and push as +1. When values between -1 and +1 are used as the task parameter, the network is able to generate untrained motions in-between the two trained motions. Thereby, we can achieve multiple untrained manipulations, and can achieve robustness with high-dimensional tactile feedback.

Future robots are expected to share their workspace with humans. Controlling and limiting the forces that such robots exert on their environment is crucial. While force control can be achieved actively with the help of force sensing, passive mechanisms have no time delay in their response to external forces, and would therefore be preferable. Series clutch actuators can be used to achieve high levels of safety and backdrivability. This work presents the first implementation of a linear series clutch actuator. It can exert forces of more than 110N while weighing less than 2kg. Force controllability and safety are demonstrated. Static balancing, which is important for the application in a vertical joint, is also implemented. The power consumption is evaluated, and for a payload of 3kg and with the maximum speed of 94mm/s, the power consumed by the actuator is 11W. Overall, a practical implementation of a linear series clutch actuator is reported, which can be used for future collaborative robots.

Measuring the forces that humans exert with their fingers could have many potential applications, such as skill transfer from human experts to robots or monitoring humans. In this paper we introduce the "Exo-Glove" system, which can measure the joint angles and forces acting on the human finger without covering the skin that is in contact with the manipulated object. In particular, 3-axis sensors measure the deformation of the human skin on the sides of the finger to indirectly measure the 3-axis forces acting on the finger. To provide a frame of reference for the sensors, and to measure the joint angles of the human finger, an exoskeleton with remote center of motion (RCM) joints is used. Experiments showed that with the exoskeleton the quality of the force measurements can be improved.

Compelling research has recently shown that cancer is so heterogeneous that single research centres cannot produce enough data to fit prognostic and predictive models of sufficient accuracy. Data sharing in precision oncology is therefore of utmost importance. The Findable, Accessible, Interoperable and Reusable (FAIR) Data Principles have been developed to define good practices in data sharing. Motivated by the ambition of applying the FAIR Data Principles to our own clinical precision oncology implementations and research, we have performed a systematic literature review of potentially relevant initiatives. For clinical data, we suggest using the Genomic Data Commons model as a reference as it provides a field-tested and well-documented solution. Regarding classification of diagnosis, morphology and topography and drugs, we chose to follow the World Health Organization standards, i.e. ICD10, ICD-O-3 and Anatomical Therapeutic Chemical classifications, respectively. For the bioinformatics pipeline, the Genome Analysis ToolKit Best Practices using Docker containers offer a coherent solution and have therefore been selected. Regarding the naming of variants, we follow the Human Genome Variation Society's standard. For the IT infrastructure, we have built a centralized solution to participate in data sharing through federated solutions such as the Beacon Networks.

In past research, in-hand object manipulation for various sized and shaped objects has been achieved. However, the network had to be trained for each different motion. Training data takes time to acquire and increases the hardware load, thereby increasing the cost for training data. Four-fingered in-hand manipulation is especially difficult as a high number of joints need to be controlled in synchrony. This paper presents a method that reduces the required training data for in-hand manipulation with the idea of pretraining and mutual finger motions. The Allegro Hand is used with soft fingertips and integrated 6-axis F/T sensors to evaluate the proposed method. To make the network more versatile, the training data included objects of various sizes and shapes. When pretraining the network, one shot learning suffices to learn a new task; mutual finger motions can be exploited to use three-fingered pretraining data for four-fingered manipulation. Both data-sharing and weight-sharing were used and show similar results. Crucially, pretraining data from fingers with the same kinematic chain has to be used, showing the importance of morphology specific learning. Moreover, objects with untrained sizes and shapes could be manipulated.

Distributed tactile sensors on multi-fingered hands can provide high-dimensional information for grasping objects, but it is not clear how to optimally process such abundant tactile information. The current paper explores the possibility of using a morphology-specific convolutional neural network (MS-CNN). uSkin tactile sensors are mounted on an Allegro Hand, which provides 720 force measurements (15 patches of uSkin modules with 16 triaxial force sensors each) in addition to 16 joint angle measurements. Consecutive layers in the CNN get input from parts of one finger segment, one finger, and the whole hand. Since the sensors give 3D (x, y, z) vector tactile information, inputs with 3 channels (x, y and z) are used in the first layer, based on the idea of such inputs for RGB images from cameras. Overall, the layers are combined, resulting in the building of a tactile map based on the relative position of the tactile sensors on the hand. Seven different combination variations were evaluated, and an over-95% object recognition rate with 20 objects was achieved, even though only one random time instance from a repeated squeezing motion of an object in an unknown pose within the hand was used as input.

The sense of touch is often crucial for humans to perform manipulation tasks. Providing tactile feedback during teleoperation or for users of prosthetic devices would be beneficial. However, the representation of tactile information constitutes a major technical challenge, since the numerous and possibly multimodal sensor readings are massive compared to the available tactile display technology. We introduce an algorithm that deploys two stages of K-means clustering along and across tactile image frames that render tactile sensor information at each time instant. In this manner, the massive tactile information is adaptively compressed in real-time while preserving its physical meaning, thus, remains intuitive and direct. We experimentally verify and examine the characteristics of our algorithm by evaluating the original and compressed tactile data. The data was gathered during the active tactile exploration of several objects of daily living by an Allegro robot hand that was covered with 15 uSkin sensor modules providing 2403-axis force vector measurements at each time instant. Our novel algorithm is straight forward enough to be implemented into tactile feedback systems. Finally, our algorithm allows for the direct feedback of massive tactile sensor data for a broad variety of tactile sensors and tactile displays, thereby, enables the compressed yet intuitive representation of massive tactile sensor information for real-time applications.

An end-effector such as a gripper or multi-fingered hand is essential to enable robots to grasp and manipulate objects of various size and shape. Soft skin increases the grasp stability and can provide space for tactile sensors. However, covering the joints with skin is challenging, typically causing a considerable surface area of multi-segment robot fingers not to be covered by skin. This also creates the risk that objects get pinched in the joints when flexing the fingers. The current paper suggests using a remote center motion (RCM) mechanism to move the center of joint rotation to the surface of a thick skin layer. In particular, a 6-bar mechanism is used. Thereby, a thick soft skin layer with a continuous surface can be realized. Furthermore, adaptive joint coupling with linkages is implemented. In the current paper a 2-fingered gripper is realized, and objects of various size and shape are grasped (from thin paper to objects of 135 mm diameter). The current gripper was manufactured with 3D-printed material to enable rapid prototyping, therefore the payload was limited to only 1 kg for this version. Overall, this paper shows the feasibility of an RCM for a robot finger and discusses the benefits and limitations of such a mechanism.

This paper investigates tactile object recognition with relatively densely distributed force vector measurements and evaluates what kind of tactile information is beneficial for object recognition. The uSkin tactile sensors are embedded in an Allegro Hand, and provide 240 triaxial force vector measurements in total in all fingers. Active object sensing is used to gather time-series training and testing data. A simple feedforward, a recurrent, and a convolutional neural network are used for recognizing objects. Evaluations with different number of employed measurements, static vs. time series data and force vector vs. only normal force vector measurements show that the high-dimensional information provided by the sensors is indeed beneficial. An object recognition rate of up to 95% for 20 objects was achieved.

Typically, the range and sensitivity of force sensors are determined during production. However, to be able to do both delicate and high-force demanding work, adjustable force sensitivity would be beneficial. The current paper proposes such a sensor by implementing a planar electromagnet above a 3-axis magnetic sensor, separated by soft foam. Furthermore, the sensor has digital output with an integrated microcontroller. The magnetic field strength with varying currents is examined in simulation, and the field changes according to displacements are investigated both in simulation and with the actual sensor. A prototype 3-axis force sensor is implemented and the relationship between the magnetic field change and the corresponding applied force is also investigated. It could be shown that the sensitivity of the sensor to displacements, as well as force, can indeed be adjusted.

In this paper we present a low-cost and easy to fabricate 3-axis tactile sensor based on magnetic technology. The sensor consists in a small magnet immersed in a silicone body with an Hall-effect sensor placed below to detect changes in the magnetic field caused by displacements of the magnet, generated by an external force applied to the silicone body. The use of a 3-axis Hall-effect sensor allows to detect the three components of the force vector, and the proposed design assures high sensitivity, low hysteresis and good repeatability of the measurement: notably, the minimum sensed force is about 0.007N. All components are cheap and easy to retrieve and to assemble; the fabrication process is described in detail and it can be easily replicated by other researchers. Sensors with different geometries have been fabricated, calibrated and successfully integrated in the hand of the human-friendly robot Vizzy. In addition to the sensor characterization and validation, real world experiments of object manipulation are reported, showing proper detection of both normal and shear forces.

Human's ability to perceive intent plays a crucial role in achieving smooth and efficient navigation. At the present state, even with the state-of-the-art anthropomorphic robots, displaying human-like non-verbal communication (kinesics) is a challenging task. This poses a significant difficulty in performing legible navigation behavior for robots. In this paper, we look into light (turn indicator) and screen (arrow indicator) indicators as a means of overcoming the shortcomings of the robot's non-verbal communication abilities. Our results show a statistically significant improvement in perceived comfort, predictability, and performance with the use of light indicators.

A combined method of Doppler positioning and carrier-based hyperbolic positioning with a multi-channel GPS-pseudolite is proposed for indoor localization. This method uses carrier-phase output from a GPS/pseudolite receiver. The carrier-phase observable is precise but does not provide range information between the pseudolite and receiver antennas necessary for position calculation. This is because of the existence of carrier ambiguity. This problem can be solved by using the proposed combined method. In the present work, the positioning theory is established and experimentally evaluated with actual devices including a robot. The experimental result shows that a positioning accuracy of more than 10 cm is achievable.

The advantages of mechanical compliance have driven the development of devices using new smart materials. A new kind of magnetorheological piston based on a toroidal array of magnetorheological valves, has been previously tested to prove its feasibility. However, being an initial prototype its potential was still limited by its complex design, and low output force. This study presents the revisions done to the design with several improvements targeting key performance parameters. An improved annular piston design is also introduced as comparison with conventional devices. The toroidal and annular piston head prototypes are built and tested, and their force performance compared with the previous iteration. The experimental results show an overall performance improvement of the toroidal assembly. However, the force model used in the study still fails to accurately predict the magnetic flux at the gaps of the piston head. This deviation is later verify and corrected using a FEM analysis. The force performance of the new toroidal assembly is on par with the commonplace annular design. It also displays a more linear behaviour, at the expense of lower energy efficiency. Finally, it also shows potential for a greater degree of customisation to meet different system requirements.

In-hand manipulation is often needed to accomplish a practical task after grasping an object. In-hand manipulation of variously sized and shaped objects in multi-fingered hands without dropping the object is challenging. In this paper we suggest a combined strategy of force control and passive adaptation through soft fingertips with simple interpolation control to achieve in-hand manipulation between various postures and with various objects. While passive compliance can be achieved in numerous ways, this paper uses soft skin, as it does not require complex mechanisms and was easy to integrate in the robot hand (Allegro hand). Softness has proven to significantly ease object grasping, and the current paper shows the importance of softness also for in-hand manipulation. In particular, the simple interpolation strategy between various postures is successful when combined with soft fingertips, with or without force control, but fails with hard fingertips. Objects of varying size, shape and hardness were reliably manipulated. While the soft fingertips enabled good results in our experiments, a sufficiently precise definition of the postures and object size was required. When combining the interpolation control with a force control strategy, bigger errors in defining the posture and object size are possible, without deforming or dropping the object, and the resultant force is lower. As a result, we achieved robust in-hand manipulation between various postures and with objects of different size, shape and hardness.

Moving objects within the hand is challenging, especially if the objects are of various shape and size. In this paper we use machine learning to learn in-hand manipulation of such various sized and shaped objects. The TWENDY-ONE hand is used, which has various properties that makes it well suited for in-hand manipulation: a high number of actuated joints, passive degrees of freedom and soft skin, six-axis force/torque (F/T) sensors in each fingertip, and distributed tactile sensors in the skin. A dataglove is used to gather training samples for teaching the required behavior. The object size information is extracted from the initial grasping posture. After training a neural network, the robot is able to manipulate objects of untrained sizes and shape. The results show the importance of size and tactile information. Compared to interpolation control, the adaptability for the initial posture gap could be greatly extended. Final results show that with deep learning the number of required training sets can be drastically reduced.

As robots progressively continue to enter human lives, it becomes important for robots to navigate safely and efficiently in crowded environments. In fact, efficient navigation in crowded areas is an important prerequisite for successful coexistence between humans and robots. In this paper, we explore an unconventional idea wherein a robot tries to achieve a more efficient navigation by influencing an obstructing human to move away by means of contact. First, preliminary human reaction experiments were conducted wherein we established that we can successfully induce a human to move in a desired direction. Following this result, we have proposed a novel motion planning approach which considers inducement by contact. The system is then verified through simulation and real experiments. The results show us that the proposed method can be utilized for safer and more efficient navigation in a crowded, but relatively static environment.

BACKGROUND: Due to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other. Current methods are highly adapted to a certain biological specimen or a specific microscope. They do not ensure similarly accurate segmentation performance, i.e. their robustness for different datasets is not guaranteed. Hence, these methods require elaborate adjustments to each dataset. RESULTS: We present an advanced three-dimensional cell nuclei segmentation algorithm that is accurate and robust. Our approach combines local adaptive pre-processing with decomposition based on Lines-of-Sight (LoS) to separate apparently touching cell nuclei into approximately convex parts. We demonstrate the superior performance of our algorithm using data from different specimens recorded with different microscopes. The three-dimensional images were recorded with confocal and light sheet-based fluorescence microscopes. The specimens are an early mouse embryo and two different cellular spheroids. We compared the segmentation accuracy of our algorithm with ground truth data for the test images and results from state-of-the-art methods. The analysis shows that our method is accurate throughout all test datasets (mean F-measure: 91%) whereas the other methods each failed for at least one dataset (F-measure≤69%). Furthermore, nuclei volume measurements are improved for LoS decomposition. The state-of-the-art methods required laborious adjustments of parameter values to achieve these results. Our LoS algorithm did not require parameter value adjustments. The accurate performance was achieved with one fixed set of parameter values. CONCLUSION: We developed a novel and fully automated three-dimensional cell nuclei segmentation method incorporating LoS decomposition. LoS are easily accessible features that ensure correct splitting of apparently touching cell nuclei independent of their shape, size or intensity. Our method showed superior performance compared to state-of-the-art methods, performing accurately for a variety of test images. Hence, our LoS approach can be readily applied to quantitative evaluation in drug testing, developmental and cell biology.

BACKGROUND: In vitro generated dose-response curves of human cancer cell lines are widely used to develop new therapeutics. The curves are summarised by simplified statistics that ignore the conventionally used dose-response curves' dependency on drug exposure time and growth kinetics. This may lead to suboptimal exploitation of data and biased conclusions on the potential of the drug in question. Therefore we set out to improve the dose-response assessments by eliminating the impact of time dependency. RESULTS: First, a mathematical model for drug induced cell growth inhibition was formulated and used to derive novel dose-response curves and improved summary statistics that are independent of time under the proposed model. Next, a statistical analysis workflow for estimating the improved statistics was suggested consisting of 1) nonlinear regression models for estimation of cell counts and doubling times, 2) isotonic regression for modelling the suggested dose-response curves, and 3) resampling based method for assessing variation of the novel summary statistics. We document that conventionally used summary statistics for dose-response experiments depend on time so that fast growing cell lines compared to slowly growing ones are considered overly sensitive. The adequacy of the mathematical model is tested for doxorubicin and found to fit real data to an acceptable degree. Dose-response data from the NCI60 drug screen were used to illustrate the time dependency and demonstrate an adjustment correcting for it. The applicability of the workflow was illustrated by simulation and application on a doxorubicin growth inhibition screen. The simulations show that under the proposed mathematical model the suggested statistical workflow results in unbiased estimates of the time independent summary statistics. Variance estimates of the novel summary statistics are used to conclude that the doxorubicin screen covers a significant diverse range of responses ensuring it is useful for biological interpretations. CONCLUSION: Time independent summary statistics may aid the understanding of drugs' action mechanism on tumour cells and potentially renew previous drug sensitivity evaluation studies.

Handling objects with a single hand without dropping the object is challenging for a robot. A possible way to aid the motion planning is the prediction of the sensory results of different motions. Sequences of different movements can be performed as an offline simulation, and using the predicted sensory results, it can be evaluated whether the desired goal is achieved. In particular, the task in this paper is to roll a sphere between the fingertips of the dexterous hand of the humanoid robot TWENDY-ONE. First, a forward model for the prediction of the touch state resulting from the in-hand manipulation is developed. As it is difficult to create such a model analytically, the model is obtained through machine learning. To get real world training data, a dataglove is used to control the robot in a master-slave way. The learned model was able to accurately predict the course of the touch state while performing successful and unsuccessful in-hand manipulations. In a second step, it is shown that this simulated sequence of sensor states can be used as input for a stability assessment model. This model can accurately predict whether a grasp is stable or whether it results in dropping the object. In a final step, a more powerful grasp stability evaluator is introduced, which works for our task regardless of the sphere diameter.

Even though the sense of touch is crucial for humans, most humanoid robots lack tactile sensing. While a large number of sensing technologies exist, it is not trivial to incorporate them into a robot. We have developed a compliant “skin” for humanoids that integrates a distributed pressure sensor based on capacitive technology. The skin is modular and can be deployed on nonflat surfaces. Each module scans locally a limited number of tactile-sensing elements and sends the data through a serial bus. This is a critical advantage as it reduces the number of wires. The resulting system is compact and has been successfully integrated into three different humanoid robots. We have performed tests that show that the sensor has favorable characteristics and implemented algorithms to compensate the hysteresis and drift of the sensor. Experiments with the humanoid robot iCub prove that the sensors can be used to grasp unmodeled, fragile objects.

In order to successfully perform object manipulation, humanoid robots must be equipped with tactile sensors. However, the limited space that is available in robotic fingers imposes severe design constraints. In [1] we presented a small prototype fingertip which incorporates a capacitive pressure system. This paper shows an improved version, which has been integrated on the hand of the humanoid robot iCub. The fingertip is 14.5 mm long and 13 mm wide. The capacitive pressure sensor system has 12 sensitive zones and includes the electronics to send the 12 measurements over a serial bus with only 4 wires. Each synthetic fingertip is shaped approximately like a human fingertip. Furthermore, an integral part of the capacitive sensor is soft silicone foam, and therefore the fingertip is compliant. We describe the structure of the fingertip, their integration on the humanoid robot iCub and present test results to show the characteristics of the sensor.

The sense of touch is of major importance for object handling. Nevertheless, adequate cutaneous sensors for humanoid robot hands are still missing. Designing such sensors is challenging, because they should not only give reliable measurements and integrate many sensing points into little space, but they should also be compliant and should not obstruct the other functions of the robot. This paper presents a capacitive pressure sensor system with 108 sensitive zones for the hands of the humanoid robot iCub. In particular, the palm has 48 taxels and each of the five fingertips has 12 taxels. The size and the shape of the hand are similar to that of a human child. When designing the sensors, we paid special attention to the integration on the robot. Also the ease and speed of production was an important design factor. Furthermore, the sensor incorporates silicone foam and is therefore compliant. We show the working principle of the sensor, how it has been integrated into the hands, and describe experiments that have been performed to show the characteristics of the sensor.