Mohsen Kaboli

BMW Group & Eindhoven University of Technology(TU/e)

Interactive exploration of the unknown physical properties of objects such as stiffness, mass, center of mass, friction coefficient, and shape is crucial for autonomous robotic systems operating continuously in unstructured environments. Precise identification of these properties is essential to manipulate objects in a stable and controlled way, an...

An essential problem in robotic systems that are to be deployed in unstructured environments is the accurate and autonomous perception of the shapes of previously unseen objects. Existing methods for shape estimation or reconstruction have leveraged either visual or tactile interactive exploration techniques or have relied on comprehensive visual o...

Motivated by the growing interest in enhancing intuitive physical Human-Machine Interaction (HRI/HVI), this study aims to propose a robust tactile hand gesture recognition system. We performed a comprehensive evaluation of different hand gesture recognition approaches for a large area tactile sensing interface (touch interface) constructed from con...

Autonomously exploring the unknown physical properties of novel objects such as stiffness, mass, center of mass, friction coefficient, and shape is crucial for autonomous robotic systems operating continuously in unstructured environments. We introduce a novel visuo-tactile based predictive cross-modal perception framework where initial visual obse...

For robotic systems to interact with objects in dynamic environments, it is essential to perceive the physical properties of the objects such as shape, friction coefficient, mass, center of mass, and inertia. This not only eases selecting manipulation action but also ensures the task is performed as desired. However, estimating the physical propert...

Accurate shape reconstruction of transparent objects is a challenging task due to their non-Lambertian surfaces and yet necessary for robots for accurate pose perception and safe manipulation. As vision-based sensing can produce erroneous measurements for transparent objects, the tactile modality is not sensitive to object transparency and can be u...

Point cloud registration is a fundamental and challenging problem for autonomous robots interacting in unstructured environments for applications such as object pose estimation, simultaneous localization and mapping, robot-sensor calibration, and so on. In global correspondence-based point cloud registration, data association is a highly brittle ta...

We propose a novel framework for deep active visuotactile cross-modal robotic object recognition. Our deep neural network (termed xAVTNet) is trained solely with dense visual point cloud data and tested on sparse point clouds acquired from tactile sensors. We propose a novel unsupervised domain adaptation loss function termed VTLoss for minimising...

We propose for the first time, a novel deep active visuotactile cross-modal full-fledged framework for object recognition by autonomous robotic systems. Our proposed network xAVTNet is actively trained with labelled point clouds from a vision sensor with one robot and tested with an active tactile perception strategy to recognise objects never touc...

Neuromorphic hardware enables fast and power-efficient neural network-based artificial intelligence that is well suited to solving robotic tasks. Neuromorphic algorithms can be further developed following neural computing principles and neural network architectures inspired by biological neural systems. In this Viewpoint, we provide an overview of...

Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most o...

Accurate object pose estimation using multi-modal perception such as visual and tactile sensing have been used for autonomous robotic manipulators in literature. Due to variation in density of visual and tactile data, we previously proposed a novel probabilistic Bayesian filter-based approach termed translation-invariant Quaternion filter (TIQF) fo...

Three-dimensional (3D) object recognition is crucial for intelligent autonomous agents such as autonomous vehicles and robots alike to operate effectively in unstructured environments. Most state-of-art approaches rely on relatively dense point clouds and performance drops significantly for sparse point clouds. Unsupervised domain adaption allows t...

This work presents a novel active visuo-tactile based framework for robotic systems to accurately estimate pose of objects in dense cluttered environments. The scene representation is derived using a novel declutter graph (DG) which describes the relationship among objects in the scene for decluttering by leveraging semantic segmentation and grasp...

This work presents a novel active visuo-tactile based framework for robotic systems to accurately estimate pose of objects in dense cluttered environments. The scene representation is derived using a novel declutter graph (DG) which describes the relationship among objects in the scene for decluttering by leveraging semantic segmentation and grasp...

Adrenaline and hydrogen peroxide (H2O2) have neuromodulatory functions in the brain, and peroxide is also formed during reaction of adrenaline. Considerable interest exists in developing electrochemical sensors that can detect their levels in vivo due to their important biochemical roles. Challenges associated with the electrochemical detection of...

With rapid advances in the field of autonomous vehicles (AVs), the ways in which human–vehicle interaction (HVI) will take place inside the vehicle have attracted major interest and, as a result, intelligent interiors are being explored to improve the user experience, acceptance, and trust. This is also fueled by parallel research in areas such as...

Accurate object pose estimation using multi-modal perception such as visual and tactile sensing have been used for autonomous robotic manipulators in literature. Due to variation in density of visual and tactile data, a novel probabilistic Bayesian filter-based approach termed translation-invariant Quaternion filter (TIQF) is proposed for pose esti...

Adrenaline and hydrogen peroxide have neuromodulatory functions in the brain.Considerable interest exists in developing electrochemical sensors that can detect their levels in vivo due to their important biochemical roles. Challenges associated with electrochemical detection of hydrogen peroxide and adrenaline are that the oxidation of these molecu...

With rapid advances in the field of autonomous vehicles (AVs), the ways in which human-vehicle interaction (HVI) will take place inside the vehicle has attracted major interest and, as a result, intelligent interiors are being explored to improve the user experience, acceptance, and trust. This is also fueled by parallel research in areas such as p...

This paper proposes a novel active visuo-tactile based methodology wherein the accurate estimation of the time-invariant SE(3) pose of objects is considered for autonomous robotic manipulators. The robot equipped with tactile sensors on the gripper is guided by a vision estimate to actively explore and localize the objects in the unknown workspace....

This paper proposes a novel active visuo-tactile based methodology wherein the accurate estimation of the time-invariant SE(3) pose of objects is considered for autonomous robotic manipulators. The robot equipped with tactile sensors on the gripper is guided by a vision estimate to actively explore and localize the objects in the unknown workspace....

Carbon nanotubes (CNTs) have received considerable attention as sensors because of their nano-size and superior electron transfer kinetics. These properties make them an attractive candidate as electrode material for neurotransmitter detection using Fast Scan Cyclic Voltammetry (FSCV). Electrochemical detection of adrenaline in the brain is challen...

Tactile sensing is a key sensor modality for robots interacting with their surroundings. These sensors provide a rich and diverse set of data signals that contain detailed information collected from contacts between the robot and its environment. The data are however not limited to individual contacts and can be used to extract a wide range of info...

Car manufacturers are facing the challenge of defining suitable sensor setups that cover all requirements for the particular SAE level of automated driving. Besides the sensors' performance and surround-view coverage, other factors like vehicle integration, costs and design aspects need to be taken into account. Additionally, a redundant sensor arr...

Generating realistic motion in a motion-based (dynamic) driving simulator is challenging due to the limited workspace of the motion system of the simulator compared to the motion range of the simulated vehicle. Motion Cueing Algorithms (MCAs) render accelerations by controlling the motion system of the simulators to provide the driver with a realis...

The tasks of exploring unknown workspaces and recognizing objects based on their physical properties are challenging for autonomous robots. In this paper, we present strategies solely based on tactile information to enable robots to accomplish such tasks. (1) An active exploration approach for the robot to explore unknown workspaces; (2) an active...

A Review of Visual and Tactile Transfer Learning Methods

A full-day workshop on October 1 st , 2018 at IROS2018 in Madrid, Spain. Important Dates Paper submission guidelines We welcome submissions regarding any robotics application where tactile sensing modalities are used. As we aim to encourage meaningful discussion in the tactile perception and learning domain, work that is unpublished, recently publi...

We use sense of touch to actively explore our environment and objects through their various physical properties such as surface texture, stiffness, shape, and thermal conductivity. We intelligently re-use our previously acquired tactile knowledge to actively learn about new objects. Our prior tactile knowledge, or past tactile experience, helps us...

In this paper, we propose a set of novel tactile descriptors to enable robotic systems to extract robust tactile information during tactile object explorations, regardless of the number of the tactile sensors, sensing technologies, type of exploratory movements, and duration of the objects’ surface exploration. The performance and robustness of the...

Reusing the tactile knowledge of some previously-explored objects (prior objects) helps us to easily recognize the tactual properties of new objects. In this paper, we enable a robotic arm equipped with multi-modal artificial skin, like humans, to actively transfer the prior tactile exploratory action experiences when it learns the detailed physica...

Video to A Tactile-Based Framework for Active Object Learning and Discrimination using Multimodal Robotic Skin

Sense of touch plays an important role in our daily lives from grasping and manipulating to identifying and interacting with objects. For robotic systems that interact with dynamic environments, it is crucial to recognize objects via their physical properties. However, this is difficult to achieve even with advanced vision techniques due to occlusi...

Reusing the tactile knowledge of some previously explored objects helps us to easily recognize the tactual properties of new objects. In this paper, we enable a robotic arm equipped with multi-modal artificial skin, like humans, to actively transfer the prior tactile exploratory action experiences when it learns the detailed physical properties of...

In this paper, we propose a probabilistic active tactile transfer learning (ATTL) method to enable robotic systems to exploit their prior tactile knowledge when discriminating among objects via their physical properties (surface texture, stiffness, and thermal conductivity). Using the proposed method, the robot autonomously selects and exploits its...

In robotic tasks, objects can be discriminated according to their physical properties, such as color, shape, stiffness, and surface textures, which can be detected by using either cameras or tactile sensors. However, objects can hardly be discriminated if their external properties are identical. In this case, internal properties of the objects shou...

In this paper, we propose a probabilistic active tactile transfer learning (ATTL) method to enable robotic systems to exploit their prior tactile knowledge when discriminating among objects via their physical properties (surface texture, stiffness, and thermal conductivity). Using the proposed method, the robot autonomously selects and exploits its...

In this paper, we propose a complete probabilistic tactile-based framework to enable robots to autonomously explore unknown workspaces and recognize objects based on their physical properties. Our framework consists of three components: (1) an active pre-touch strategy to efficiently explore unknown workspaces; (2) an active touch learning method t...

In this paper, we present a novel approach for touch modality identification via tactile sensing on a humanoid. In this respect, we equipped a NAO humanoid with whole upper body coverage of multi-modal artificial skin. We propose a set of biologically inspired feature descriptors to provide robust and abstract tactile information for use in touch c...

Flexible electronics has huge potential to bring revolution in robotics and prosthetics as well as to bring about the next big evolution in electronics industry. In robotics and related applications, it is expected to revolutionise the way with which machines interact with humans, real-world objects and the environment. For example, the conformable...

This paper, for the first time, proposes a solution for the problem of in-hand object recognition via surface textures. In this study, a robotic hand with an artificial skin on the fingertips was employed to explore the texture properties of various objects. This was conducted via the small sliding movements of the fingertips of the robot over the...

This paper presents new methods for the recognition and categorization of object properties such as surface texture, weight, and compliance using a multi-modal artificial skin mounted on both arms of a humanoid. In addition, it introduces two novel feature descriptors, which are useful for providing high-level information to learning algorithms. Th...

An aperture-coupled dual-polarized wideband microstrip antenna with high port-to-port isolation in C-Band is presented in this paper. The proposed antenna contains reflector, substrate, patch and director. By means of four shorting pins the bandwidth increases noticeably. It has 50% bandwidth of -10dB return loss at the 5.25GHz center frequency, an...

Open ended learning is a dynamic process based on the continuous analysis of new data, guided by past experience. On one side it is helpful to take advantage of prior knowledge when only few information on a new task is available (transfer learning). On the other, it is important to continuously update an existing model so to exploit the new incomi...

Antennas with dual linear slant polarization are necessary for modern personal communication base station applications. X-Polar antenna stands for an antenna with dual linear slant polarization. The most significant feature of the proposed antenna is high isolation between two different polarizations. The structure used here includes a reflector, a...