Amir Ghalamzan

University of Surrey · Comp. Sci. & Elect. Engineering

Ensuring a stable grasp during manipulative movements is crucial for robotic applications. While grip force has been the primary means of slip control, our human study revealed that trajectory modulation is also an effective slip control policy during pick-and-place tasks. Motivated by these findings, we developed and compared a slip control policy...

This letter introduces a novel Soft Acoustic Curvature (SAC) sensor. SAC incorporates integrated audio components and features an acoustic channel within a flexible structure. A reference acoustic wave, generated by a speaker at one end of the channel, propagates and is received by a microphone at the other channel's end. Our previous study reveale...

Active perception enables robots to dynamically gather information by adjusting their viewpoints, a crucial capability for interacting with complex, partially observable environments. In this paper, we present AP-VLM, a novel framework that combines active perception with a Vision-Language Model (VLM) to guide robotic exploration and answer semanti...

This paper introduces a novel Soft Acoustic Curvature (SAC) sensor. SAC incorporates integrated audio components and features an acoustic channel within a flexible structure. A reference acoustic wave, generated by a speaker at one end of the channel, propagates and is received by a microphone at the other channel's end. Our previous study revealed...

Selective harvesting by autonomous robots will be a critical enabling technology for future farming. Increases in inflation and shortages of skilled labor are driving factors that can help encourage user acceptability of robotic harvesting. For example, robotic strawberry harvesting requires real‐time high‐precision fruit localization, three‐dimens...

Acoustic Soft Tactile (AST) skin is a novel sensing technology which derives tactile information from the modulation of acoustic waves travelling through the skin's embedded acoustic channels. A generalisable data-driven calibration model maps the acoustic modulations to the corresponding tactile information in the form of contact forces with their...

Selective harvesting by autonomous robots will be a critical enabling technology for future farming. Increases in inflation and shortages of skilled labour are driving factors that can help encourage user acceptability of robotic harvesting. For example, robotic strawberry harvesting requires real-time high-precision fruit localisation, 3D mapping...

This paper introduces an innovative, cost-effective design of Acoustic Soft Tactile (AST) Skin, significantly enhancing the accuracy of 2-D tactile feature estimation. Beyond precision, this technology expands the scope of captured tac-tile features, encompassing contact geometry characteristics. By harnessing acoustic energy propagated through aco...

Tactile sensing plays a pivotal role in achieving precise physical manipulation tasks and extracting vital physical features. This comprehensive review paper presents an in-depth overview of the growing research on tactile-sensing technologies, encompassing state-of-the-art techniques, future prospects, and current limitations. The paper focuses on...

This paper provides an overview of the current state‐of‐the‐art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labor costs, and minimize wastage by selectively harvesting only ripe fruits and vegetables. The paper discusses t...

Challenges in strawberry picking made selective harvesting robotic technology very demanding. However, the selective harvesting of strawberries is a complicated robotic task forming a few scientific research questions. Most available solutions only deal with a specific picking scenario, for example, picking only a single variety of fruit in isolati...

The growing population, demand for healthy diets, and shift towards plant-based protein diets are increasing pressure on food production and land use. However, current agricultural practices jeopardize soil health and biodiversity, which threatens future ecosystems and food production. Precision Agriculture (PA) offers a solution to these challenge...

In this paper, we explore the impact of adding tactile sensation to video prediction models for physical robot interactions. Predicting the impact of robotic actions on the environment is a fundamental challenge in robotics. Current methods leverage visual and robot action data to generate video predictions over a given time period, which can then...

This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discuss...

Teleoperating robotic manipulators can be complicated and cognitively demanding for the human operator. Despite these difficulties, teleoperated robotic systems are still popular in several industrial applications, e.g., remote handling of hazardous material. In this context, we present a novel haptic shared control method for minimising the manipu...

In this paper, we present a grammar-based natural language framework for robot programming, specifically for pick-and-place tasks. Our approach uses a custom dictionary of action words, designed to store together words that share meaning, allowing for easy expansion of the vocabulary by adding more action words from a lexical database. We validate...

Acoustic Soft Tactile (AST) skin is a novel soft-flexible, low-cost sensor that can measure static normal forces and their contact location. This letter presents the design, fabrication, and experimental evaluation of AST skin. The proposed AST skin has some Acoustic channels(s) (ACs) arranged in parallel below the sensing surface. A reference acou...

This paper introduces a novel approach to address the problem of Physical Robot Interaction (PRI) during robot pushing tasks. The approach uses a data-driven forward model based on tactile predictions to inform the controller about potential future movements of the object being pushed, such as a strawberry stem, using a robot tactile finger. The mo...

Challenges in strawberry picking made selective harvesting robotic technology demanding. However, selective harvesting of strawberries is complicated forming a few scientific research questions. Most available solutions only deal with a specific picking scenario, e.g., picking only a single variety of fruit in isolation. Nonetheless, most economica...

This paper presents a novel control approach to dealing with object slip during robotic manipulative movements. Slip is a major cause of failure in many robotic grasping and manipulation tasks. Existing works increase grip force to avoid/control slip. However, this may not be feasible when (i) the robot cannot increase the gripping force -- the max...

To better optimise the global food supply chain, robotic solutions are needed to automate tasks currently completed by humans. Namely, phenotyping, quality analysis and harvesting are all open problems in the field of agricultural robotics. Robotic perception is a key challenge for autonomous solutions to such problems as scene understanding and ob...

This paper presents a novel probabilistic approach to deep robot learning from demonstrations (LfD). Deep movement primitives (DMPs) are deterministic LfD model that maps visual information directly into a robot trajectory. This paper extends DMPs and presents a deep probabilistic model that maps the visual information into a distribution of effect...

Robotic harvesting of strawberries has gained much interest in the recent past. Although there are many innovations, they haven't yet reached a level that is comparable to an expert human picker. The end effector unit plays a major role in defining the efficiency of such a robotic harvesting system. Even though there are reports on various end effe...

Breast cancer is the most common type of cancer worldwide. A robotic system performing autonomous breast palpation can make a significant impact on the related health sector worldwide. However, robot programming for breast palpating with different geometries is very complex and unsolved. Robot learning from demonstrations (LfD) reduces the programm...

Tactile predictive models can be useful across several robotic manipulation tasks, e.g. robotic pushing, robotic grasping, slip avoidance, and in-hand manipulation. However, available tactile prediction models are mostly studied for image-based tactile sensors and there is no comparison study indicating the best performing models. In this paper, we...

Tactile predictive models can be useful across several robotic manipulation tasks, e.g. robotic pushing, robotic grasping, slip avoidance, and in-hand manipulation. However, available tactile prediction models are mostly studied for image-based tactile sensors and there is no comparison study indicating the best performing models. In this paper, we...

Breast cancer is the most common type of cancer worldwide. A robotic system performing autonomous breast palpation can make a significant impact on the related health sector worldwide. However, robot programming for breast palpating with different geometries is very complex and unsolved. Robot learning from demonstrations (LfD) reduces the programm...

In 2020, breast cancer affected around two million people worldwide. Early cancer detection is, therefore, needed to save many lives and reduce treatment costs. Nowadays, mammography and self- palpation are the most popular monitoring methods. The high number of cases and the difficulty of correct self-diagnosis has prompted this research work to d...

Tactile sensing provides essential information about the state of the world for the robotic system to perform a successful and robust manipulation task. Integrating and exploiting tactile sensation enables the robotic systems to perform wider variety of manipulation tasks in unstructured environments relative to pure vision based systems. While sli...

Robots learning a new manipulation task from a small amount of demonstrations are increasingly demanded in different workspaces. A classifier model assessing the quality of actions can predict the successful completion of a task, which can be used by intelligent agents for action-selection. This paper presents a novel classifier that learns to clas...

Like other robot learning from demonstration (LfD) approaches, deep-LfD builds a task model from sample demonstrations. However, unlike conventional LfD, the deep-LfD model learns the relation between high dimensional visual sensory information and robot trajectory/path. This paper presents a dataset of successful needle insertion by da Vinci Resea...

Automating a robotic task, e.g., robotic suturing can be very complex and time-consuming. Learning a task model to autonomously perform the task is invaluable making the technology, robotic surgery, accessible for a wider community. The data of robotic surgery can be easily logged where the collected data can be used to learn task models. This will...

The volume LNAI 13054 constitutes the refereed proceedings of the 22th Annual Conference Towards Autonomous Robotic Systems, TAROS 2021, held in Lincoln, UK, in September 2021.* The 45 full papers were carefully reviewed and selected from 66 submissions. Organized in the topical sections "Algorithms" and "Systems", they discuss significant findings...

Many robotic tasks are still teleoperated since automating them is very time consuming and expensive. Robot Learning from Demonstrations (RLfD) can reduce programming time and cost. However, conventional RLfD approaches are not directly applicable to many robotic tasks, e.g. robotic suturing with minimally invasive robots, as they require a time-co...

Estimating the inertial properties of an object can make robotic manipulations more efficient, especially in extreme environments. This paper presents a novel method of estimating the 2D inertial parameters of an object, by having a robot applying a push on it. We draw inspiration from previous analyses on quasi-static pushing mechanics, and introd...

We propose a haptic-guided shared control system that provides an operator with force cues during reach-to-grasp phase of tele-manipulation. The force cues inform the operator of grasping configuration which allows collision-free autonomous post-grasp movements. Previous studies showed haptic guided shared control significantly reduces the complexi...

Robotic technology is increasingly considered the major mean for fruit picking. However, picking fruits in a dense cluster imposes a challenging research question in terms of motion/path planning as conventional planning approaches may not find collision-free movements for the robot to reach-and-pick a ripe fruit within a dense cluster. In such cas...

Robotic technology is increasingly considered the major mean for fruit picking. However, picking fruits in a dense cluster imposes a challenging research question in terms of motion/path planning as conventional planning approaches may not find collision-free movements for the robot to reach-and-pick a ripe fruit within a dense cluster. In such cas...

This letter presents a method for constrained motion planning from vision, which enables a robot to move its end-effector over an observed surface, given start and destination points. The robot has no prior knowledge of the surface shape, but observes it from a noisy point cloud. We consider the multi-objective optimisation problem of finding robot...

In this study the problem of fitting shape primitives to point cloud scenes was tackled as a parameter optimisation procedure, and solved using the popular bees algorithm. Tested on three sets of clean and differently blurred point cloud models, the bees algorithm obtained performances comparable to those obtained using the state-of-the-art random...

This paper presents a method for constrained motion planning from vision, which enables a robot to move its end-effector over an observed surface, given start and destination points. The robot has no prior knowledge of the surface shape, but observes it from a noisy point-cloud camera. We consider the multi-objective optimisation problem of finding...

During suturing tasks performed with minimally invasive surgical robots, configuration singularities and joint limits often force surgeons to interrupt the task and re-grasp the needle using dual-arm movements. This yields an increased operator's cognitive load, time-to-completion and performance degradation. In this paper, we propose a haptic-guid...

In this paper, we propose a new feature selection method called kernel fisher discriminant analysis and regression learning based algorithm for unsupervised feature selection. The existing feature selection methods are based on either manifold learning or discriminative techniques, each of which has some shortcomings. Although some studies show the...

Consider the task of grasping the handle of a door, and then pushing it until the door opens. These two fundamental robotics problems (selecting secure grasps of a hand on an object, e.g. the door handle, and planning collision-free trajectories of a robot arm that will move that object along a desired path) have predominantly been studied separate...

Appeared in International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 2018)

In this article, we study the problem of selecting a grasping pose on the surface of an object to be manipulated by considering three post-grasp objectives. These objectives include (i) kinematic manipulation capability, (ii) torque effort \cite{mavrakis2016analysis} and (iii) impact force in case of a collision during post-grasp manipulative actio...

In this paper, we present an approach to the problem of Robot Learning from Demonstration (RLfD) in a dynamic environment, i.e. an environment whose state changes throughout the course of performing a task. RLfD mostly has been successfully exploited only in non-varying environments to reduce the programming time and cost, e.g. fixed manufacturing...

Recent high-performance clustering methods process all pixels when segmenting an image, which results in a very large time complexity of these algorithms. Additionally, the performance of such algorithms can be severely affected by noise when dealing with highly polluted images. To address these problems, we propose a new unsupervised algorithm for...

Community structure has become one of the central studies of the topological structure of complex networks in the past decades. Although many advanced approaches have been proposed to identify community structure, those state-of-the-art methods still lack efficiency in terms of a balance between stability, accuracy and computation time. Here, we pr...

This paper addresses the problem of mixed initiative, shared control for master-slave grasping and manipulation. We propose a novel system, in which an autonomous agent assists a human in teleoperating a remote slave arm/gripper, using a haptic master device. Our system is designed to exploit the human operator's expertise in selecting stable grasp...

This paper addresses the problem of selecting from a choice of possible grasps, so that impact forces will be minimised if a collision occurs while the robot is moving the grasped object along a post-grasp trajectory. Such considerations are important for safety in human-robot interaction, where even a certified "human-safe" (e.g. compliant) arm ma...

Detecting changed regions between two given synthetic aperture radar images is very important to monitor change of landscapes, change of ecosystem and so on. This can be formulated as a classification problem and addressed by learning a classifier, traditional machine learning classification methods very easily stick to local optima which can be ca...