This paper presents a robot system for manipulation and picking of tomato fruits guided by computer vision. A CNN algorithm trained on a custom image dataset is used for fruit detection and the depth camera enables position estimation. The robot manipulator is equipped with a commercial soft gripper for picking fragile objects. Three planning procedures have been proposed to successfully reach and grasp the tomatoes. Experiments on simulated crops have compared the effectiveness of the proposed procedures.

In Smart Farming, geospatial machine learning has the potential to enable data-driven crop management across large areas for optimization of resource consumption. In this work, we describe an edge-to-cloud system designed for soil-plant moisture detection and classification in a realistic scenario, where ground truth data are scarce and inaccurate and yet district-level irrigation water provisioning must rely on these data. Global data sources are scouted and integrated with purposely-designed edge and fog components to support soil and crop data collection, data filtering and validation, and moisture assessment. Exploiting suitable combinations of large scale data (retrieved in public satellite data hubs) and specific but inaccurate manual data, sufficiently accurate moisture assessments are obtained by machine learning algorithms, thereby avoiding expensive manual labeling of large data collections as well as human inspection of individual plots. The resulting accuracy (in the order of 80%) is comparable with that obtained by human-laboured approaches.

Current smart farming solutions are either local or cloud-based. The first approach limits the types and scale of available data processing services. In the second approach, the physical and virtual distance between sensors/actuators and the cloud has a significant impact on latency, which leads to quality of service degradation. Another major issue of the all-in-cloud approach is related to data privacy, which is not yet completely regulated. In this paper, we define an N-tier fog architecture and service placement scenarios that concern realistic smart farming applications with data privacy constraints. Then, we introduce a service placement policy that leverages the N-tier fog architecture to cope with availability, deadline satisfaction and data privacy objectives. Finally, we demonstrate the viability of the proposed approach by means of a simulation analysis.

The persuasive potential of games and their use in political propaganda and raising social awareness are well-established components of the game studies discourse, and the literature around persuasive games highlights satire among the expressive tones of several games. Despite this, what persuasive games’ literature still lacks is a complete and stand-alone defining account of satire in games, which could be useful in analyzing both analog and digital games used for satirical purposes.

To recognize satire, the audience must be aware of the context and the satirical intent of the work in question. Academic research on the possibilities and effects of satire in games is minimal, if compared with other rhetorical uses of playful interaction. This paper contributes to our understanding of satire in games by discussing and annotating design decisions that were meant to be taken satirically. More specifically, the focus of this paper is Construction BOOM!, a tile-laying boardgame designed by the some of the authors of the paper (Gualeni and Schellekens) themselves with the overt intention of satirizing the current situation of real-estate development in Malta. Part of our contribution consists in leveraging the notion of the ’implied designer’ as articulated by Van de Mosselaer and Gualeni to show how game elements participate in the player’s inferring a satiric implied designer for the game. The paper highlights the opportunities available for designers to implement satire into the various elements of a game and opens the door to further research into exploring how much these elements influence the perception of satire by players.

The level of adoption of Precision Agriculture (PA) technologies is still very different from one country to another and from one region to another in the same country. A major challenge is to develop PA from a best practice pursued by a minority of enlightened farmers to a widespread practice with sizeable impact on the use of environmental resources. One of the obstacles hindering PA is the lack of quantitative data readily accessible to farmers to guide their daily operations. In this paper, we present the information system developed within project POSITIVE to support and enhance precision irrigation across the whole Emilia-Romagna region. To this purpose, the POSITIVE information system establishes a service transforming satellite and sensor data into biophysical parameters and vegetation indices with full regional coverage. These data are automatically fed into a public irrigation advisory service (Irrinet+) thereby enabling precision irrigation and fertigation on a regional scale. Irrigation maps can be sent as advice to farmers or directly commanded to registered irrigation machines. The architecture of the distributed information system and the open protocols developed to achieve scalability and enable interaction of multiple heterogeneous components are reported in the paper.

This paper describes the design and evaluation of a vision system conceived to provide perception in Autonomous Underwater Vehicle (AUV) intervention tasks. Due to the accuracy requirements inherent in manipulation tasks, high performance vision systems, enabling adequate perception capabilities, are needed to cope with underwater interventions. However, vision systems are challenged by the difficulties and variability of underwater environments as well as by the need to operate in a sealed canister. The vision system described in this paper addresses design issues like computational performance, energy power consumption, heat dissipation, and network capabilities. Even though the system has been designed to support stereovision, experiments in several underwater contexts have shown that stereovision is seldom applicable, due to the many problems faced by light propagation in water. Developing a system for underwater operation emphasizes the need for tradeoffs between computational performance and power consumption and dissipation, as well as the need for flexibility to support multiple vision processing pipelines and adapt to the specific underwater context.

This work proposes a method for autonomous robot exploration of unknown objects by sensor fusion of 3D range data. The approach aims at overcoming the physical limitation of the minimum sensing distance of range sensors. Two range sensors are used with complementary characteristics mounted in eye-in-hand configuration on a robot arm. The first sensor operates at mid-range and is used in the initial phase of exploration when the environment is unknown. The second sensor, which provides short-range data, is used in the following phase where the objects are explored at close distance through next best view planning. Next best view planning is performed using a volumetric representation of the environment. A complete point cloud model of each object is finally computed by global registration of all object observations including mid-range and short range views. In experiments performed in environments with multiple rigid objects the global registration algorithm has proven more accurate than a standard sequential registration approach.

A method is proposed for gesture recognition and humanoid imitation based on Functional Principal Component Analysis (FPCA). FPCA is a statistical technique of functional data analysis that has never been applied before for humanoid imitation. In functional data analysis data (e.g. gestures) are functions that can be considered as observations of a random variable on a functional space. FPCA is an extension of multivariate PCA that provides functional principal components which describe the modes of variation in the data. In the proposed approach FPCA is used for both unsupervised clustering of training data and gesture recognition. In this work we focus on arm gesture recognition. Human hand paths in Cartesian space are reconstructed from inertial sensors. Recognized gestures are reproduced by a small humanoid robot. The FPCA algorithm has also been compared to a state of the art algorithm for gesture classification based on Dynamic Time Warping (DTW). Results indicate that, in this domain, the FPCA algorithm achieves a comparable recognition rate while it outperforms DTW in terms of efficiency in execution time.

Complex architectural artifacts and industrial systems are increasingly being designed with the help of virtual prototypes. In these virtual environments users can interact with specific features of a simulation to evaluate functional aspects or learn complex operations before system construction or while unaccessible. A key issue for a virtual prototype-based design cycle is the possibility to easily interact with the simulated system to trigger specific functions regardless of the limited technical skills of the user. In this paper, the potential of the Nintendo Wiimote device programmed as a tangible interface to navigate and interact in virtual environments has been assessed in a user study involving multiple subjects. Results with basic 2D point-and-click and select-drag-and-drop tasks, as well as in 3D navigation and interaction scenarios, have shown the effectiveness and reliability of the Wiimote-based interaction. Moreover, the interface was perceived by the subjects as intuitive, not complicated, and requiring little mental effort.

Object category recognition and localization in 3D range data is of great importance in robot manipulation. In this work we propose a novel approach for object categorization and grasping that is focused on topological shape segmentation. The method allows generation of watertight triangulated models of the objects and their shape segmentation into parts. This segmentation provides meaningful information about grasp affordances. An efficient technique for encoding proximity data from range scans is also presented as well as an advanced strategy for manipulation of object sub-parts. Experiments are reported in a real environment using a robot arm equipped with eye-in-hand laser scanner and a parallel gripper.

A method for robot to human object hand-over is presented that takes into account user comfort. Comfort is addressed by serving the object to facilitate user's convenience. The object is delivered so that the most appropriate part is oriented towards the person interacting with the robot. This approach, aimed at contributing to the development of socially aware robots, has not been considered in previous works. The robot system also supports sensory-motor skills like object and people detection, robot grasping and motion planning. The experimental setup consists of a six degrees of freedom robot arm with both an eye-in-hand laser scanner and a fixed range sensor. The user interacting with the robot can assume an arbitrary position in front of the robot. Experiments are reported from a user study.

In this work we introduce a novel approach for robot grasp planning. The proposed method combines the benefits of programming by human demonstration for teaching appropriate grasps with those of automatic 3D shape segmentation for object recognition and semantic modeling. The work is motivated by important studies on human manipulation suggesting that when an object is perceived for grasping it is first parsed in its constituent parts. Following these findings we present a manipulation planning system capable of grasping objects by their parts which learns new tasks from human demonstration. The central advantage over previous approaches is the use of a topological method for shape segmentation enabling both object retrieval and part-based grasp planning according to the affordances of an object. Manipulation tasks are demonstrated in a virtual reality environment using a data glove. After the learning phase, each task is planned and executed in a robot environment that is able to generalize to similar, but previously unknown, objects.

Loop closure is one of the most difficult task in localization and mapping problems since it suffers from perceptual aliasing. Multi-hypothesis topological SLAM algorithms have been developed to exploit connectivity and disambiguate such difficult task. In this paper, we propose a multi-hypothesis constraint network algorithm that tracks multiple map topologies and simultaneously keeps metric information. The map is stored as a graph consisting of poses and constraints and each constraint is associated to a loop closure hypothesis. Hypotheses are stored in a hypothesis tree that is expanded whenever possible loop closure may occur. Network poses are computed according to the most likely topological configuration, but alternative pose values are also computed for the poses that are adjacent to a hypothesis constraint to recover quickly the new configuration when required. Results provide a validation of the proposed approach.

In the last decade, several algorithms, usually based on information filtering techniques, have been proposed to address multi-robot mapping problem. Less interest has been devoted to investigate a parallel or distributed organization of such algorithms in the perspective of multi-robot exploration. In this paper, we propose a distributed algorithm for map estimation based on Gauss-Seidel relaxation. The complete map is shared among independent tasks running on each robot, which integrate the independent robot measurements in local submaps, and a server, which stores contour nodes separating the submaps. Each task updates its local submap and periodically checks for inter-robot data associations. Gauss-Seidel relaxation is performed independently on each robot and afterwards on the contour nodes set on the server. Results illustrate the potential and flexibility of the new approach.

We present an approach for visuo-haptic augmented reality, which is focused on object manipulation tasks. The interactive environment combines three-DOF haptic rendering and physics-based animation. A desktop haptic device with force feedback is driven by the user to interact with movable virtual objects that are superimposed upon a visual representation of the real workspace. Virtual objects coexist with real objects in the augmented reality space and are simulated in a physically plausible manner. The system supports both rigid and deformable objects with arbitrary shape. Accurate algorithms for camera calibration, registration, object manipulation and feedback computation have also been developed. Several experiments have been performed in order to test both single and dual-user collaborative tasks.

Physics-based animation is becoming an essential feature for any advanced simulation software. In this paper we explore potential benefits of physics-based modeling for task programming in virtual reality. Firstly, we show how realistic animation of manipulation tasks can be exploited for learning sequential constraints from user demonstrations. In particular, we propose a method where information about physical interaction is used to discover task precedences and to reason about task similarities at the goal level. A second contribution of the paper is the application of physics-based modeling to the problem of disassembly sequence planning. Experiments have been performed in a desktop virtual reality environment with dataglove and motion tracker.

We address the problem of disassembly planning from a novel perspective. In the proposed method the goal is to find all the physically admissible subassemblies in which a set of objects can be disassembled and to identify feasible disassembly motions. Stability of object configurations under the effect of gravity and friction is computed by relying on a physics-based animation engine. We propose efficient strategies to reduce computational time that take into account precedence relations, arising from user assembly demonstrations as well as geometrical clustering. We have also developed a motion planning technique for generating non-destructive disassembly paths on a query-based approach. Experiments have been performed in an interactive virtual environment including a dataglove that allows realistic object manipulation and grasping.

Several recent algorithms address simultaneous localization and mapping as a maximum likelihood problem. While many proposed methods focus on efficiency or on online computation, less interest has been devoted to investigate a parallel or distributed organization of such algorithms in the perspective of multi-robot exploration. In this paper, we propose a parallel algorithm for map estimation based on Gauss-Seidel relaxation. The map is given in the form of a constraints network and is partioned into clusters of nodes by applying a node-tearing technique. The identified clusters of nodes can be processed independently as tasks assigned to different processors. The graph decomposition induces also a hierarchical organization of nodes that could be exploited for more sophisticated relaxation techniques. Results illustrate the potential and flexibility of the new approach.

Biomechanics has strong implications in both medicine and robotics as it is concerned with the study of biological systems from an engineering point of view. In this work we propose a physics-based system for accurate simulation of dogpsilas spine at walking gait. The spinal column has been modeled as a set of rigid bodies, representing the vertebrae, connected by joints. Real-time dynamic simulation has been carried out from motion captured data collected from skin markers. A feedback controller has been developed for error minimization. The main contribution of the paper is the development of a tool for analyzing the forces acting on the spine. We report experiments performed with a boxer dog walking on a treadmill. Potential applications of this study involve bio-inspired robotics and computer graphics, as well as diagnostic applications for live animals.

This paper addresses the issue of how Programming by Demonstration can assist the development of task-related grasping capabilities in a robotic system. Finding a proper quality measure for the evaluation of grasping tasks is a crucial topic for service robots. While classical grasp quality measures do not include task information, we propose a measure which takes into account user experience. Experiments have been performed in a virtual environment that enables real-time human interaction by means of a dataglove and a motion tracker. Also, a local grasp optimization technique is described to amend uncertainties arising from user demonstration. Finally, the grasp quality measure has been applied for synthesizing manipulation tasks with a simulated robot arm.

Automatic environment modeling is an essential requirement for intelligent robots to execute manipulation tasks. Object recognition and workspace reconstruction also enable 3D user interaction and programming of assembly operations. In this paper a novel method for synthesizing robot grasps from demonstration is presented. The system allows learning and classification of human grasps demonstrated in virtual reality as well as teaching of robot grasps and simulation of manipulation tasks. Both virtual grasp demonstration and grasp synthesis take advantage of a topology-preserving approach for automatic workspace modeling with a monocular camera. The method is based on the computation of edge-face graphs. The algorithm works in real-time and shows high scalability in the number of objects thus allowing accurate reconstruction and registration from multiple views. Grasp synthesis is performed mimicking the human hand pre-grasp motion with data smoothing. Experiments reported in the paper have tested the capabilities of both the vision algorithm and the grasp synthesizer.

Sales of entertainment robots are primed to explode in the next few years. Gesture imitation from a human operator demonstration is considered a promising technique that allows unexperienced users to easily interact with a robotic platform. In this paper a Programming by Demonstration system for a humanoid robot is proposed. The robot is a Robosapien V2 toy which is programmed to observe and imitate human gestures. The system combines inputs from multiple sensory devices, including a dataglove, a motion tracker and a monocular vision system. In the proposed imitation approach the sensor data are mapped to the joint space of the robot. Even though the performance of the robot is constrained by the kinematic and dynamic limitations of RSV2 and by the sensor inaccuracies, experiments involving simple gesture imitation of an untrained user show the viability and effectiveness of the method.

This paper describes a virtual reality based programming by demonstration system for grasp recognition in manipulation tasks and robot pregrasp planning. The system classifies the human hand postures taking advantage of virtual grasping and information about the contact points and normals computed in the virtual reality environment. A pregrasp planning algorithm mimicking the human hand motion is also proposed. Reconstruction of human hand trajectories, approaching the objects in the environment, is based on NURBS curves and a data smoothing algorithm. Some experiments involving grasp classification and pregrasp planning, while avoiding obstacles in the workspace, show the viability and effectiveness of the approach

This paper focuses on the role of real-time operation support in mobile robot control architectures for service applications. We show that a control architecture with integrated real-time features ensures better performance in terms of accuracy in navigation and reactivity to external events, compared with an architecture where no provision for real-time has been made. We argue that proper consideration to real-time operation should be given in order to ensure safe, dependable robot operation in human-inhabited environments. Along the paper, we describe features of YARA, a software framework improving dependability of mobile robot architectures by virtue of its support for real-time operation.

This paper describes the trajectory learning component of a programming by demonstration (PbD) system for manipulation tasks. In case of multiple user demonstrations, the proposed approach clusters a set of hand trajectories and recovers smooth robot trajectories overcoming sensor noise and human motion inconsistency problems. More specifically, we integrate a geometric approach for trajectory clustering with a stochastic procedure for trajectory evaluation based on hidden Markov models. Furthermore, we propose a method for human hand trajectory reconstruction with NURBS curves by means of a best-fit data smoothing algorithm. Some experiments show the viability and effectiveness of the approach.

We investigate the effectiveness of several types of virtual fixtures in a robot programming by demonstration interface. We show that while all types of virtual fixtures examined yield a significant reduction in the number of errors in tight tolerance peg-in-hole tasks, color and sound fixtures generally outperform a tactile fixture in terms of both execution time of successful trials and error rate. We have found also that when users perceive that the task is very difficult but the system is providing some help by means of a virtual fixture, they tend to spend more time trying to achieve a successful task execution. Thus, for difficult tasks the benefits of virtual fixturing are better reflected in a reduction of the error rate than in a decreased execution time. We conjecture that these trends are related to the limitations of currently available interfaces for human-robot interaction through virtual environments and to the different strategies adopted by the users to cope with such limitations in high-accuracy tasks.

Distributed telerobotic applications exploiting Internet-related technologies, such as virtual laboratories and on-line robots, require effective techniques for timely delivery of sensory data to remote clients. In these systems, there is a need to distribute increasing quantities of sensory data to a potentially large number of clients during system operation. In this paper, we describe and evaluate three implementations of a sensory data distribution subsystem in the context of a CORBA-based framework for telerobotic applications. Experimental results show that solutions exploiting CORBA services and based on the event channel paradigm represent a viable alternative to ad-hoc solutions. The overhead associated to CORBA services becomes less significant with larger message size. Moreover, these services ensure portability, extensibility, reduction in programming complexity, and improved scalability when the number of clients increases.

Probabilistic path planning driven by a potential field is a well established technique and has been successfully exploited to solve complex problems arising in a variety of domains. However, planners implementing this approach are rather inefficient in dealing with certain types of local minima occurring in the potential field, especially those characterized by deep or large attraction basins. In this paper, we present a potential field planner combining "smart" escape motions from local minima with parallel computation to improve overall performance. The results obtained show significant improvement in planning time, along with remarkable reduction in standard deviation. A performance comparison on a benchmark problem of the potential field planner and an existing, state-of-the-art planner is also included. Our investigation confirms the effectiveness of potential field as heuristic to solve difficult path planning problems.

The technological developments in distributed systems have led to new telerobotic applications, such as virtual laboratories and remote maintenance of complex equipment. These applications must satisfy both the general requirements of distributed computing, e.g. location transparency and interoperability, and the domain-specific requirements of reconfigurability, guaranteed performance, real-time operation, and cooperation among robots and sensory systems. In this paper, we describe a software framework for distributed telerobotic systems exploiting advanced CORBA features, including Asynchronous Method Invocation and real-time priorities. The framework allows development of portable multithreaded client-server applications supporting concurrent and preemptable actions in the target robot system, and has been evaluated in a laboratory setup including a robot manipulator and two cameras accessible by multiple clients.

Randomized motion planners are greatly affected by the efficiency and robustness of algorithms for collision checking of robot configurations. A number of powerful collision detection algorithms are currently available to the research community although their relative capabilities and performance cannot be easily compared due to the many factors involved. We have experimentally evaluated collision detection packages within the context of motion planning for rigid and articulated robots in 3D workspaces. Artificial and realistic problems have been chosen as benchmarks to assess package behavior with different object models. Reported experimental results should help the user in choosing the appropriate collision detection package. In this paper we also present the framework we exploited in our planner to allow pluggable distance computation functions and keep the code simple and easy to maintain.

Presents the classification results of a bistatic sonar sensor with decision tree classifier for use in mobile robot navigation. Unlike previous work the paper investigates the discrimination and robustness of the sensor's classifications when presented with common office objects with complex geometries. The feature extraction process uses a novel perturbed L/sub 2/ method which allows a physical interpretation of the features. The robustness of the classifications indicate that, given a suitably enlarged set of training objects, the approach would be suitable for use within office environments.

This paper presents a motion planning algorithm capable of exploiting the experience gained in previous path computations in the same static workspace. The algorithm takes advantage of a parallel approach to speed up computation and compile a graph retaining useful knowledge about the environment. Experimental results assess the performance improvement of the experience-based planner over the parallel implementation of a well-known probabilistic motion planning algorithm.

Due to the high number of sensors managed and need to perform complex reasoning activities, real-time control systems of autonomous robots exhibit a high potential for overload, i.e., real-time tasks missing their deadlines. In these systems overload should be regarded as a likely occurrence and hence managed accordingly. In this paper we illustrate a novel scheduling technique for adaptation of soft real-time load to available computational capacity in the context of autonomous robot control architectures. The technique is based on rate modulation of a set of periodic tasks in a range of admissible rates. The technique is shown to be easily computable and several variations in implementation are reviewed within the paper.

Describes a real-time library providing facilities useful in the design of robot control architectures. The library supports structured creation of reactive and deliberative modules, dynamic modification of relevant real-time parameters, generation of timing fault handlers, measurement and monitoring of execution times. This support enables adaptation of the rate of computation of real-time modules to the rate of change of the external world, and hence better tuning of robot behavior to the world uncertainty and dynamics. The real-time library has been put to work by designing a hybrid control architecture for a robot performing a kitting task. In the prototype experiment described in the paper, a Puma 560 robot manipulator is fed with parts by a small mobile robot. The control architecture governing Puma operations dynamically allocates computational resources to reactive and deliberative modules, according to the task level priorities.

The paper describes a technique for pose-independent recognition of convex, free-form objects from tactile sensing. The technique is based on internal and external volumetric approximations of the unknown object built from sensed contact information. Center of mass and principal inertia axes of the volumetric approximations are then exploited to estimate object pose. Recognition is carried out based on features extracted from the approximations considered in their currently recovered pose. The paper illustrates the attained pose-independent recognition performance with the help of automated exploration strategies, on a problem involving a set of 20 objects. Registration performance is largely dependent upon the accuracy of the developed volumetric approximations and hence upon the extent of the exploration. The main advantages of the overall recognition technique are its ability to deal with sparse sensory data and its limited computational requirements.

The paper describes an efficient technique for recognition of convex objects from tactile sensing. The technique is based on the development of internal and external volumetric approximations of the unknown object, and exploits an effective feature selection strategy along with early pruning of incompatible objects to improve recognition performance. Exploration strategies conceived for a single-finger probing device rely on the developed volumetric approximations to guide sensing along directions where uncertainty about the explored object is larger, and can take into account exploration costs.

This paper describes a novel method of object recognition based upon the piece-wise linear approximation of ultrasonic echoes which, when tested with examples typical of a work cell environment, achieves classification success rates of 92-98%. The approach uses decision tree classifiers constructed from simple features derived from the echoes of a monostatic sonar and taken from a number of view points. The results illustrate the effect upon the method's classification success of the use of a single view point, the inclusion of additional view points, and the possibility of object rotations, for 22 classes of objects. The processes of feature extraction and classification are accomplished within a time comparable with the time of flight of the pulse, and hence the method has potential for real time applications.

We describe an object recognition technique based upon the extraction of simple features from the initial part of ultrasonic echoes. Features collected from a single or multiple viewpoints are classified using a decision tree. Since only the initial part of the echo is examined, the approach has potential for faster classification than alternative techniques requiring processing of the entire waveform. To emulate a workcell scenario, the approach has been verified mounting a Polaroid sensor at the wrist of a Puma 560 manipulator and implementing a simple modification of the proprietary circuitry (Polaroid Ranging Unit 6500). When tested with a set of 8 small plastic objects with regular shapes, the recognition technique has achieved classification success rates from 72% to 98%, depending upon the number and selection of echoes exploited for recognition. The paper illustrates classification performance using single or multiple viewpoints under both axis parallel and oblique decision trees.

Polyhedral models derived from haptically-sensed information have been shown to effectively support recognition of convex objects in known poses. In this paper we investigate the robustness of the recognition methodology based on these models with respect to various sources of potentially inaccurate knowledge, including inexact a priori knowledge about shape and location of the objects to be identified and noise in the sensed information. The methodology is shown to exhibit some general robustness property, essentially stemming from the underlying volumetric modeling approach. Specific attention is paid to the effect of errors in the sensed normal directions at the contacts, since reliable estimation of this quantity could prove difficult in the practical application of the methodology. To this purpose, a technique is developed for identification and pruning of outliers from the data set. Further, a polyhedral model is defined which does not rely on normals but only on contact localization and hand pose information. Experimental results assess the impact on recognition performance of the loss of the normal information.

This paper discusses a multi-agent framework for the control of robot systems along with an integrated programming environment which takes advantage of it. An agent society is engaged in a competitive or cooperative interaction under a control metalevel while a particular agent represents the real-time robot machine. By virtue of the support of adequate tools, this schema well lends itself both to a general increase of robot programming capability and flexibility and to rapid prototyping of different architectural solutions. The overall programming environment is built upon the integration of a distributed, extended logic programming environment with a real-time RCCL-based robot server. It is also interfaced to a graphical simulator in which a sensory driven task can be given a preliminary validation. An example of application of the environment to a simple test case concludes the paper.>

This paper presents two volumetric object representation models oriented to the dynamic integration of the information perceived by a robot hand in a haptic exploration. The enveloping polyhedral model is an upper approximation of the object derived from contact planes, whereas the approximating polyhedral model also takes advantage of the information provided by non-contacting hand elements. Features for object classification are derived from a spatial sampling of the polyhedral approximation. The effectiveness of these models in haptic recognition is investigated by considering alternative classification techniques and the haptic development of internal representations about the samples when formal models are unavailable. Experimental results demonstrate the effectiveness and robustness of the overall methodology.>

Addresses the grasp synthesis problem arising in task planning for robotic dextrous hands. For this purpose, a hybrid architecture is proposed, which relies on symbolic and subsymbolic computations to exploit heterogeneous sources of knowledge, such as practical experience learned through experiments with a real device, heuristic rules gained from human observation, geometric reasoning and, when applicable, analytical results. After a preliminary discussion of representation levels and techniques, this paper describes the design of a tool for selecting the feasible grasps of a robotic hand under various situations and for ranking them according to task-oriented criteria. The interaction of a rule-based expert system with a neural network-based classifier provides support to both explicit reasoning and direct learning from experience. The features taken into account by the tool are object geometry, hand kinematic capabilities, task requirements, (in terms of both accessibility and robustness) and workspace constraints.