An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation

This research presents an autonomous robotic framework for academic, vocational and training purpose. The platform is centred on a 6 Degree Of Freedom (DOF) serial robotic arm. The kinematic and dynamic models of the robot have been derived to facilitate controller design. An on-board camera to scan the arm workspace permits autonomous applications development. The sensory system consists of position feedback from each joint of the robot and a force sensor mounted at the arm gripper. External devices can be interfaced with the platform through digital and analog I/O ports of the robot controller. To enhance the learning outcome for beginners, higher level commands have been provided. Advanced users can tailor the platform by exploiting the open-source custom-developed hardware and software architectures. The efficacy of the proposed platform has been demonstrated by implementing two experiments; autonomous sorting of objects and controller design. The proposed platform finds its potential to teach technical courses (like Robotics, Control, Electronics, Image-processing and Computer vision) and to implement and validate advanced algorithms for object manipulation and grasping, trajectory generation, path planning, etc. It can also be employed in an industrial environment to test various strategies prior to their execution on actual manipulators.     

Soft object deformation monitoring and learning for model-based robotic hand manipulation

This paper discusses the design and implementation of a framework that automatically extracts and monitors the shape deformations of soft objects from a video sequence and maps them with force measurements with the goal of providing the necessary information to the controller of a robotic hand to ensure safe model-based deformable object manipulation. Measurements corresponding to the interaction force at the level of the fingertips and to the position of the fingertips of a three-finger robotic hand are associated with the contours of a deformed object tracked in a series of images using neural-network approaches. The resulting model captures the behavior of the object and is able to predict its behavior for previously unseen interactions without any assumption on the object's material. The availability of such models can contribute to the improvement of a robotic hand controller, therefore allowing more accurate and stable grasp while providing more elaborate manipulation capabilities for deformable objects. Experiments performed for different objects, made of various materials, reveal that the method accurately captures and predicts the object's shape deformation while the object is submitted to external forces applied by the robot fingers. The proposed method is also fast and insensitive to severe contour deformations, as well as to smooth changes in lighting, contrast, and background.     

Model-based autonomous system for performing dexterous, human-level manipulation tasks

This article presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation Software (ARM-S) program. Performing human-level manipulation tasks is achieved through a novel combination of perception in uncertain environments, precise tool use, forceful dual-arm planning and control, persistent environmental tracking, and task level verification. Deliberate interaction with the environment is incorporated into planning and control strategies, which, when coupled with world estimation, allows for refinement of models and precise manipulation. The system takes advantage of sensory feedback immediately with little open-loop execution, attempting true autonomous reasoning and multi-step sequencing that adapts in the face of changing and uncertain environments. A tire change scenario utilizing human tools, discussed throughout the article, is used to described the system approach. A second scenario of cutting a wire is also presented, and is used to illustrate system component reuse and generality.     

面向灵巧操作的视觉目标识别

为了能够实现灵巧手对目标物体进行精准操作,研究了一种利用Kinect检测出目标物体,在帧差法的基础上对获取的深度进行背景相减,获取出目标物体的运动点,在此基础上利用获取的目标物体的特征采用T-S模糊逻辑判断出目标物体的方法,以BH8-280对目标物体进行抓取实验为例,在实验中,Kinect在帧差法的基础上检测出目标物体的位姿,大小,形状,以此为基础建立起T-S模糊逻辑系统,判断出目标物体的属性和类别,通过实验结果进一步说明了利用本文研究的方法显著地提高了判断物体的准确率和效率,为灵巧手的精细控制抓取奠定了基础。     

Developmental learning for autonomous robots

Developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of competence.This paper uses ideas and inspiration from early infant psychology (up to three months of age) to examine how robot systems could discover the structure of their local sensory-motor spaces and learn how to coordinate these for the control of action.An experimental learning model is described and results from robotic experiments using the model are presented and discussed.     

Vehicle-manipulator system dynamic modeling and control for underwater autonomous manipulation

Underwater autonomous manipulation is a challenging task, which not only includes a complicated multibody dynamic and hydrodynamic process, but also involves the limited observation environment. This study systematically investigates the dynamic modeling and control of the underwater vehicle-manipulator multibody system. The dynamic model of underwater vehicle-manipulator system has been established on the basis of the Newton–Euler recursive algorithm. On the basis of dynamic analysis, a motion planning optimization algorithm has been designed in order to realize the coordinate motions between AUV and manipulator through reducing the restoring forces and saving the electric power. On the other hand, a disturbance force observer including the coupling and restoring forces has been designed. An observer-based dynamic control scheme has been established in combination with kinematic and dynamic controller. Furthermore, from the simulations, although the disturbance forces such as restoring and coupling forces are time varying and great, the observer-based dynamic coordinate controller can maintain the AUV attitude stable during the manipulator swing and pitch motions. During the precise manipulation simulation, the stable AUV attitude and minimization of disturbance forces have been realized through combination of optimal motion planning and the observer-based dynamic coordinate controller.     

Learning object,grasping and manipulation activities using hierarchical HMMs

This article presents a probabilistic algorithm for representing and learning complex manipulation activities performed by humans in everyday life. The work builds on the multi-level Hierarchical Hidden Markov Model (HHMM) framework which allows decomposition of longer-term complex manipulation activities into layers of abstraction whereby the building blocks can be represented by simpler action modules called action primitives. This way, human task knowledge can be synthesised in a compact, effective representation suitable, for instance, to be subsequently transferred to a robot for imitation. The main contribution is the use of a robust framework capable of dealing with the uncertainty or incomplete data inherent to these activities, and the ability to represent behaviours at multiple levels of abstraction for enhanced task generalisation. Activity data from 3D video sequencing of human manipulation of different objects handled in everyday life is used for evaluation. A comparison with a mixed generative-discriminative hybrid model HHMM/SVM (support vector machine) is also presented to add rigour in highlighting the benefit of the proposed approach against comparable state of the art techniques.     

Policy learning for autonomous feature tracking

We consider the problem of tracing the structure of oceanological features using autonomous underwater vehicles (AUVs). Solving this problem requires the construction of a control strategy that will determine the actions for the AUV based on the current state, as measured by on-board sensors and the historic trajectory (including sensed data) of the AUV. We approach this task by applying plan-based policy-learning, in which a large set of sampled problems are solved using planning and then, from the resulting plans a decision-tree is learned, using an established machine-learning algorithm, which forms the resulting policy. We evaluate our approach in simulation and report on sea trials of a prototype of a learned policy. We indicate some of the lessons learned from this deployed system and further evaluate an extended policy in simulation.     

A next-best-view system for autonomous 3-D object reconstruction

The focus of this paper is to design and implement a system capable of automatically reconstructing a prototype 3D model from a minimum number of range images of an object. Given an ideal 3D object model, the system iteratively renders range and intensity images of the model from a specified position, assimilates the range information into a prototype model, and determines the sensor pose (position and orientation) from which an optimal amount of previously unrecorded information may be acquired. Reconstruction is terminated when the model meets a given threshold of accuracy. Such a system has applications in the context of robot navigation, manufacturing, or hazardous materials handling. The system has been tested successfully on several synthetic data models, and each set of results was found to be reasonably consistent with an intuitive human search. The number of views necessary to reconstruct an adequate 3D prototype depends on the complexity of the object or scene and the initial data collected. The prototype models which the system recovers compare well with the ideal models     

Fostering collaborative learning in Second Life: Metaphors and affordances

In this paper we examine the transferability of the Jigsaw and Fishbowl collaborative learning techniques to the Second Life platform. Our aim is to assess the applicability of Second Life for collaborative learning by developing virtual tools and metaphors and exploiting the representational richness of this novel medium. In order to enhance the existing metaphors and affordances of SL, our research team implemented educational spaces, avatar clothing, and tools for non-verbal communication and visualisation. By implementing a blended learning evaluation approach we attempted to answer three research questions focusing on student collaboration, avatar representation and learning space awareness. We can conclude that SL can supplement and/or augment face to face interactions, improving upon previous approaches in distance collaboration and communication. Furthermore, although our team augmented SL’s ability to support collaborative learning, avatar representation does not seem to scale well. Finally, the majority of the implemented affordances and metaphors seem to have enhanced collaboration and learning space awareness.     

Pyramid context learning for object detection

Contextual information in complex scenarios is critical for accurate object detection. Existing state-of-the-art detectors have greatly improved detection performance with the use of contexts around objects. However, these detectors consider the local and global contexts separately, which limits the improvement in detection accuracy. In this paper, we propose a pyramid context learning module (PCL) for object detection, which makes full use of the feature context at different levels. Specifically, two operators, named aggregation and distribution, are designed to assemble and synthesize contextual information at different levels. In addition, a channel context learning operator is also used to capture the channel context. PCL is a universal module, so it can be easily integrated into most of the detection frameworks. To evaluate our PCL, we apply it into some popular detectors, e.g., SSD, Faster R-CNN and RetinaNet, and conduct extensive experiments on PASCAL VOC and MS COCO datasets. Experimental results show that PCL can produce competitive performance gains and significantly improve the baselines.

     

Probabilistic visual learning for object representation

We present an unsupervised technique for visual learning, which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a mixture-of-Gaussians model (for multimodal distributions). Those probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and nonrigid objects, such as hands     

A quantum model for autonomous learning automata

The idea of information encoding on quantum bearers and its quantum-mechanical processing has revolutionized our world and brought mankind on the verge of enigmatic era of quantum technologies. Inspired by this idea, in present paper, we search for advantages of quantum information processing in the field of machine learning. Exploiting only basic properties of the Hilbert space, superposition principle of quantum mechanics and quantum measurements, we construct a quantum analog for Rosenblatt’s perceptron, which is the simplest learning machine. We demonstrate that the quantum perceptron is superior to its classical counterpart in learning capabilities. In particular, we show that the quantum perceptron is able to learn an arbitrary (Boolean) logical function, perform the classification on previously unseen classes and even recognize the superpositions of learned classes—the task of high importance in applied medical engineering.     

Visibility-based spatial reasoning for object manipulation in cluttered environments

In this paper, we present visibility-based spatial reasoning techniques for real-time object manipulation in cluttered environments. When a robot is requested to manipulate an object, a collision-free path should be determined to access, grasp, and move the target object. This often requires processing of time-consuming motion planning routines, making real-time object manipulation difficult or infeasible, especially in a robot with a high DOF and/or in a highly cluttered environment. This paper places special emphasis on developing real-time motion planning, in particular, for accessing and removing an object in a cluttered workspace, as a local planner that can be integrated with a general motion planner for improved overall efficiency. In the proposed approach, the access direction of the object to grasp is determined through visibility query, and the removal direction to retrieve the object grasped by the gripper is computed using an environment map. The experimental results demonstrate that the proposed approach, when implemented by graphics hardware, is fast and robust enough to manipulate 3D objects in real-time applications.     

Statistically correlated multi-task learning for autonomous driving

Neural Computing and Applications - Autonomous driving research is an emerging area in the machine learning domain. Most existing methods perform single-task learning, while multi-task learning...     

Channel spatial attention based single-shot object detector for autonomous vehicles

Multimedia Tools and Applications - Real-time object detection with high accuracy is the measure concern for the autonomous vehicle to provide safety. Recently many state-of-the-art methods used...     

Disengaged students and dialogic learning: the role of CSCL affordances

Only a few studies have dealt with the challenge of bridging the linguistic gap between the dialogic realm and the talk of disengaged students. Bridging this gap is particularly relevant to the CSCL community since one of its utmost aims is to promote the dialogic. This study aims to articulate how to harness the CSCL design and affordances to enhance dialogic pedagogy with disengaged students. Using temporal analysis of philosophical discussions for children, we focus on three disengaged 8th grade students participating in successive discussions mediated by a CSCL tool (Argunaut), and follow the way they talk with their peers in the classroom. The study shows the gradual emergence of the dialogic among those students. We describe the transition of their talk moves, from initially reproducing the way they talk to adopting dialogical norms. To explain this we conceptualize the notion of carriers of discursive norms and discuss its transformative role in dialogue. The dialogic transition was made possible by the pedagogical design and the design of the CSCL tools. These affordances allowed the students change the meaning of the conversational building blocks of space, silence, addressee, and the ethics of talk.     

Single machine scheduling with autonomous learning and induced learning

As an important management tool of winning competitive advantage, induced learning effect has been widely studied in empirical research area. But it is hardly considered in scheduling problems. In this paper, autonomous and induced learning are both taken into consideration. The investment of induced learning is interpreted as specialized time intervals to implement training, knowledge sharing and transferring etc. We present algorithms to determine jointly the optimal job sequence and the optimal position of induced learning intervals, with the objective of minimizing makespan.