A Developmental Learning Approach of Mobile Manipulator via Playing

Inspired by infant development theories, a robotic developmental model combined with game elements is proposed in this paper. This model does not require the definition of specific developmental goals for the robot, but the developmental goals are implied in the goals of a series of game tasks. The games are characterized into a sequence of game modes based on the complexity of the game tasks from simple to complex, and the task complexity is determined by the applications of developmental constraints. Given a current mode, the robot switches to play in a more complicated game mode when it cannot find any new salient stimuli in the current mode. By doing so, the robot gradually achieves it developmental goals by playing different modes of games. In the experiment, the game was instantiated into a mobile robot with the playing task of picking up toys, and the game is designed with a simple game mode and a complex game mode. A developmental algorithm, “Lift-Constraint, Act and Saturate,” is employed to drive the mobile robot move from the simple mode to the complex one. The experimental results show that the mobile manipulator is able to successfully learn the mobile grasping ability after playing simple and complex games, which is promising in developing robotic abilities to solve complex tasks using games

Learning and reuse of engineering ramp-up strategies for modular assembly systems

YesWe present a decision-support framework for speeding up the ramp-up of modular assembly systems by learning from past experience. Bringing an assembly system to the expected level of productivity requires engineers performing mechanical adjustments and changes to the assembly process to improve the performance. This activity is time-consuming, knowledge-intensive and highly dependent on the skills of the engineers. Learning the ramp-up process has shown to be effective for making progress faster. Our approach consists of automatically capturing information about the changes made by an operator dealing with disturbances, relating them to the modular structure of the machine and evaluating the resulting system state by analysing sensor data. The feedback thus obtained on applied adaptations is used to derive recommendations in similar contexts. Recommendations are generated with a variant of the k-nearest neighbour algorithm through searching in a multidimensional space containing previous system states. Applications of the framework include knowledge transfer among operators and machines with overlapping structure and functionality. The application of our method in a case study is discussed.Funded by the European Commission as part of the 7th Framework Program under the Grant agreement CP-FP 229208-2, FRAME project

Internal visuomotor models for cognitive simulation processes

Kaiser A. Internal visuomotor models for cognitive simulation processes. Bielefeld: Bielefeld University; 2014.Recent theories in cognitive science step back from the strict separation of perception, cognition, and the generation of behavior. Instead, cognition is viewed as a distributed process that relies on sensory, motor and affective states. In this notion, internal simulations -i.e. the mental reenactment of actions and their corresponding perceptual consequences - replace the application of logical rules on a set of abstract representations. These internal simulations are directly related to the physical body of an agent with its designated senses and motor repertoire. Correspondingly, the environment and the objects that reside therein are not viewed as a collection of symbols with abstract properties, but described in terms of their action possibilities, and thus as reciprocally coupled to the agent. In this thesis we will investigate a hypothetical computational model that enables an agent to infer information about specific objects based on internal sensorimotor simulations. This model will eventually enable the agent to reveal the behavioral meaning of objects. We claim that such a model would be more powerful than classical approaches that rely on the classification of objects based on visual features alone. However, the internal sensorimotor simulation needs to be driven by a number of modules that model certain aspects of the agents senses which is, especially for the visual sense, demanding in many aspects. The main part of this thesis will deal with the learning and modeling of sensorimotor patterns which represents an essential prerequisite for internal simulation. We present an efficient adaptive model for the prediction of optical flow patterns that occur during eye movements: This model enables the agent to transform its current view according to a covert motor command to virtually fixate a given point within its visual field. The model is further simplified based on a geometric analysis of the problem. This geometric model also serves as a solution to the problem of eye control. The resulting controller generates a kinematic motor command that moves the eye to a specific location within the visual field. We will investigate a neurally inspired extension of the eye control scheme that results in a higher accuracy of the controller. We will also address the problem of generating distal stimuli, i.e. views of the agent's gripper that are not present in its current view. The model we describe associates arm postures to pictorial views of the gripper. Finally, the problem of stereoptic depth perception is addressed. Here, we employ visual prediction in combination with an eye controller to generate virtually fixated views of objects in the left and right camera images. These virtually fixated views can be easily matched in order to establish correspondences. Furthermore, the motor information of the virtual fixation movement can be used to infer depth information

Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting

This paper proposes a single-shot approach for recognising clothing categories from 2.5D features. We propose two visual features, BSP (B-Spline Patch) and TSD (Topology Spatial Distances) for this task. The local BSP features are encoded by LLC (Locality-constrained Linear Coding) and fused with three different global features. Our visual feature is robust to deformable shapes and our approach is able to recognise the category of unknown clothing in unconstrained and random configurations. We integrated the category recognition pipeline with a stereo vision system, clothing instance detection, and dual-arm manipulators to achieve an autonomous sorting system. To verify the performance of our proposed method, we build a high-resolution RGBD clothing dataset of 50 clothing items of 5 categories sampled in random configurations (a total of 2,100 clothing samples). Experimental results show that our approach is able to reach 83.2\% accuracy while classifying clothing items which were previously unseen during training. This advances beyond the previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach in an autonomous robot sorting system, in which the robot recognises a clothing item from an unconstrained pile, grasps it, and sorts it into a box according to its category. Our proposed sorting system achieves reasonable sorting success rates with single-shot perception.Comment: 9 pages, accepted by IROS201

Advances in Intelligent Robotics and Collaborative Automation

This book provides an overview of a series of advanced research lines in robotics as well as of design and development methodologies for intelligent robots and their intelligent components. It represents a selection of extended versions of the best papers presented at the Seventh IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications IDAACS 2013 that were related to these topics. Its contents integrate state of the art computational intelligence based techniques for automatic robot control to novel distributed sensing and data integration methodologies that can be applied to intelligent robotics and automation systems. The objective of the text was to provide an overview of some of the problems in the field of robotic systems and intelligent automation and the approaches and techniques that relevant research groups within this area are employing to try to solve them.The contributions of the different authors have been grouped into four main sections:• Robots• Control and Intelligence• Sensing• Collaborative automationThe chapters have been structured to provide an easy to follow introduction to the topics that are addressed, including the most relevant references, so that anyone interested in this field can get started in the area

Surrogate models for the design and control of soft mechanical systems

Soft mechanical systems constitute stretchable skins, tissue-like appendages, fibers and fluids, and utilize material deformation to transmit forces or motion to perform a mechanical task. These systems may possess infinite degrees of freedom with finite modes of actuation and sensing, and this creates challenges in modeling, design and controls. This thesis explores the use of surrogate models to approximate the complex physics between the inputs and outputs of a soft mechanical system composed of a ubiquitous soft building block known as Fiber Reinforced Elastomeric Enclosures (FREEs). Towards this the thesis is divided into two parts, with the first part investigating reduced order models for design and the other part investigating reinforcement learning (RL) framework for controls. The reduced order models for design is motivated by the need for repeated quick and accurate evaluation of the system performance. Two mechanics-based models are investigated: (a) A Pseudo Rigid Body model (PRB) with lumped spring and link elements, and (b) a Homogenized Strain Induced (HIS) model that can be implemented in a finite element framework. The parameters of the two models are fit either directly with experiments on FREE prototypes or with a high fidelity robust finite element model. These models capture fundamental insights on design by isolating a fundamental dyad building block of contracting FREEs that can be configured to either obtain large stroke (displacement) or large force. Furthermore, the thesis proposes a novel building block-based design framework where soft FREE actuators are systematically integrated in a compliant system to yield a given motion requirement. The design process is deemed useful in shape morphing adaptive structures such as airfoils, soft skins, and wearable devices for the upper extremities. Soft robotic systems such as manipulators are challenging to control because of their flexibility, ability to undergo large spatial deformations that are dependent on the external load. The second part of this work focuses on the control of a unique soft continuum arm known as the BR2 manipulator using reinforcement learning (RL). The BR2 manipulator has a unique parallel architecture with a combined bending mode and torsional modes, and its inherent asymmetric nature precludes well defined analytical models to capture its forward kinematics. Two RL-based frameworks are evaluated on the BR2 manipulator and their efficacy in carrying out position control using simple state feedback is reported in this work. The results highlight external load invariance of the learnt control policies which is a significant factor for deformable continuum arms for applications involving pick and place operations. The manipulator is deemed useful in berry harvesting and other agricultural applications

Model-based and Model-Free Robot Control : A Review

Robot control is one of the key aspects of robotics research. Models are essential tools in robotics, such as the robot’s own body dynamics and kinematics models, actuator/motor models, and the models of external controllable objects. In this paper, we review the latest advances in model-based and model-free ap-proaches with a strong focus on robot control. Based on the designed search strategy, several prevailing control approaches are classified and discussed ac-cording to their control strategies. An insight into the gripper control is also explored. Then the research problems and applicability of the control methods are discussed by investigating their merits and demerits. Based on the discussion, we summarize the challenges and future research trends of robot control