An analysis on controlling humanoid robot arm using Robot Operating System (ROS)

Humanoid robots are extensively discussed in modern days. The movement task and manipulation of Humanoid Robots is examined based on mobility of platforms and control of the arm. This project describes a robotic arm that is analogous to an arm of a human being. Some important parameters to be considered are reachability, stability and manipulability. This thesis aims at adapting a humanoid robot arm for performing movement operation that can be used for various purposes. The proposed robot arm has 3 motors on the left arm and 3 motors on the right arm thereby constituting a total of 6 motors. This operation can be achieved by the use of sensor like ultrasonic sensor. Here Beaglebone Black, an open source linux based controller board is used. The Beaglebone Black acts as the main controller for the entire system. A research is also being made to implement the robotic arm using Robot Operating System (ROS) platform. ROS is preferred since it is modular, simple and easy to use tools for development, it provides good hardware support, lots of algorithms are implemented together as package, etc

Towards observable haptics: Novel sensors for capturing tactile interaction patterns

Kõiva R. Towards observable haptics: Novel sensors for capturing tactile interaction patterns. Bielefeld: Bielefeld University; 2014.Touch is one of the primary senses humans use when performing coordinated interaction, but the lack of a sense of touch in the majority of contemporary interactive technical systems, such as robots, which operate in non-deterministic environments, results in interactions that can at best be described as clumsy. Observing human haptics and extracting the salient information from the gathered data is not only relevant if we are to try to understand the involved underlying cognitive processes, but should also provide us with significant clues to design future intelligent interactive systems. Such systems could one day help to take the burden of tedious tasks off our hands in a similar fashion to how industrial robots revolutionized manufacturing. The aim of the work in this thesis was to provide significant advancements in tactile sensing technology, and thus move us a step closer to realizing this goal. The contributions contained herein can be broken into two major parts. The first part investigates capturing interaction patterns in humans with the goals of better understanding manual intelligence and improving the lives of hand amputees, while the second part is focused on augmenting technical systems with a sense of touch. tacTiles, a wireless tactile sensitive surface element attached to a deformable textile, was developed to capture human full-body interactions with large surfaces we come into contact with in our daily lives, such as floors, chairs, sofas or other furniture. The Tactile Dataglove, iObject and the Tactile Pen were developed especially to observe human manual intelligence. Whereas iObject allows motion sensing and a higher definition tactile signal to be captured than the Tactile Dataglove (220 tactile cells in the first iObject prototype versus 54 cells in the glove), the wearable glove makes haptic interactions with arbitrary objects observable. The Tactile Pen was designed to measure grip force during handwriting in order to better facilitate therapeutic treatment assessments. These sensors have already been extensively used by various research groups, including our own, to gain a better understanding of human manual intelligence. The Finger-Force-Linear-Sensor and the Tactile Bracelet are two novel sensors that were developed to facilitate more natural control of dexterous multi Degree-of-Freedom (DOF) hand prostheses. The Finger-Force-Linear-Sensor is a very accurate bidirectional single finger force ground-truth measurement device that was designed to enable testing and development of single finger forces and muscle activations mapping algorithms. The Tactile Bracelet was designed with the goal to provide a more robust and intuitive means of control for multi-DOF hand prostheses by measuring the muscle bulgings of the remnant muscles of lower arm amputees. It is currently in development and will eventually cover the complete forearm circumference with high spatial resolution tactile sensitive surfaces. An experiment involving a large number of lower arm amputees has already been planned. The Modular flat tactile sensor system, the Fabric-based touch sensitive artificial skin and the 3D shaped tactile sensor were developed to cover and to add touch sensing capabilities to the surfaces of technical systems. The rapid augmentation of systems with a sense of touch was the main goal of the modular flat tactile sensor system. The developed sensor modules can be used alone or in an array to form larger tactile sensitive surfaces such as tactile sensitive tabletops. As many robots have curved surfaces, using flat rigid modules severely limits the areas that can be covered with tactile sensors. The Fabric-based tactile sensor, originally developed to form a tactile dataglove for human hands, can with minor modifications also function as an artificial skin for technical systems. Finally, the 3D shaped tactile sensor based on Laser-Direct-Structuring technology is a novel tactile sensor that has a true 3D shape and provides high sensitivity and a high spatial resolution. These sensors take us further along the path towards creating general purpose technical systems that in time can be of great help to us in our daily lives. The desired tactile sensor characteristics differ significantly according to which haptic interaction patterns we wish to measure. Large tactile sensor arrays that are used to capture full body haptic interactions with floors and upholstered furniture, or that are designed to cover large areas of technical system surfaces, need to be scalable, have low power consumption and should ideally have a low material cost. Two examples of such sensors are tacTiles and the Fabric-based sensor for curved surfaces. At the other end of the tactile sensor development spectrum, if we want to observe manual interactions, high spatial and temporal resolution are crucial to enable the measurement of fine grasping and manipulation actions. Our fingertips contain the highest density area of mechanoreceptors, the organs that sense mechanical pressure and distortions. Thus, to construct biologically inspired anthropomorphic robotic hands, the artificial tactile sensors for the fingertips require similar high-fidelity sensors with surfaces that are curved under small bending radii in 2 dimensions, have high spatial densities, while simultaneously providing high sensitivity. With the fingertip tactile sensor, designed to fit the Shadow Robot Hands' fingers, I show that such sensors can indeed be constructed in the 3D-shaped high spatial resolution tactile sensor section of my thesis. With my work I have made a significant contribution towards making haptics more observable. I achieved this by developing a high number of novel tactile sensors that are usable, give a deeper insight into human haptic interactions, have great potential to help amputees and that make technical systems, such as robots, more capable

Probabilistic Models of Motor Production

N. Bernstein defined the ability of the central neural system (CNS) to control many degrees of freedom of a physical body with all its redundancy and flexibility as the main problem in motor control. He pointed at that man-made mechanisms usually have one, sometimes two degrees of freedom (DOF); when the number of DOF increases further, it becomes prohibitively hard to control them. The brain, however, seems to perform such control effortlessly. He suggested the way the brain might deal with it: when a motor skill is being acquired, the brain artificially limits the degrees of freedoms, leaving only one or two. As the skill level increases, the brain gradually "frees" the previously fixed DOF, applying control when needed and in directions which have to be corrected, eventually arriving to the control scheme where all the DOF are "free". This approach of reducing the dimensionality of motor control remains relevant even today. One the possibles solutions of the Bernstetin's problem is the hypothesis of motor primitives (MPs) - small building blocks that constitute complex movements and facilitite motor learnirng and task completion. Just like in the visual system, having a homogenious hierarchical architecture built of similar computational elements may be beneficial. Studying such a complicated object as brain, it is important to define at which level of details one works and which questions one aims to answer. David Marr suggested three levels of analysis: 1. computational, analysing which problem the system solves; 2. algorithmic, questioning which representation the system uses and which computations it performs; 3. implementational, finding how such computations are performed by neurons in the brain. In this thesis we stay at the first two levels, seeking for the basic representation of motor output. In this work we present a new model of motor primitives that comprises multiple interacting latent dynamical systems, and give it a full Bayesian treatment. Modelling within the Bayesian framework, in my opinion, must become the new standard in hypothesis testing in neuroscience. Only the Bayesian framework gives us guarantees when dealing with the inevitable plethora of hidden variables and uncertainty. The special type of coupling of dynamical systems we proposed, based on the Product of Experts, has many natural interpretations in the Bayesian framework. If the dynamical systems run in parallel, it yields Bayesian cue integration. If they are organized hierarchically due to serial coupling, we get hierarchical priors over the dynamics. If one of the dynamical systems represents sensory state, we arrive to the sensory-motor primitives. The compact representation that follows from the variational treatment allows learning of a motor primitives library. Learned separately, combined motion can be represented as a matrix of coupling values. We performed a set of experiments to compare different models of motor primitives. In a series of 2-alternative forced choice (2AFC) experiments participants were discriminating natural and synthesised movements, thus running a graphics Turing test. When available, Bayesian model score predicted the naturalness of the perceived movements. For simple movements, like walking, Bayesian model comparison and psychophysics tests indicate that one dynamical system is sufficient to describe the data. For more complex movements, like walking and waving, motion can be better represented as a set of coupled dynamical systems. We also experimentally confirmed that Bayesian treatment of model learning on motion data is superior to the simple point estimate of latent parameters. Experiments with non-periodic movements show that they do not benefit from more complex latent dynamics, despite having high kinematic complexity. By having a fully Bayesian models, we could quantitatively disentangle the influence of motion dynamics and pose on the perception of naturalness. We confirmed that rich and correct dynamics is more important than the kinematic representation. There are numerous further directions of research. In the models we devised, for multiple parts, even though the latent dynamics was factorized on a set of interacting systems, the kinematic parts were completely independent. Thus, interaction between the kinematic parts could be mediated only by the latent dynamics interactions. A more flexible model would allow a dense interaction on the kinematic level too. Another important problem relates to the representation of time in Markov chains. Discrete time Markov chains form an approximation to continuous dynamics. As time step is assumed to be fixed, we face with the problem of time step selection. Time is also not a explicit parameter in Markov chains. This also prohibits explicit optimization of time as parameter and reasoning (inference) about it. For example, in optimal control boundary conditions are usually set at exact time points, which is not an ecological scenario, where time is usually a parameter of optimization. Making time an explicit parameter in dynamics may alleviate this

Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

Probabilistic Models of Motor Production

N. Bernstein defined the ability of the central neural system (CNS) to control many degrees of freedom of a physical body with all its redundancy and flexibility as the main problem in motor control. He pointed at that man-made mechanisms usually have one, sometimes two degrees of freedom (DOF); when the number of DOF increases further, it becomes prohibitively hard to control them. The brain, however, seems to perform such control effortlessly. He suggested the way the brain might deal with it: when a motor skill is being acquired, the brain artificially limits the degrees of freedoms, leaving only one or two. As the skill level increases, the brain gradually "frees" the previously fixed DOF, applying control when needed and in directions which have to be corrected, eventually arriving to the control scheme where all the DOF are "free". This approach of reducing the dimensionality of motor control remains relevant even today. One the possibles solutions of the Bernstetin's problem is the hypothesis of motor primitives (MPs) - small building blocks that constitute complex movements and facilitite motor learnirng and task completion. Just like in the visual system, having a homogenious hierarchical architecture built of similar computational elements may be beneficial. Studying such a complicated object as brain, it is important to define at which level of details one works and which questions one aims to answer. David Marr suggested three levels of analysis: 1. computational, analysing which problem the system solves; 2. algorithmic, questioning which representation the system uses and which computations it performs; 3. implementational, finding how such computations are performed by neurons in the brain. In this thesis we stay at the first two levels, seeking for the basic representation of motor output. In this work we present a new model of motor primitives that comprises multiple interacting latent dynamical systems, and give it a full Bayesian treatment. Modelling within the Bayesian framework, in my opinion, must become the new standard in hypothesis testing in neuroscience. Only the Bayesian framework gives us guarantees when dealing with the inevitable plethora of hidden variables and uncertainty. The special type of coupling of dynamical systems we proposed, based on the Product of Experts, has many natural interpretations in the Bayesian framework. If the dynamical systems run in parallel, it yields Bayesian cue integration. If they are organized hierarchically due to serial coupling, we get hierarchical priors over the dynamics. If one of the dynamical systems represents sensory state, we arrive to the sensory-motor primitives. The compact representation that follows from the variational treatment allows learning of a motor primitives library. Learned separately, combined motion can be represented as a matrix of coupling values. We performed a set of experiments to compare different models of motor primitives. In a series of 2-alternative forced choice (2AFC) experiments participants were discriminating natural and synthesised movements, thus running a graphics Turing test. When available, Bayesian model score predicted the naturalness of the perceived movements. For simple movements, like walking, Bayesian model comparison and psychophysics tests indicate that one dynamical system is sufficient to describe the data. For more complex movements, like walking and waving, motion can be better represented as a set of coupled dynamical systems. We also experimentally confirmed that Bayesian treatment of model learning on motion data is superior to the simple point estimate of latent parameters. Experiments with non-periodic movements show that they do not benefit from more complex latent dynamics, despite having high kinematic complexity. By having a fully Bayesian models, we could quantitatively disentangle the influence of motion dynamics and pose on the perception of naturalness. We confirmed that rich and correct dynamics is more important than the kinematic representation. There are numerous further directions of research. In the models we devised, for multiple parts, even though the latent dynamics was factorized on a set of interacting systems, the kinematic parts were completely independent. Thus, interaction between the kinematic parts could be mediated only by the latent dynamics interactions. A more flexible model would allow a dense interaction on the kinematic level too. Another important problem relates to the representation of time in Markov chains. Discrete time Markov chains form an approximation to continuous dynamics. As time step is assumed to be fixed, we face with the problem of time step selection. Time is also not a explicit parameter in Markov chains. This also prohibits explicit optimization of time as parameter and reasoning (inference) about it. For example, in optimal control boundary conditions are usually set at exact time points, which is not an ecological scenario, where time is usually a parameter of optimization. Making time an explicit parameter in dynamics may alleviate this

Perception of Human Movement Based on Modular Movement Primitives

People can identify and understand human movement from very degraded visual information without effort. A few dots representing the position of the joints are enough to induce a vivid and stable percept of the underlying movement. Due to this ability, the realistic animation of 3D characters requires great skill. Studying the constituents of movement that looks natural would not only help these artists, but also bring better understanding of the underlying information processing in the brain. Analogous to the hurdles in animation, the efforts of roboticists reflect the complexity of motion production: controlling the many degrees of freedom of a body requires time-consuming computations. Modularity is one strategy to address this problem: Complex movement can be decomposed into simple primitives. A few primitives can conversely be used to compose a large number of movements. Many types of movement primitives (MPs) have been proposed on different levels of information processing hierarchy in the brain. MPs have mostly been proposed for movement production. Yet, modularity based on primitives might similarly enable robust movement perception. For my thesis, I have conducted perceptual experiments based on the assumption of a shared representation of perception and action based on MPs. The three different types of MPs I have investigated are temporal MPs (TMP), dynamical MPs (DMP), and coupled Gaussian process dynamical models (cGPDM). The MP-models have been trained on natural movements to generate new movements. I then perceptually validated these artificial movements in different psychophysical experiments. In all experiments I used a two-alternative forced choice paradigm, in which human observers were presented a movement based on motion-capturing data, and one generated by an MP-model. They were then asked to chose the movement which they perceived as more natural. In the first experiment I investigated walking movements, and found that, in line with previous results, faithful representation of movement dynamics is more important than good reconstruction of pose. In the second experiment I investigated the role of prediction in perception using reaching movements. Here, I found that perceived naturalness of the predictions is similar to the perceived naturalness of movements itself obtained in the first experiment. I have found that MP models are able to produce movement that looks natural, with the TMP achieving the highest perceptual scores as well highest predictiveness of perceived naturalness among the three model classes, suggesting their suitability for a shared representation of perception and action