Robot Learning Dual-Arm Manipulation Tasks by Trial-and-Error and Multiple Human Demonstrations

In robotics, there is a need of an interactive and expedite learning method as experience is expensive. In this research, we propose two different methods to make a humanoid robot learn manipulation tasks: Learning by trial-and-error, and Learning from demonstrations. Just like the way a child learns a new task assigned to him by trying all possible alternatives and further learning from his mistakes, the robot learns in the same manner in learning by trial-and error. We used Q-learning algorithm, in which the robot tries all the possible ways to do a task and creates a matrix that consists of Q-values based on the rewards it received for the actions performed. Using this method, the robot was made to learn dance moves based on a music track. Robot Learning from Demonstrations (RLfD) enable a human user to add new capabilities to a robot in an intuitive manner without explicitly reprogramming it. In this method, the robot learns skill from demonstrations performed by a human teacher. The robot extracts features from each demonstration called as key-points and learns a model of the demonstrated task or trajectory using Hidden Markov Model (HMM). The learned model is further used to produce a generalized trajectory. In the end, we discuss the differences between two developed systems and make conclusions based on the experiments performed

Autonomous Weapons and the Nature of Law and Morality: How Rule-of-Law-Values Require Automation of the Rule of Law

While Autonomous Weapons Systems have obvious military advantages, there are prima facie moral objections to using them. By way of general reply to these objections, I point out similarities between the structure of law and morality on the one hand and of automata on the other. I argue that these, plus the fact that automata can be designed to lack the biases and other failings of humans, require us to automate the formulation, administration, and enforcement of law as much as possible, including the elements of law and morality that are operated by combatants in war. I suggest that, ethically speaking, deploying a legally competent robot in some legally regulated realm is not much different from deploying a more or less well-armed, vulnerable, obedient, or morally discerning soldier or general into battle, a police officer onto patrol, or a lawyer or judge into a trial. All feature automaticity in the sense of deputation to an agent we do not then directly control. Such relations are well understood and well-regulated in morality and law; so there is not much challenging philosophically in having robots be some of these agents — excepting the implications of the limits of robot technology at a given time for responsible deputation. I then consider this proposal in light of the differences between two conceptions of law. These are distinguished by whether each conception sees law as unambiguous rules inherently uncontroversial in each application; and I consider the prospects for robotizing law on each. Likewise for the prospects of robotizing moral theorizing and moral decision-making. Finally I identify certain elements of law and morality, noted by the philosopher Immanuel Kant, which robots can participate in only upon being able to set ends and emotionally invest in their attainment. One conclusion is that while affectless autonomous devices might be fit to rule us, they would not be fit to vote with us. For voting is a process for summing felt preferences, and affectless devices would have none to weigh into the sum. Since they don't care which outcomes obtain, they don't get to vote on which ones to bring about

Choreographic and Somatic Approaches for the Development of Expressive Robotic Systems

As robotic systems are moved out of factory work cells into human-facing environments questions of choreography become central to their design, placement, and application. With a human viewer or counterpart present, a system will automatically be interpreted within context, style of movement, and form factor by human beings as animate elements of their environment. The interpretation by this human counterpart is critical to the success of the system's integration: knobs on the system need to make sense to a human counterpart; an artificial agent should have a way of notifying a human counterpart of a change in system state, possibly through motion profiles; and the motion of a human counterpart may have important contextual clues for task completion. Thus, professional choreographers, dance practitioners, and movement analysts are critical to research in robotics. They have design methods for movement that align with human audience perception, can identify simplified features of movement for human-robot interaction goals, and have detailed knowledge of the capacity of human movement. This article provides approaches employed by one research lab, specific impacts on technical and artistic projects within, and principles that may guide future such work. The background section reports on choreography, somatic perspectives, improvisation, the Laban/Bartenieff Movement System, and robotics. From this context methods including embodied exercises, writing prompts, and community building activities have been developed to facilitate interdisciplinary research. The results of this work is presented as an overview of a smattering of projects in areas like high-level motion planning, software development for rapid prototyping of movement, artistic output, and user studies that help understand how people interpret movement. Finally, guiding principles for other groups to adopt are posited.Comment: Under review at MDPI Arts Special Issue "The Machine as Artist (for the 21st Century)" http://www.mdpi.com/journal/arts/special_issues/Machine_Artis

Robot learning from demonstration of force-based manipulation tasks

One of the main challenges in Robotics is to develop robots that can interact with humans in a natural way, sharing the same dynamic and unstructured environments. Such an interaction may be aimed at assisting, helping or collaborating with a human user. To achieve this, the robot must be endowed with a cognitive system that allows it not only to learn new skills from its human partner, but also to refine or improve those already learned. In this context, learning from demonstration appears as a natural and userfriendly way to transfer knowledge from humans to robots. This dissertation addresses such a topic and its application to an unexplored field, namely force-based manipulation tasks learning. In this kind of scenarios, force signals can convey data about the stiffness of a given object, the inertial components acting on a tool, a desired force profile to be reached, etc. Therefore, if the user wants the robot to learn a manipulation skill successfully, it is essential that its cognitive system is able to deal with force perceptions. The first issue this thesis tackles is to extract the input information that is relevant for learning the task at hand, which is also known as the what to imitate? problem. Here, the proposed solution takes into consideration that the robot actions are a function of sensory signals, in other words the importance of each perception is assessed through its correlation with the robot movements. A Mutual Information analysis is used for selecting the most relevant inputs according to their influence on the output space. In this way, the robot can gather all the information coming from its sensory system, and the perception selection module proposed here automatically chooses the data the robot needs to learn a given task. Having selected the relevant input information for the task, it is necessary to represent the human demonstrations in a compact way, encoding the relevant characteristics of the data, for instance, sequential information, uncertainty, constraints, etc. This issue is the next problem addressed in this thesis. Here, a probabilistic learning framework based on hidden Markov models and Gaussian mixture regression is proposed for learning force-based manipulation skills. The outstanding features of such a framework are: (i) it is able to deal with the noise and uncertainty of force signals because of its probabilistic formulation, (ii) it exploits the sequential information embedded in the model for managing perceptual aliasing and time discrepancies, and (iii) it takes advantage of task variables to encode those force-based skills where the robot actions are modulated by an external parameter. Therefore, the resulting learning structure is able to robustly encode and reproduce different manipulation tasks. After, this thesis goes a step forward by proposing a novel whole framework for learning impedance-based behaviors from demonstrations. The key aspects here are that this new structure merges vision and force information for encoding the data compactly, and it allows the robot to have different behaviors by shaping its compliance level over the course of the task. This is achieved by a parametric probabilistic model, whose Gaussian components are the basis of a statistical dynamical system that governs the robot motion. From the force perceptions, the stiffness of the springs composing such a system are estimated, allowing the robot to shape its compliance. This approach permits to extend the learning paradigm to other fields different from the common trajectory following. The proposed frameworks are tested in three scenarios, namely, (a) the ball-in-box task, (b) drink pouring, and (c) a collaborative assembly, where the experimental results evidence the importance of using force perceptions as well as the usefulness and strengths of the methods

Interactive Robot Task Learning: Human Teaching Proficiency with Different Feedback Approaches

The deployment of versatile robot systems in diverse environments requires intuitive approaches for humans to flexibly teach them new skills. In our present work, we investigate different user feedback types to teach a real robot a new movement skill. We compare feedback as star ratings on an absolute scale for single roll-outs versus preference-based feedback for pairwise comparisons with respective optimization algorithms (i.e., a variation of co-variance matrix adaptation -evolution strategy (CMA-ES) and random optimization) to teach the robot the game of skill cup-and-ball. In an experimental investigation with users, we investigated the influence of the feedback type on the user experience of interacting with the different interfaces and the performance of the learning systems. While there is no significant difference for the subjective user experience between the conditions, there is a significant difference in learning performance. The preference-based system learned the task quicker, but this did not influence the users’ evaluation of it. In a follow-up study, we confirmed that the difference in learning performance indeed can be attributed to the human users’ performance

Cognition, Affects et Interaction

International audienceCet ouvrage rassemble les travaux d’études et de recherche effectués dans le cadre du cours «Cognition, Affects et Interaction » que nous avons animé au 1er semestre 2015-2016. Cette deuxième édition de cours poursuit le principe inauguré en 2014 : aux cours magistraux donnés sur la thématique "Cognition, Interaction & Affects" qui donnent les outils méthodologiques des composantes de l’interaction socio-communicative, nous avons couplé une introduction à la robotique sociale et un apprentissage actif par travail de recherche en binômes. Le principe de ces travaux d’études et de recherche est d’effectuer une recherche bibliographique et de rédiger un article de synthèse sur un aspect de l’interaction homme-robot. Si plusieurs sujets ont été proposés aux étudiants en début d’année, certains binômes ont choisi d’aborder l’interaction avec un angle original qui reflète souvent les trajectoires de formation variés des étudiants en sciences cognitives (ingénierie, sociologie, psychologie, etc). Le résultat dépasse nos espérances : le lecteur trouvera une compilation d’articles argumentés de manière solide, rédigés de manière claire et présentés avec soin. Ces premières «publications» reflètent les capacités singulières de réflexion de cette promotion en nette augmentation par rapport à l’année précédente. Nous espérons que cette série d’ouvrages disponibles sous HAL puisse servir de point d’entrée à des étudiants ou chercheurs intéressés à explorer ce champ de recherches pluri-disciplinaire

Remedies for Robots

What happens when artificially intelligent robots misbehave? The question is not just hypothetical. As robotics and artificial intelligence systems increasingly integrate into our society, they will do bad things. We seek to explore what remedies the law can and should provide once a robot has caused harm. Remedies are sometimes designed to make plaintiffs whole by restoring them to the condition they would have been in “but for” the wrong. But they can also contain elements of moral judgment, punishment, and deterrence. In other instances, the law may order defendants to do (or stop doing) something unlawful or harmful. Each of these goals of remedies law, however, runs into difficulties when the bad actor in question is neither a person nor a corporation but a robot. We might order a robot—or, more realistically, the designer or owner of the robot—to pay for the damages it causes. But it turns out to be much harder for a judge to “order” a robot, rather than a human, to engage in or refrain from certain conduct. Robots can’t directly obey court orders not written in computer code. And bridging the translation gap between natural language and code is often harder than we might expect. This is particularly true of modern artificial intelligence techniques that empower machines to learn and modify their decision-making over time. If we don’t know how the robot “thinks,” we won’t know how to tell it to behave in a way likely to cause it to do what we actually want it to do. Moreover, if the ultimate goal of a legal remedy is to encourage good behavior or discourage bad behavior, punishing owners or designers for the behavior of their robots may not always make sense—if only for the simple reason that their owners didn’t act wrongfully in any meaningful way. The same problem affects injunctive relief. Courts are used to ordering people and companies to do (or stop doing) certain things, with a penalty of contempt of court for noncompliance. But ordering a robot to abstain from certain behavior won’t be trivial in many cases. And ordering it to take affirmative acts may prove even more problematic. In this Article, we begin to think about how we might design a system of remedies for robots. Robots will require us to rethink many of our current doctrines. They also offer important insights into the law of remedies we already apply to people and corporations

Robotics in Germany and Japan

This book comprehends an intercultural and interdisciplinary framework including current research fields like Roboethics, Hermeneutics of Technologies, Technology Assessment, Robotics in Japanese Popular Culture and Music Robots. Contributions on cultural interrelations, technical visions and essays are rounding out the content of this book