This work introduces Human-Agent Transfer (HAT), a method that combines transfer learning, learning from demonstration and reinforcement learning to achieve rapid learning and high performance in complex domains. Using experiments in a simulated robot soccer domain, we show that human demonstrations can be transferred into a baseline policy for an agent, and reinforcement learning can be used to significantly improve policy performance. These results are an important initial step that suggest that agents can not only quickly learn to mimic human actions, but that they can also learn to surpass the abilities of the teacher. 1.

Accurate models of the robot dynamics allow the design of significantly more precise, energy-efficient and more compliant computed torque control for robots. However, in some cases the accuracy of rigid-body models does not suffice for sound control performance due to unmodeled nonlinearities such as hydraulic cables, complex friction, or actuator dynamics. In such cases, learning the models from data poses an interesting alternative and estimating the dynamics model using regression techniques becomes an important problem. However, the most accurate regression methods, e.g. Gaussian processes regression (GPR) and support vector regression (SVR), suffer from exceptional high computational complexity which prevents their usage for large numbers of samples or online learning to date. We proposed an approximation to the standard GPR using local Gaussian processes models inspired by [1], [2]. Due to reduced computational cost, local Gaussian processes (LGP) is capable for an online learning. Comparisons with other nonparametric regressions, e.g. standard GPR, ν-SVR and locally weighted projection regression (LWPR), show that LGP has higher accuracy than LWPR and close to the performance of standard GPR and ν-SVR while being sufficiently fast for online learning.

the date of receipt and acceptance should be inserted later Abstract Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot’s own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.

Abstract. Human behavior is complex, but structured along individual and social lines. Robotic systems interacting with people in uncontrolled environments need capabilities to correctly interpret, predict and respond to human behaviors. This paper discusses the scientific, technological and application challenges that arise from the mutual interaction of robotics and computational human behavior understanding. We supply a short survey of the area to provide a contextual framework and describe the most recent research in this area. 1

Abstract—In teleoperation, the user’s input is mapped onto the robot via a motion retargetting function. This function must differ between robots because of their different kinematics, between users because of their different preferences, and even between tasks that the users perform with the robot. Our work enables users to customize this retargetting function, and achieve any of these required differences. In our approach, the robot starts with an initial function. As the user teleoperates the robot, he can pause and provide example correspondences, which instantly update the retargetting function. We select the algorithm underlying these updates by formulating the problem as an instance of online function approximation. The problem’s requirements, as well as the semantics and constraints of motion retargetting, lead to an extension of Online Learning with Kernel Machines in which the width of the kernel can vary. Our central hypothesis is that this method enables users to train retargetting functions to good outcomes. We validate this hypothesis in a user study, which also reveals the importance of providing users with tools to verify their examples: much like an actor needs a mirror to verify his pose, a user needs to verify his input before providing an example. We conclude with a demonstration from an expert user that shows the potential of the method for achieving more sophisticated customization that makes particular tasks easier to complete, once users get expertise with the system. x q

Abstract—A key requirement for seamless human-robot collaboration is for the robot to make its intentions clear to its human collaborator. A collaborative robot’s motion must be legible, or intent-expressive. Legibility is often described in the literature as and effect of predictable, unsurprising, or expected motion. Our central insight is that predictability and legibility are fundamentally different and often contradictory properties of motion. We develop a formalism to mathematically define and distinguish predictability and legibility of motion. We formalize the two based on inferences between trajectories and goals in opposing directions, drawing the analogy to action interpretation in psychology. We then propose mathematical models for these inferences based on optimizing cost, drawing the analogy to the principle of rational action. Our experiments validate our formalism’s prediction that predictability and legibility can contradict, and provide support for our models. Our findings indicate that for robots to seamlessly collaborate with humans, they must change the way they plan their motion. Keywords—human-robot collaboration, motion planning, trajectory optimization, formalism, manipulation, action interpretation I.