Petar Kormushev

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A method to learn and reproduce robot force interactions in a Human-Robot Interaction setting is proposed. The method allows a robotic manipulator to learn to perform tasks which require exerting forces on external objects by interacting with a human operator in an unstructured environment. This is achieved by learning two aspects of a task: positional and(More)
This paper reports on development of an open source dynamic simulator for the Compliant huMANoid robot, COMAN. The key advantages of this simulator are: it generates efficient symbolic dynamical equations of the robot with high degrees of freedom, it includes a user-defined model of the actuator dynamics (the passive elasticity and the DC motor equations),(More)
We present an approach allowing a robot to acquire new motor skills by learning the couplings across motor control variables. The demonstrated skill is first encoded in a compact form through a modified version of Dynamic Movement Primitives (DMP) which encapsulates correlation information. Expectation-Maximization based Reinforcement Learning is then used(More)
In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to learn, improve, adapt and reproduce tasks with dynamically changing constraints based on exploration and autonomous learning. We give a summary of the state-of-the-art of reinforcement learning in the context of robotics, in terms of both algorithms and policy(More)
Legged robots are uniquely privileged over their wheeled counterparts in their potential to access rugged terrain. However, designing walking gaits by hand for legged robots is a difficult and time-consuming process, so we seek algorithms for learning such gaits to automatically using real world experimentation. Numerous previous studies have examined a(More)
The democratization of robotics technology and the development of new actuators progressively bring robots closer to humans. The applications that can now be envisaged drastically contrast with the requirements of industrial robots. In standard manufacturing settings, the criterions used to assess performance are usually related to the robot’s accuracy,(More)
We present a novel robot learning approach based on visual perception that allows a robot to acquire new skills by observing a demonstration from a tutor. Unlike most existing learning from demonstration approaches, where the focus is placed on the trajectories, in our approach the focus is on achieving a desired goal configuration of objects relative to(More)
This paper investigates learning approaches for discovering fault-tolerant control policies to overcome thruster failures in Autonomous Underwater Vehicles (AUV). The proposed approach is a model-based direct policy search that learns on an on-board simulated model of the vehicle. When a fault is detected and isolated the model of the AUV is reconfigured(More)
We present an integrated approach allowing the humanoid robot iCub to learn the skill of archery. After being instructed how to hold the bow and release the arrow, the robot learns by itself to shoot the arrow in such a way that it hits the center of the target. Two learning algorithms are proposed and compared to learn the bi-manual skill: one with(More)
of the displayed information, the importance and the degree of certainty of the information should be communicated along with the main content. The proposed intent expression system aims at conveying this additional information using the eye robot system. Eye motions are represented as the states in a pleasure-arousal space model. Changes in the model state(More)