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This paper discusses the learning of robot point-to-point motions via non-linear dynamical systems and Gaussian Mixture Regression (GMR). The novelty of the proposed approach consists in guaranteeing the stability of a learned dynamical system via Contraction theory. A contraction analysis is performed to derive sufficient conditions for the global stability of a dynamical system represented by GMR...
While teleoperation provides a possibility for a robot to operate at extreme conditions instead of a human, teleoperating a robot still demands a heavy mental workload from a human operator. Learning from demonstrations can reduce the human operator's burden by learning repetitive teleoperation tasks. However, one of challenging issues is that demonstrations via teleoperation are less consistent compared...
In this paper, an adaptive control approach is proposed for performance of constrained robot end-effector movements in presence of uncertainty. In real-world scenarios, complex physical phenomena occuring at the place of interaction may introduce nonlinearities in the system dynamics, which have to be taken into account for proper system control. We currently propose an Extremum Seeking (ES) Model...
Human-robot skill transfer has been deeply investigated from a kinematic point of view, generating various approaches to increase the robot knowledge in a simple and compact way. Nevertheless, social robotics applications require a close and active interaction with humans in a safe and natural manner. Torque controlled robots, with their variable impedance capabilities, seem a viable option toward...
Task recognition and future human activity prediction are of importance for a safe and profitable human-robot cooperation. In real scenarios, the robot has to extract this information merging the knowledge of the task with contextual information from the sensors, minimizing possible misunderstandings. In this paper, we focus on tasks that can be represented as a sequence of manipulated objects and...
Performance of constrained movements in multiple directions of a workspace simultaneously and in presence of uncertainty is a great challenge for robots. Achieving such tasks by employing control policies which are fully determined a priori and do not take into account the system uncertainty can cause undesired stress on the robot end-effector or the environment and result in poor performance. Instead,...
An algorithm which allows the robot to avoid moving obstacles and to reach the assigned goal is proposed. For this purpose, a dynamical system (DS) modulation matrix is calculated using the distance from the obstacles and their velocity, without the need of an analytical representation of the obstacles. This matrix modulates a generic first order DS, used to generate the desired path, saving the equilibrium...
Human gesture recognition is of importance for smooth and efficient human robot interaction. One of difficulties in gesture recognition is that different actors have different styles in performing even same gestures. In order to move towards more realistic scenarios, a robot is required to handle not only different users, but also different view points and noisy incomplete data from onboard sensors...
Proactive physical robotic assistance in the presence of human prediction uncertainty is a very challenging control problem. In this paper we propose a risk-sensitive optimal feedback controller for physical assistance that autonomously adapts the robot's behavior even during unknown situations. Using a probabilistic model to represent the cooperative task execution behavior and modeling the human...
Goal-directed physical assistance to the human is one of the most challenging problems in the area of human-robot interaction. Planning and learning from demonstration represent two conceptually different approaches to achieve goal-directed behavior. Here we examine the properties of a planning-based and a learning-based approach in the context of physical robotic assistance for the prototypical task...
While human behavior prediction can increase the capability of a robotic partner to generate anticipatory behavior during physical human robot interaction (pHRI), predictions in uncertain situations can lead to large disturbances for the human if they do not match the human intentions. In this paper we present a novel control concept in which the assistive control parameters are adapted to the uncertainty...
Imitation learning, also known as Programming by Demonstration, allows a non-expert user to teach complex skills to a robot. While so far researchers focused on abstracting kinematic relations, only little attention has been paid to force information. In this work we study imitation learning of human grasping skills from motion and force data. For this purpose a teleoperation system is realized that...
We present an approach for kinesthetic teaching of motion primitives for a humanoid robot. The proposed teaching method allows for iterative execution and motion refinement using a forgetting factor. During the iterative motion refinement, a confidence value specifies an area of allowed refinement around the nominal trajectory. A novel method for continuous generation of motions from a hidden Markov...
Since humanoid robots have similar body structures to humans, a humanoid robot is expected to perform various dynamic tasks including object manipulation. This research focuses on issues related to learning and performing object manipulation. Basic motion primitives for tasks are learned from observation of human's behaviors. An object manipulation task is divided into two types of motion primitives,...
In this paper, mimetic communication is extended to human-robot interaction tasks, in which physical contact transitions must be handled. The mimetic communication consists of imitation learning for learning low level motion primitives and a higher level interaction learning stage in which also the information about the human-robot contacts is included. For the imitation learning, Cartesian marker...
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