Vision-Based Online Adaptation of Motion Primitives to Dynamic Surfaces: Application to an Interactive Robotic Wiping Task

Dometios, Athanasios; Zhou, You; Papageorgiou, Xanthi S.; Tzafestas, Costas S.; Asfour, Tamim

doi:10.1109/lra.2018.2800031

Cited by 31 publications

(18 citation statements)

References 22 publications

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“…Another important aspect of the motion planning method is the straightforward combination with other motion planning techniques, which allow for the incorporation of imitation learning techniques. More specifically in [62] we integrated a leaderfollower framework of motion primitives (CC-DMP) [63] with a vision-based motion planning method to adapt the reference path of a robots end-effector and allow the execution of washing actions. This system incorporates clinical carers expertise by producing motions, which are learned by demonstration, using data from the publicly available KIT whole-body motion database [64].…”

Section: Real-time Robotic Motion Adaptationmentioning

confidence: 99%

I-Support: A robotic platform of an assistive bathing robot for the elderly population

Zlatintsi

Dometios

Kardaris

et al. 2020

Robotics and Autonomous Systems

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In this paper we present a prototype integrated robotic system, the I-Support bathing robot, that aims at supporting new aspects of assisted daily-living activities on a real-life scenario. The paper focuses on describing and evaluating key novel technological features of the system, with the emphasis on cognitive human-robot interaction modules and their evaluation through a series of clinical validation studies. The I-Support project on its whole has envisioned the development of an innovative, modular, ICTsupported service robotic system that assists frail seniors to safely and independently complete an entire sequence of physically and cognitively demanding bathing tasks, such as properly washing their back and their lower limbs. A variety of innovative technologies have been researched and a set of advanced modules of sensing, cognition, actuation and control have been developed and seamlessly integrated to enable the system to adapt to the target population abilities. These technologies include: human activity monitoring and recognition, adaptation of a motorized chair for safe transfer of the elderly in and out the bathing cabin, a context awareness system that provides full environmental awareness, as well as a prototype soft robotic arm and a set of user-adaptive robot motion planning and control algorithms. This paper focuses in particular on the multimodal action recognition system, developed to monitor, analyze and predict user actions with a high level of accuracy and detail in real-time, which are then interpreted as robotic tasks. In the same framework, the analysis of human actions that have become available through the project's multimodal audio-gestural dataset, has led to the successful modelling of Human-Robot Communication, achieving an effective and natural interaction between users and the assistive robotic platform. In order to evaluate the I-Support system, two multinational validation studies were conducted under realistic operating conditions in two clinical pilot sites. Some of the findings of these studies are presented and analysed in the paper, showing good results in terms of: (i) high acceptability regarding the system usability by this particularly challenging target group, the elderly end-users, and (ii) overall task effectiveness of the system in different operating modes.

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Section: Real-time Robotic Motion Adaptationmentioning

confidence: 99%

I-Support: A robotic platform of an assistive bathing robot for the elderly population

Zlatintsi

Dometios

Kardaris

et al. 2020

Robotics and Autonomous Systems

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“…137 To overcome these issues, the CC-DMP and TP-DMP approaches were developed for wiping and sweeping tasks. 71,74 Another approach of LfD, GMM/ GMR, is used to compute an average trajectory from a set of human demonstrations and then reproduce new trajectories for cleaning tasks. However, GMM is not able to generate all cleaning trajectories.…”

Section: Discussionmentioning

confidence: 99%

“…The extension of CC-DMP is represented with a vision-based controller to adopt the reference path of a robot’s end-effector to allow wiping of a dynamic whiteboard. 74 In addition, a task-parameterized DMP (TP-DMP) approach is exploited with adaptive motion encoding to a sweeping task based on a few demonstrations. 71 The authors extracted the task parameter (e.g.…”

Section: Control Approach For Cleaning Tasksmentioning

confidence: 99%

Control strategies for cleaning robots in domestic applications: A comprehensive review

Kim

Mishra

Limosani

et al. 2019

International Journal of Advanced Robotic Systems

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Service robots are built and developed for various applications to support humans as companion, caretaker, or domestic support. As the number of elderly people grows, service robots will be in increasing demand. Particularly, one of the main tasks performed by elderly people, and others, is the complex task of cleaning. Therefore, cleaning tasks, such as sweeping floors, washing dishes, and wiping windows, have been developed for the domestic environment using service robots or robot manipulators with several control approaches. This article is primarily focused on control methodology used for cleaning tasks. Specifically, this work mainly discusses classical control and learning-based controlled methods. The classical control approaches, which consist of position control, force control, and impedance control , are commonly used for cleaning purposes in a highly controlled environment. However, classical control methods cannot be generalized for cluttered environment so that learning-based control methods could be an alternative solution. Learning-based control methods for cleaning tasks can encompass three approaches: learning from demonstration (LfD), supervised learning (SL), and reinforcement learning (RL). These control approaches have their own capabilities to generalize the cleaning tasks in the new environment. For example, LfD, which many research groups have used for cleaning tasks, can generate complex cleaning trajectories based on human demonstration. Also, SL can support the prediction of dirt areas and cleaning motion using large number of data set. Finally, RL can learn cleaning actions and interact with the new environment by the robot itself. In this context, this article aims to provide a general overview of robotic cleaning tasks based on different types of control methods using manipulator. It also suggest a description of the future directions of cleaning tasks based on the evaluation of the control approaches.

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“…A complex motion can be generated by combining several MPs and a path trajectory. The popular MP representations include dynamic movement primitives (DMPs) [22] and GMMs [4], [5], [19], [7]. In [22], a complex motion sequence involves MPs and a moving path.…”

Section: Introductionmentioning

confidence: 99%

“…The popular MP representations include dynamic movement primitives (DMPs) [22] and GMMs [4], [5], [19], [7]. In [22], a complex motion sequence involves MPs and a moving path. The KIT whole-body human motion database [23] is used to train a model and then the robot can learn the trajectory of a wiping task and wipe a subject's back while showering.…”

Section: Introductionmentioning

confidence: 99%

Motion Imitation and Augmentation System for a Six Degrees of Freedom Dual-Arm Robot

Liu

Liang

et al. 2019

IEEE Access

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A motion imitation and augmentation system comprising an imitation system, a self-collision avoidance system, and a motion augmented system was proposed in this study. The imitation system captures the human skeleton by using an RGB-D camera and a method involving forward kinematics and the space vector method was developed and integrated to map the human joint angles to the motor angles of the robot's arm. To improve safety of the system, a self-collision avoidance system was incorporated so that the various parts of the robot do not collide with each other while imitating human motions. The imitated motion was recorded and stored in a motion dataset as a reference motion. To address different situations and objects, the reference motion was adjusted using the motion augmented system in which both the adjusted trajectory and its corresponding motor angles were generated using the integrated adaptive constricted particle swarm optimization algorithm. So that, the adjusted motion can adapt to the real situation while maintaining similarity with the reference motion. Experiments conducted in practical situations demonstrated the efficiency of the motion imitation and augmentation system. The robot can mimic a reference motion by using its vision and can generate a suitable motion based on the imitated motion for coping with different objects and situations.

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Vision-Based Online Adaptation of Motion Primitives to Dynamic Surfaces: Application to an Interactive Robotic Wiping Task

Cited by 31 publications

References 22 publications

I-Support: A robotic platform of an assistive bathing robot for the elderly population

I-Support: A robotic platform of an assistive bathing robot for the elderly population

Control strategies for cleaning robots in domestic applications: A comprehensive review

Motion Imitation and Augmentation System for a Six Degrees of Freedom Dual-Arm Robot

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