A Framework of Hybrid Force/Motion Skills Learning for Robots

Wang, Ning; Chen, Chuize; Nuovo, Alessandro Di

doi:10.1109/tcds.2020.2968056

Cited by 33 publications

(19 citation statements)

References 44 publications

(50 reference statements)

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“…Moreover, due to the coupling between each driven branch, excessive tensile force might occur, which might cause safety hazards for the subjects [39], [40]. Wang et al used a hybrid force/motion control for a learning robot to ensure its accurate movement tracking and reproduction of human force [41]. The hybrid position and force control in this study, likewise, could guarantee appropriate assistance as intended and safe human-robot interaction.…”

Section: Discussionmentioning

confidence: 94%

Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

et al. 2020

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Rehabilitation robots play an increasingly important role in the recovery of motor function for stroke. To ensure a natural physical human-robot interaction (pHRI) and enhance the active participation of subjects, it is necessary for the robots to understand the human intention and cooperate actively with humanlike characteristics. This study proposed a hybrid active control algorithm with human motion intention detection. The motion intention was defined as the desired position and velocity, which were continuously estimated according to the human upper-limb model and minimum jerk model, respectively. The motion intention was then fed into a hybrid force and position controller of an upper-limb cable driven rehabilitation robot (CDRR). And a three-dimensional reaching task without predefined trajectory was employed to validate the effectiveness of the proposed control algorithm. Experimental results showed that the control algorithm could continuously recognize the human motion intention and enabled the robot better movement performance indicated as smaller offset error, smoother trajectory, and lower impact. The proposed method could guarantee a natural pHRI and improve the engagement of the subjects, which has great potential in clinical applications.

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Section: Discussionmentioning

confidence: 94%

Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

et al. 2020

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“…In [46], DMPs are separately modelled for each joint of the manipulator by establishing multiple nonlinear functions and transformation systems. In [47], motion trajectory and force trajectory are encoded into a single skill. � Inherent generalisability is another advantage of DMPs, which can be implemented straight forwardly.…”

Section: Represent and Learn Individual Motor Skillsmentioning

confidence: 99%

Review of the techniques used in motor‐cognitive human‐robot skill transfer

Guan

Wang

Yang

2021

Cognitive Comp and Systems

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A conventional robot programming method extensively limits the reusability of skills in the developmental aspect. Engineers programme a robot in a targeted manner for the realisation of predefined skills. The low reusability of general-purpose robot skills is mainly reflected in inability in novel and complex scenarios. Skill transfer aims to transfer human skills to general-purpose manipulators or mobile robots to replicate human-like behaviours. Skill transfer methods that are commonly used at present, such as learning from demonstrated (LfD) or imitation learning, endow the robot with the expert's lowlevel motor and high-level decision-making ability, so that skills can be reproduced and generalised according to perceived context. The improvement of robot cognition usually relates to an improvement in the autonomous high-level decision-making ability. Based on the idea of establishing a generic or specialised robot skill library, robots are expected to autonomously reason about the needs for using skills and plan compound movements according to sensory input. In recent years, in this area, many successful studies have demonstrated their effectiveness. Herein, a detailed review is provided on the transferring techniques of skills, applications, advancements, and limitations, especially in the LfD. Future research directions are also suggested.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…As a consequence, the PRM algorithm is applied for the table cleaning task where the objects of interest stay static. Furthermore, positional information, muscle stiffness of the human arm, contact force with the environment also play important roles in understanding and generating human-like manipulation behaviors for robots as in Reference [31].…”

Section: Introductionmentioning

confidence: 99%

Table Cleaning Task by Human Support Robot Using Deep Learning Technique

Yin

Apuroop

Tamilselvam

et al. 2020

Sensors

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This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting. The HSR is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. A lightweight Deep Convolutional Neural Network (DCNN) has been proposed to recognize the food litter on top of the table. In addition, the planner framework was proposed to HSR for accomplishing the table cleaning task which generates the cleaning path according to the detection of food litter and then the cleaning action is carried out. The effectiveness of the food litter detection module is verified with the cleanliness inspection task using Toyota HSR, and its detection results are verified with standard quality metrics. The experimental results show that the food litter detection module achieves an average of 96% detection accuracy, which is more suitable for deploying the HSR robots for performing the cleanliness inspection and also helps to select the different cleaning modes. Further, the planner part has been tested through the table cleaning tasks. The experimental results show that the planner generated the cleaning path in real time and its generated path is optimal which reduces the cleaning time by grouping based cleaning action for removing the food litters from the table.Sensors 2020, 20, 1698 2 of 20 vision-based techniques are widely used in cleaning robots for recognizing the litter and compute the cleaning action [14][15][16][17][18][19]. Andersen et al., built up a visual cleaning map for cleaning robots using a vision algorithm and a powerful light-transmitting diode. The sensor recognizes the grimy region and generates the dirt map by examining the surface pictures pixel-by-pixel utilizing the multi-variable statistical method [15]. David et al., proposed high-level manipulation actions for cleaning dirt from table surfaces using REEM a humanoid service robot. The author uses a background subtraction algorithm for recognizing the dirt from the table and Noisy Indeterministic Deictic (NID) rules-based learning algorithm to generate the sequence of cleaning action [16]. Ariyan et al., developed a planning algorithm for the removal of stains from non-planar surfaces where the author uses a depth-first branch-and-bound search to generate cleaning trajectories with the K-means clustering algorithm [17]. Hass et al., demonstrated the use of unsupervised clustering algorithm and Markov Decision Problem (MDP) for performing the cleaning task where unsupervised clustering algorithm is used to distinguish the dirt from surface and MDP algorithm is used to generate the maps, and transition model from clustered image is used to describe the robot cleaning action [18]. Nonetheless, these approaches have some practical issues and disadvantages for using in food court table cleaning; the detection ratio relies heavily on the textured surfaces, which makes it challenging to identify the litter type as solid...

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A Framework of Hybrid Force/Motion Skills Learning for Robots

Cited by 33 publications

References 44 publications

Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

Hybrid Active Control With Human Intention Detection of an Upper-Limb Cable-Driven Rehabilitation Robot

Review of the techniques used in motor‐cognitive human‐robot skill transfer

Table Cleaning Task by Human Support Robot Using Deep Learning Technique

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