Organization and Understanding of a Tactile Information Dataset TacAct For Physical Human-Robot Interaction

Wang, Peng; Liu, Jixiao; Hou, Funing; Chen, Dicai; Xia, Zihou; Guo, Shuxiang

doi:10.1109/iros51168.2021.9636389

Cited by 14 publications

(6 citation statements)

References 14 publications

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“…When human beings are in a delighted mood, they will produce friendly actions to the robot, such as shaking hands, gently stroke, etc. When in anger or sadness emotion, humans will beat or collide with the robotic manipulator [34]- [36]. According to the reference in [37], f i (q i , qi ) denotes:…”

Section: Remarkmentioning

confidence: 99%

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Decentralized Robust Active Disturbance Rejection Control of Modular Robot Manipulators: An Experimental Investigation With Emotional pHRI

et al. 2022

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This paper presents an active disturbance rejection control (ADRC) method for modular robot manipulators (MRMs) based on extended state observer (ESO), which solves the problem of trajectory tracking when modular robot manipulators facing the emotional physical human-robot interaction (pHRI). The dynamic model of MRMs is formulated via joint torque feedback (JTF) technique that is deployed for each joint module to design the model compensation controller. ESO is used to estimate the interconnected dynamic coupling (IDC) term and the interference term caused by emotional pHRI. An uncertainty decomposition-based robust control is developed to compensate the friction term. The terminal sliding mode control (TSMC) algorithm is introduced to the controller to provide faster convergence and higher precision control effect. Based on the Lyapunov theory, the tracking error is proved to be ultimately uniformly bounded (UUB). Finally, experiments demonstrate advantages of the proposed method.INDEX TERMS Modular robot manipulators, extended state observer, terminal sliding mode, emotional pHRI, active disturbance rejection control.

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Section: Remarkmentioning

confidence: 99%

“…Theorem 1: Consider n modules MRMs system with (1), and the model uncertainty is defined by ( 7) and ( 8), as well as the disturbance term caused by the emotional pHRI in (2). The tracking error can be guaranteed UUB under control law proposed by (36).…”

Section: Stability Proofmentioning

confidence: 99%

Decentralized Robust Active Disturbance Rejection Control of Modular Robot Manipulators: An Experimental Investigation With Emotional pHRI

et al. 2022

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“…For training on NTU Mutual dataset, SGD optimizer is used with Nesterov momentum of 0.9, a initial learning rate of 0.1 and a decay rate 0.1. Window size is set to [20,1,2]. Cross entropy is used as loss function with label smoothing factor 0.1 and temperature factor 1.0.…”

Section: B Implementation Detailsmentioning

confidence: 99%

“…Human Interaction Recognition (HIR) has become a significant challenge and research focus in the field of computer vision for identifying and comprehending video content of human actions [1]- [4]. The rapid development of fields such as social media, intelligent surveillance, and virtual reality has increased the demand for real-time recognition and analysis of human behaviour in videos.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stream Recurrent Network for Skeleton-based Human Interaction Recognition

Men

Shum

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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This paper addresses the problem of recognizing human-human interaction from skeletal sequences. Existing methods are mainly designed to classify single human action. Many of them simply stack the movement features of two characters to deal with human interaction, while neglecting the abundant relationships between characters. In this paper, we propose a novel two-stream recurrent neural network by adopting the geometric features from both single actions and interactions to describe the spatial correlations with different discriminative abilities. The first stream is constructed under pairwise joint distance (PJD) in a fully-connected mesh to categorize the interactions with explicit distance patterns. To better distinguish similar interactions, in the second stream, we combine PJD with the spatial features from individual joint positions using graph convolutions to detect the implicit correlations among joints, where the joint connections in the graph are adaptive for flexible correlations. After spatial modeling, each stream is fed to a bi-directional LSTM to encode two-way temporal properties. To take advantage of the diverse discriminative power of the two streams, we come up with a late fusion algorithm to combine their output predictions concerning information entropy. Experimental results show that the proposed framework achieves state-of-theart performance on 3D and comparable performance on 2D interaction datasets. Moreover, the late fusion results demonstrate the effectiveness of improving the recognition accuracy compared with single streams.

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“…Wang et al compiled a dataset containing haptic information for physical human–robot interactions. [ 19 ] By tracking 50 individuals using a tactile sensor, they captured 12 distinct types of tactile motions. This dataset enabled the prediction of human intentions during interactions with robots.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Learning‐Based Proactive Human Handover Intention Prediction Using Wearable Data Gloves and Augmented Reality

Zou,

Liu,

Zhao

et al. 2024

Advanced Intelligent Systems

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Efficient object handover between humans and robots holds significant importance within collaborative manufacturing environments. Enhancing the efficacy of human–robot handovers involves enabling robots to comprehend and foresee human handover intentions. This article introduces human‐teaching–robot‐learning‐prediction framework, allowing robots to learn from diverse human demonstrations and anticipate human handover intentions. The framework facilitates human programming of robots through demonstrations utilizing augmented reality and a wearable dataglove, aligned with task requirements and human working preferences. Subsequently, robots enhance their cognitive capabilities by assimilating insights from human handover demonstrations, utilizing deep neural network algorithms. Furthermore, robots can proactively seek clarification from humans via an augmented reality system when confronted with ambiguity in human intentions, mirroring how humans seek clarity from their counterparts. This proactive approach empowers robots to anticipate human intentions and assist human partners during handovers. Empirical results underscore the benefits of the proposed approach, demonstrating highly accurate prediction of human intentions in human–robot handover tasks.

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Organization and Understanding of a Tactile Information Dataset TacAct For Physical Human-Robot Interaction

Cited by 14 publications

References 14 publications

Decentralized Robust Active Disturbance Rejection Control of Modular Robot Manipulators: An Experimental Investigation With Emotional pHRI

Decentralized Robust Active Disturbance Rejection Control of Modular Robot Manipulators: An Experimental Investigation With Emotional pHRI

A Two-Stream Recurrent Network for Skeleton-based Human Interaction Recognition

Multimodal Learning‐Based Proactive Human Handover Intention Prediction Using Wearable Data Gloves and Augmented Reality

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