Human-in-the-Loop Adaptive Control of a Soft Exo-Suit With Actuator Dynamics and Ankle Impedance Adaptation

Li, Zhijun; Li, Qinjian; Huang, Pengbo; Xia, Haisheng; Li, Guoxin

doi:10.1109/tcyb.2023.3240231

Cited by 19 publications

(5 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…of this approach to any imperfectly labeled neurophysiological time series data characterized by repeating events. Whilst the study focused specifically on action potentials, the proposed methodology could also be used for pattern recognition in bulk neurophysiological signal, for example by supplementing contemporary prosthetics and exoskeletons [68], [69]. Additionally, the labeling process need not be by a BSS algorithm.…”

Section: Discussionmentioning

confidence: 99%

Deep Metric Learning With Locality Sensitive Mining for Self-Correcting Source Separation of Neural Spiking Signals

Clarke

Farina

2024

IEEE Trans. Cybern.

View full text Add to dashboard Cite

Automated source separation algorithms have become a central tool in neuroengineering and neuroscience, where they are used to decompose neurophysiological signal into its constituent spiking sources. However, in noisy or highly multivariate recordings these decomposition techniques often make a large number of errors. Such mistakes degrade online humanmachine interfacing methods and require costly post-hoc manual cleaning in the offline setting. In this article we propose an automated error correction methodology using a deep metric learning (DML) framework, generating embedding spaces in which spiking events can be both identified and assigned to their respective sources. Furthermore, we investigate the relative ability of different DML techniques to preserve the intraclass semantic structure needed to identify incorrect class labels in neurophysiological time series. Motivated by this analysis, we propose locality sensitive mining, an easily implemented sampling-based augmentation to typical DML losses which substantially improves the local semantic structure of the embedding space. We demonstrate the utility of this method to generate embedding spaces which can be used to automatically identify incorrectly labeled spiking events with high accuracy.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep Metric Learning With Locality Sensitive Mining for Self-Correcting Source Separation of Neural Spiking Signals

Clarke

Farina

2024

IEEE Trans. Cybern.

View full text Add to dashboard Cite

show abstract

“…Reliable grasping plays a vital role in numerous applications [12, 13]. Recent studies [14–15] have focused on leveraging deep learning techniques for visual grasping.…”

Section: Related Workmentioning

confidence: 99%

Multi-modal interaction with transformers: bridging robots and human with natural language

Wang,

Zhou,

et al. 2023

Robotica

View full text Add to dashboard Cite

The language-guided visual robotic grasping task focuses on enabling robots to grasp objects based on human language instructions. However, real-world human-robot collaboration tasks often involve situations with ambiguous language instructions and complex scenarios. These challenges arise in the understanding of linguistic queries, discrimination of key concepts in visual and language information, and generation of executable grasping configurations for the robot’s end-effector. To overcome these challenges, we propose a novel multi-modal transformer-based framework in this study, which assists robots in localizing spatial interactions of objects using text queries and visual sensing. This framework facilitates object grasping in accordance with human instructions. Our developed framework consists of two main components. First, a visual-linguistic transformer encoder is employed to model multi-modal interactions for objects referred to in the text. Second, the framework performs joint spatial localization and grasping. Extensive ablation studies have been conducted on multiple datasets to evaluate the advantages of each component in our model. Additionally, physical experiments have been performed with natural language-driven human-robot interactions on a physical robot to validate the practicality of our approach.

show abstract

“…Although this method solves the force-position decoupling problem of the actuator under complex constraint conditions, if the corresponding force needs to be controlled in the direction of actuator movement, force-position hybrid control cannot meet the requirements from its principle characteristics (Chu et al , 2021). Therefore, impedance control has been derived based on force-position hybrid control (Hogan, 1985; Fu et al , 2018; Li et al , 2023).…”

Section: Introductionmentioning

confidence: 99%

Adaptive positioning method for ball head based on impedance control

Liu,

Huang,

et al. 2024

RIA

View full text Add to dashboard Cite

Purpose Component positioning is an important part of aircraft assembly, aiming at the problem that it is difficult to accurately fall into the corresponding ball socket for the ball head connected with aircraft component. This study aims to propose a ball head adaptive positioning method based on impedance control. Design/methodology/approach First, a target impedance model for ball head positioning is constructed, and a reference positioning trajectory is generated online based on the contact force between the ball head and the ball socket. Second, the target impedance parameters were optimized based on the artificial fish swarm algorithm. Third, to improve the robustness of the impedance controller in unknown environments, a controller is designed based on model reference adaptive control (MRAC) theory and an adaptive impedance control model is built in the Simulink environment. Finally, a series of ball head positioning experiments are carried out. Findings During the positioning of the ball head, the contact force between the ball head and the ball socket is maintained at a low level. After the positioning, the horizontal contact force between the ball head and the socket is less than 2 N. When the position of the contact environment has the same change during ball head positioning, the contact force between the ball head and the ball socket under standard impedance control will increase to 44 N, while the contact force of the ball head and the ball socket under adaptive impedance control will only increase to 19 N. Originality/value In this paper, impedance control is used to decouple the force-position relationship of the ball head during positioning, which makes the entire process of ball head positioning complete under low stress conditions. At the same time, by constructing an adaptive impedance controller based on MRAC, the robustness of the positioning system under changes in the contact environment position is greatly improved.

show abstract

Human-in-the-Loop Adaptive Control of a Soft Exo-Suit With Actuator Dynamics and Ankle Impedance Adaptation

Cited by 19 publications

References 49 publications

Deep Metric Learning With Locality Sensitive Mining for Self-Correcting Source Separation of Neural Spiking Signals

Deep Metric Learning With Locality Sensitive Mining for Self-Correcting Source Separation of Neural Spiking Signals

Multi-modal interaction with transformers: bridging robots and human with natural language

Adaptive positioning method for ball head based on impedance control

Contact Info

Product

Resources

About