Adaptive Impedance Controller for Human-Robot Arbitration based on Cooperative Differential Game Theory

Franceschi, Paolo; Pedrocchi, Nicola; Beschi, Manuel

doi:10.1109/icra46639.2022.9811853

Cited by 14 publications

(6 citation statements)

References 45 publications

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“…Extension to these works is in [58], where the trajectory tracking problem is addressed in the non-cooperative scenario. Finally, the authors also investigated the cooperative scenario in [59], where the weighting factor is variable to allow the adaptive impedance behavior of the robot.…”

Section: A Related Workmentioning

confidence: 99%

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Human-Robot Role Arbitration via Differential Game Theory

Franceschi,

Pedrocchi,

Beschi

2024

IEEE Trans. Automat. Sci. Eng.

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The industry needs controllers that allow smooth and natural physical Human-Robot Interaction (pHRI) to make production scenarios more flexible and user-friendly. Within this context, particularly interesting is Role Arbitration, which is the mechanism that assigns the role of the leader to either the human or the robot. This paper investigates Game-Theory (GT) to model pHRI, and specifically, Cooperative Game Theory (CGT) and Non-Cooperative Game Theory (NCGT) are considered. This work proposes a possible solution to the Role Arbitration problem and defines a Role Arbitration framework based on differential game theory to allow pHRI. The proposed method can allow trajectory deformation according to human will, avoiding reaching dangerous situations such as collisions with environmental features, robot joints and workspace limits, and possibly safety constraints. Three sets of experiments are proposed to evaluate different situations and compared with two other standard methods for pHRI, the Impedance Control, and the Manual Guidance. Experiments show that with our Role Arbitration method, different situations can be handled safely and smoothly with a low human effort. In particular, the performances of the IMP and MG vary according to the task. In some cases, MG performs well, and IMP does not. In some others, IMP performs excellently, and MG does not. The proposed Role Arbitration controller performs well in all the cases, showing its superiority and generality. The proposed method generally requires less force and ensures better accuracy in performing all tasks than standard controllers.Note to Practitioners-This work presents a method that allows role arbitration for physical Human-Robot Interaction, motivated by the need to adjust the role of leader/follower in a shared task according to the specific phase of the task or the knowledge of one of the two agents. This method suits applications such as object co-transportation, which requires final precise positioning but allows some trajectory deformation on the fly. It can also handle situations where the carried obstacle occludes human sight, and

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Section: A Related Workmentioning

confidence: 99%

“…Compared to the previous work of some of the authors [59], this work uses the knowledge of the human's desired trajectory to solve the optimal control problem. Moreover, a more complex and comprehensive role-arbitration function is defined.…”

Section: B Motivation and Contributionmentioning

confidence: 99%

Human-Robot Role Arbitration via Differential Game Theory

Franceschi,

Pedrocchi,

Beschi

2024

IEEE Trans. Automat. Sci. Eng.

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“…Dealing with complex robotic systems that interact with the human body (Human-inthe-loop control) [24], where the subject is considered the controller [25], it is essential to develop control strategies capable of making the robot compliant with the user's movement intention. Complex approaches based on modelling dynamic nonlinear interaction (which emphasize the importance of neuromuscular actuation mechanisms as a source of nonlinearity in the human controller) and on identifying movement intention have being explored [25].…”

Section: Design Of the Low Level Controlmentioning

confidence: 99%

A Rapid Control Prototyping and Hardware-in-the Loop Approach for Upper Limb Robotic Exoskeletons Control

Bodo,

Tessari,

Buccelli

et al. 2024

Applied Sciences

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In the last decade, robotic-mediated rehabilitation has emerged as a potential solution to improve repetitive task training. Each device in this field has a unique development history shaped by engineers’ expertise in specific programming languages or platforms. In this work we adopt an approach that tries to abstract from the final implementation with the aim to make control logic more shareable and understandable. The authors will present the outcomes of the application of a Rapid Control Prototyping strategy to an upper-limb robotic exoskeleton. A model-based design approach implemented on a real-time target machine is presented. This modern design approach was explored with several control strategies and was used to test the exoskeleton’s performances. The proposed method highlights how it is possible to develop the entire control architecture in a single programming environment.

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“…Second, the robot should share the workload with the human. Still, the human should be able to gain control over the robot, i.e., the robot and human should be able to exchange leader and follower roles continuously Franceschi, Pedrocchi, and Beschi (2022). Finally, a well-known problem of manipulating large objects is the rotation-translation ambiguity in humanrobot Dumora, Geffard, Bidard, Brouillet, and Fraisse (2012) co-manipulation, but it can arise even during human-human Jensen, Salmon, and Killpack (2021) co-manipulation.…”

Section: Introductionmentioning

confidence: 99%

Co-manipulation of soft-materials estimating deformation from depth images

Nicola¹,

Villagrossi²,

Pedrocchi³

2023

Preprint

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Human-robot co-manipulation of soft materials, such as fabrics, composites, and sheets of paper/cardboard, is a challenging operation that presents several relevant industrial applications. Estimating the deformation state of the co-manipulated material is one of the main challenges. Viable methods provide the indirect measure by calculating the human-robot relative distance. In this paper, we develop a datadriven model to estimate the deformation state of the material from a depth image through a Convolutional Neural Network (CNN). First, we define the deformation state of the material as the relative rototranslation from the current robot pose and a human grasping position. The model estimates the current deformation state through a Convolutional Neural Network, specifically a DenseNet-121 pretrained on ImageNet. The delta between the current and the desired deformation state is fed to the robot controller that outputs twist commands. The paper describes the developed approach to acquire, preprocess the dataset and train the model. The model is compared with the current state-of-the-art method based on a skeletal tracker from cameras. Results show that our approach achieves better performances and avoids the various drawbacks caused by using a skeletal tracker. Finally, we also studied the model performance according to different architectures and dataset dimensions to minimize the time required for dataset acquisition.

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Adaptive Impedance Controller for Human-Robot Arbitration based on Cooperative Differential Game Theory

Cited by 14 publications

References 45 publications

Human-Robot Role Arbitration via Differential Game Theory

Human-Robot Role Arbitration via Differential Game Theory

A Rapid Control Prototyping and Hardware-in-the Loop Approach for Upper Limb Robotic Exoskeletons Control

Co-manipulation of soft-materials estimating deformation from depth images

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