An Object Deformation-Agnostic Framework for Human–Robot Collaborative Transportation

Sirintuna, Doganay; Giammarino, Alberto; Ajoudani, Arash

doi:10.1109/tase.2023.3259162

Cited by 11 publications

(2 citation statements)

References 36 publications

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“…In order to interpret complex manipulation behaviors into a more mathematically tractable form [15][16][17][18][19][20][21][22][23][24], Calinon et al [25] used GMM to establish a probabilistic representation of demonstration data and used Gaussian mixture regression (GMR) to generate smooth trajectory curves. Evrard et al [26] proposed a probabilistic framework based on GMM and GMR for encoding and reconstructing robotic collaborative behavior.…”

Section: Related Workmentioning

confidence: 99%

Research on LFD System of Humanoid Dual-Arm Robot

Cui,

Kou,

Chen

et al. 2024

Symmetry

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Although robots have been widely used in a variety of fields, the idea of enabling them to perform multiple tasks in the same way that humans do remains a difficulty. To solve this, we investigate the learning from demonstration (LFD) system with our independently designed symmetrical humanoid dual-arm robot. We present a novel action feature matching algorithm. This algorithm accurately transforms human demonstration data into task models that robots can directly execute, considerably improving LFD’s generalization capabilities. In our studies, we used motion capture cameras to capture human demonstration actions, which included combinations of simple actions (the action layer) and a succession of complicated operational tasks (the task layer). For the action layer data, we employed Gaussian mixture models (GMM) for processing and constructing an action primitive library. As for the task layer data, we created a “keyframe” segmentation method to transform this data into a series of action primitives and build another action primitive library. Guided by our algorithm, the robot successfully imitated complex human tasks. Results show its excellent task learning and execution, providing an effective solution for robots to learn from human demonstrations and significantly advancing robot technology.

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Section: Related Workmentioning

confidence: 99%

Research on LFD System of Humanoid Dual-Arm Robot

Cui,

Kou,

Chen

et al. 2024

Symmetry

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“…Another application can involve flexible material co-transportation as in [31] and [32]. Similarly to the previous case, a precise position for the target pose is sometimes needed to match the component's design, such as in composite material draping precisely.…”

Section: Introductionmentioning

confidence: 99%

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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