Inverse Optimal Control for the identification of human objective: a preparatory study for physical Human-Robot Interaction

franceschi, paolo; beschi, manuel; pedrocchi, nicola

doi:10.1109/etfa52439.2022.9921553

Cited by 7 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The velocity component has always had a minor and typically negligible influence on human behavior, as they are proven to care only about the position and visual feedback [77], [78], [79]. Therefore, it can be set to zero without any loss of generality, as ẋre f ,h = 0 6×1 .…”

Section: Human Reference Trajectory Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Human-Robot Role Arbitration via Differential Game Theory

Franceschi,

Pedrocchi,

Beschi

2024

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

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

show abstract

Section: Human Reference Trajectory Estimationmentioning

confidence: 99%

“…The values for the human are computed offline via Inverse Optimal Control as in [79]. Despite the human cost function values possibly changing according to the task, the subject, and the different stages of the task, the choice of using fixed values can be justified for several reasons.…”

Section: B Experimental Setupmentioning

confidence: 99%

Human-Robot Role Arbitration via Differential Game Theory

Franceschi,

Pedrocchi,

Beschi

2024

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Two relevant components of the proposed controller must be addressed when dealing with real humans: knowledge of the human's reference and cost function. An Inverse Optimal Controller (IOC) [26], [27] recovers an estimate of the human parameters, Qh and Rh . The robot knows the human target that denes the reference in (10).…”

Section: B Real-world Experimentsmentioning

confidence: 99%

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

Franceschi

Pedrocchi

Beschi

2022

2022 International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

The problem addressed in this work is the arbitration of the role between a robot and a human during physical Human-Robot Interaction, sharing a common task. The system is modeled as a Cartesian impedance, with two separate external forces provided by the human and the robot. The problem is then reformulated as a Cooperative Differential Game, which possibly has multiple solutions on the Pareto frontier. Finally, the bargaining problem is addressed by proposing a solution depending on the interaction force, interpreted as the human will to lead or follow. This defines the arbitration law and assigns the role of leader or follower to the robot. Experiments show the feasibility and capabilities of the proposed control in managing the human-robot arbitration during a sharedtrajectory following task.

show abstract