A Framework of Human–Robot Coordination Based on Game Theory and Policy Iteration

Li, Yanan; Tee, Keng Peng; Yan, Rui; Chan, Wei Liang; Wu, Yan

doi:10.1109/tro.2016.2597322

Cited by 93 publications

(64 citation statements)

References 38 publications

(54 reference statements)

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“…For scenarios where the task has few features (i.e., two to three), global replanning methods have already been implemented online [4,5]: thus, in practice, the time spent replanning may not be significant enough to warrant our alternate approach. Finally, as we demonstrated within the worst-case simulations, shared and optimal controllers that do not learn the correct trajectory are often sufficient when the hypothesis space is largely incorrect [12,20,25,30,31]. Our experimental results should therefore be interpreted as useful trends-demonstrating that LQR+L is beneficial in the right contexts-rather than claiming that this method is better for all cases.…”

Section: Discussionmentioning

confidence: 60%

“…Optimal control. Other research studies optimal control strategies for tasks that involve physical human interaction [20,25,30]. In Li et al [25], the authors leverage game theory and optimal control to identify the correct robot behavior: like Medina et al [30], their approach reduces the robot's stiffness when the human applies forces and torques but increases the rendered stiffness when the human does not interact, or when the human's interactions agree with the robot's prediction.…”

Section: Control Strategies For Physical Human Interactionmentioning

confidence: 99%

“…Within our formulation, θ * captures the human's preferences for how the robot should behave-for example, the goal the robot should reach, the speed of the robot's motion, and/or the trajectory features. Similar to recent works on optimal control for pHRI [20,25,30], we therefore have the following cost function:…”

Section: Cost Functionmentioning

confidence: 99%

See 2 more Smart Citations

Learning the Correct Robot Trajectory in Real-Time from Physical Human Interactions

Losey

O’Malley

2019

J. Hum.-Robot Interact.

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We present a learning and control strategy that enables robots to harness physical human interventions to update their trajectory and goal during autonomous tasks. Within the state of the art, the robot typically reacts to physical interactions by modifying a local segment of its trajectory, or by searching for the global trajectory offline, using either replanning or previous demonstrations. Instead, we explore a one-shot approach: here, the robot updates its entire trajectory and goal in real time without relying on multiple iterations, offline demonstrations, or replanning. Our solution is grounded in optimal control and gradient descent, and extends linear-quadratic regulator controllers to generalize across methods that locally or globally modify the robot's underlying trajectory. In the best case, this Linear-quadratic regulator + Learning approach matches the optimal offline response to physical interactions, and-in more challenging cases-our strategy is robust to noisy and unexpected human corrections. We compare the proposed solution against other real-time strategies in a user study and demonstrate its efficacy in terms of both objective and subjective measures.

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Section: Discussionmentioning

confidence: 60%

Section: Control Strategies For Physical Human Interactionmentioning

confidence: 99%

See 1 more Smart Citation

Learning the Correct Robot Trajectory in Real-Time from Physical Human Interactions

Losey

O’Malley

2019

J. Hum.-Robot Interact.

View full text Add to dashboard Cite

show abstract

“…To adjust the robot's role to lead or to follow according to the human's intention, game theory was employed for fundamental analysis of humanrobot interaction and an adaptation law was developed in [106]. Policy iteration combining with NN was adopted to provide a rigorous solution to the problem of the system equilibrium in human-robot interaction [107].…”

Section: Nn Based Human-robot Interaction Controlmentioning

confidence: 99%

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Jiang

Yang

et al. 2017

Complexity

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As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

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“…The significance of adjustable leader/follower roles for shared control has been emphasized in a recent review in the field of human-robot interaction [14], and there are several works in this direction [15], [16], [17]. Such an human-robot system is formulated as a two-agent system with one leader and one follower.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Coordination for Formation Tracking Control in Constrained Space With Limited Communication

Liu

Goh

et al. 2019

IEEE Trans. Cybern.

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In this paper, the formation tracking control is studied for a multiagent system (MAS) with communication limitations. The objective is to control a group of agents to track a desired trajectory while maintaining a given formation in nonomniscient constrained space. The role switching triggered by the detection of unexpected spatial constraints facilitates efficiency of event-triggered control in communication bandwidth, energy consumption, and processor usage. A coordination mechanism is proposed based on a novel role ``coordinator'' to indirectly spread environmental information among the whole communication network and form a feedback link from followers to the leader to guarantee the formation keeping. A formation scaling factor is introduced to scale up or scale down the given formation size in the case that the region is impassable for MAS with the original formation size. Controllers for the leader and followers are designed and the adaptation law is developed for the formation scaling factor. The conditions for asymptotic stability of MAS are discussed based on the Lyapunov theory. Simulation results are presented to illustrate the performance of proposed approaches.

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A Framework of Human–Robot Coordination Based on Game Theory and Policy Iteration

Cited by 93 publications

References 38 publications

Learning the Correct Robot Trajectory in Real-Time from Physical Human Interactions

Learning the Correct Robot Trajectory in Real-Time from Physical Human Interactions

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Event-Triggered Coordination for Formation Tracking Control in Constrained Space With Limited Communication

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