A game-theoretic approach for adaptive action selection in close proximity human-robot-collaboration

Gabler, Volker; Stahl, Tim; Huber, Gerold; Oguz, Ozgur S.; Wollherr, Dirk

doi:10.1109/icra.2017.7989336

Cited by 35 publications

(29 citation statements)

References 14 publications

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“…The game theory methods have been used in different HRC applications, for instance, in analyzing and detecting the human behavior to adapt the robot’s one to it for reaching a better collaboration performance ( Jarrassé et al, 2012 ; Li et al, 2019 ). Game theory has been also utilized in HRC in mutual adaptation to achieve industrial assembly scenarios ( Gabler et al, 2017 ). We choose the game theory as a decision-making method due to its simplicity and effectiveness in modeling most interactions between a group of participants and their reactions to each other’s decisions.…”

Section: Related Workmentioning

confidence: 99%

General Framework for the Optimization of the Human-Robot Collaboration Decision-Making Process Through the Ability to Change Performance Metrics

et al. 2021

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This paper proposes a new decision-making framework in the context of Human-Robot Collaboration (HRC). State-of-the-art techniques consider the HRC as an optimization problem in which the utility function, also called reward function, is defined to accomplish the task regardless of how well the interaction is performed. When the performance metrics are considered, they cannot be easily changed within the same framework. In contrast, our decision-making framework can easily handle the change of the performance metrics from one case scenario to another. Our method treats HRC as a constrained optimization problem where the utility function is split into two main parts. Firstly, a constraint defines how to accomplish the task. Secondly, a reward evaluates the performance of the collaboration, which is the only part that is modified when changing the performance metrics. It gives control over the way the interaction unfolds, and it also guarantees the adaptation of the robot actions to the human ones in real-time. In this paper, the decision-making process is based on Nash Equilibrium and perfect-information extensive form from game theory. It can deal with collaborative interactions considering different performance metrics such as optimizing the time to complete the task, considering the probability of human errors, etc. Simulations and a real experimental study on “an assembly task” -i.e., a game based on a construction kit-illustrate the effectiveness of the proposed framework.

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Section: Related Workmentioning

confidence: 99%

General Framework for the Optimization of the Human-Robot Collaboration Decision-Making Process Through the Ability to Change Performance Metrics

et al. 2021

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“…Unfortunately, the works mentioned thus far focused on groups of robotic agents. In contrast, Gabler et al [22] regarded a two-agent team consisting of a human and a robot. They presented a method to predict the actions of a human during a collaborative pick and place task based on game theory and Nash equilibria.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, artificial agents are starting to share their workspaces with humans: robots navigate autonomously through pedestrian areas (see Fig. 1) or highways [9,82,87,89]; they guide people at museums or fairs [64]; and they even have physical contact with humans and work together with them on an assembly or manipulation task [19,22] or assist the elderly [23]. All these examples have in common that their performance could be improved (e.g., readability, trustworthiness, fault tolerance, and work pace) if the navigation is human-like [10,11,13].…”

Section: Introductionmentioning

confidence: 99%

Human-Like Motion Planning Based on Game Theoretic Decision Making

Turnwald

Wollherr

2018

Int J of Soc Robotics

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Robot motion planners are increasingly being equipped with an intriguing property: human likeness. This property can enhance human-robot interactions and is essential for a convincing computer animation of humans. This paper presents a (multi-agent) motion planner for dynamic environments that generates human-like motion. The presented motion planner stands out against other motion planners by explicitly modeling human-like decision making and taking interdependencies between individuals into account, which is achieved by applying game theory. Non-cooperative games and the concept of a Nash equilibrium are used to formulate the decision process that describes human motion behavior while walking in a populated environment. We evaluate whether our approach generates human-like motions through two experiments: a video study showing simulated, moving pedestrians, wherein the participants are passive observers, and a collision avoidance study, wherein the participants interact within virtual reality with an agent that is controlled by different motion planners. The experiments are designed as variations of the Turing test, which determines whether participants can differentiate between human motions and artificially generated motions. The results of both studies coincide and show that the participants could not distinguish between human motion behavior and our artificial behavior based on game theory. In contrast, the participants could distinguish human motions from motions based on established planners, such as the reciprocal velocity obstacles or social forces.

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“…b) Predictive multi-agent systems: Increasing efforts have been made to design systems that are capable of predicting other agents' intents/actions to some level. Prediction algorithms have been explicitly or implicitly applied to problems such as navigation in crowds and traffic [8], [9], [10], motion planning [11], [12], and human-robot collaborative planning [13], [14]. Despite the promising results on specific problems, there lacks a framework to unify learning, prediction, and planning in general multi-agent systems.…”

Section: Belief Of Other Agents' Goal Intrinsic Value Utilitymentioning

confidence: 99%

Intent-Aware Multi-Agent Reinforcement Learning

Zhu

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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This paper proposes an intent-aware multi-agent planning framework as well as a learning algorithm. Under this framework, an agent plans in the goal space to maximize the expected utility. The planning process takes the belief of other agents' intents into consideration. Instead of formulating the learning problem as a partially observable Markov decision process (POMDP), we propose a simple but effective linear function approximation of the utility function. It is based on the observation that for humans, other people's intents will pose an influence on our utility for a goal. The proposed framework has several major advantages: i) it is computationally feasible and guaranteed to converge. ii) It can easily integrate existing intent prediction and low-level planning algorithms. iii) It does not suffer from sparse feedbacks in the action space. We experiment our algorithm in a real-world problem that is non-episodic, and the number of agents and goals can vary over time. Our algorithm is trained in a scene in which aerial robots and humans interact, and tested in a novel scene with a different environment. Experimental results show that our algorithm achieves the best performance and human-like behaviors emerge during the dynamic process.

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A game-theoretic approach for adaptive action selection in close proximity human-robot-collaboration

Cited by 35 publications

References 14 publications

General Framework for the Optimization of the Human-Robot Collaboration Decision-Making Process Through the Ability to Change Performance Metrics

General Framework for the Optimization of the Human-Robot Collaboration Decision-Making Process Through the Ability to Change Performance Metrics

Human-Like Motion Planning Based on Game Theoretic Decision Making

Intent-Aware Multi-Agent Reinforcement Learning

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