Human-robot collaboration by intention recognition using probabilistic state machines

Awais, Muhammad; Henrich, Dominik

doi:10.1109/raad.2010.5524605

Cited by 47 publications

(27 citation statements)

References 1 publication

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“…The cases discussed the mapping of human intention to the corresponding observation sequence. The mechanism used for intention recognition consists of the probabilistic FSMs [12]. For online intention recognition a probabilistic FSM regarding to a specific intention is constructed online.…”

Section: Discussionmentioning

confidence: 99%

“…The weight of each FSM represents the probability of the intention, represented by that FSM [12]. A general FSM is shown in the Fig.…”

Section: A State Machine Constructionmentioning

confidence: 99%

“…The approach [16] uses a novel formulation of HMM to recognize the human intention. The intention recognition approach introduced in [12] uses probabilistic Finite State Machines (FSM). The described approaches recognize the human intention if the human intentions are already known and modeled.…”

Section: Introductionmentioning

confidence: 99%

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Online intention learning for human-robot interaction by scene observation

Awais

Henrich

2012

2012 IEEE Workshop on Advanced Robotics and Its Social Impacts (ARSO)

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Intention recognition plays a key role in the cooperation among the humans. An intention describes an action or sequence of actions to be performed for achieving the intended purpose. The cooperating humans learn each others intentions while cooperation. In this paper we propose three ways how a robot can learn the intention of the cooperating human. In the first case, the robot learns the human intention by mapping the known human intention given in terms of scene information to the observed action sequence. The actions are already known to the robot. In the second case, the robot is only given the human actions but the robot estimates the human intention in terms of the changes that occur in the scene due to the human actions. The robot learns the human intention by mapping the observed action sequence to the human intention. The human intention is estimated from the scene information. In the third case, only the scene information is used in order to learn the human intention mapping. The scene information is used to infer the human actions as well as the human intention.

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Section: Discussionmentioning

confidence: 99%

“…The weight of each FSM represents the probability of the intention, represented by that FSM [12]. A general FSM is shown in the Fig.…”

Section: A State Machine Constructionmentioning

confidence: 99%

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Online intention learning for human-robot interaction by scene observation

Awais

Henrich

2012

2012 IEEE Workshop on Advanced Robotics and Its Social Impacts (ARSO)

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“…Previous work has shown that robots that successfully predict humans' behavior exhibit improved performance in many applications, such as assistive robotics [2,14,22,23], motion planning [27,48], collaborative games [26], and autonomous driving [3,38,39]. One reason behind this success is that human modeling equips robots with a theory of mind (ToM), or the ability to attribute a mind to oneself and others [34,41].…”

Section: Related Workmentioning

confidence: 99%

When Humans Aren't Optimal

Kwon

Bıyık

Talati³

et al. 2020

Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction

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In order to collaborate safely and efficiently, robots need to anticipate how their human partners will behave. Some of today's robots model humans as if they were also robots, and assume users are always optimal. Other robots account for human limitations, and relax this assumption so that the human is noisily rational. Both of these models make sense when the human receives deterministic rewards: i.e., gaining either $100 or $130 with certainty. But in realworld scenarios, rewards are rarely deterministic. Instead, we must make choices subject to risk and uncertainty-and in these settings, humans exhibit a cognitive bias towards suboptimal behavior. For example, when deciding between gaining $100 with certainty or $130 only 80% of the time, people tend to make the risk-averse choice-even though it leads to a lower expected gain! In this paper, we adopt a well-known Risk-Aware human model from behavioral economics called Cumulative Prospect Theory and enable robots to leverage this model during human-robot interaction (HRI). In our user studies, we offer supporting evidence that the Risk-Aware model more accurately predicts suboptimal human behavior. We find that this increased modeling accuracy results in safer and more efficient human-robot collaboration. Overall, we extend existing rational human models so that collaborative robots can anticipate and plan around suboptimal human behavior during HRI.

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“…With the achieved goal, human intention is recognized under current context but this does not reconfigures the intention recognizer, each time when user changes its intentions. In [20] a novel approach for recognizing the human intention using weighted probabilistic state machines has been presented but with this approach reconfiguration doesn't hold.…”

Section: Proposed Architecturementioning

confidence: 99%

A Model to Incorporate Emotional Sensitivity into Human Computer Interactions

Ramnani

Gorthi

2014

Proceedings of the 2014 Workshop on Emotion Representation and Modelling in Human-Computer-Interaction-Systems

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Human machine interaction has started receiving much attention in recent years. The 'Link' which plays a major role in interfacing and facilitating human machine interaction, is getting more and more equipped with different modalities (such as natural language processing , intelligent reasoning etc.) so as to shift the cognitive load of understanding from human to machine. This makes machines not only be able to respond in more appropriate and relevant ways but also reduces the human efforts (in making the system understand and act) to the extent possible. This research work contributes to the design and implementation of the interface by presenting a unified model that takes human machine interaction to a advanced level of understanding with an intelligent interface that not only consists of incorporating emotional intelligence into machines but also identifying contextual information, intention recognition (for a given user's textual response) leading to strategic adaptive dialogue management. The motivation for the system proposed is to provide an inexpensive, unobtrusive means for human beings to interact with computer applications in a meaningful way that requires minimal effort. Research work is completely dedicated to provide a baseline mechanism for achieving stress free satisfying interaction between human and machines. Our system is tested against the existing IVR technology and corresponding results are captured.

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Human-robot collaboration by intention recognition using probabilistic state machines

Cited by 47 publications

References 1 publication

Online intention learning for human-robot interaction by scene observation

Online intention learning for human-robot interaction by scene observation

When Humans Aren't Optimal

A Model to Incorporate Emotional Sensitivity into Human Computer Interactions

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