Prediction of intent in robotics and multi-agent systems

Demiris, Yiannis

doi:10.1007/s10339-007-0168-9

Cited by 107 publications

(93 citation statements)

References 57 publications

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“…[1,2] for wheelchair-bound populations (figure 1)) to continue to safely operate their powered devices, and receive support only when needed, through a combination of prediction of human intention [4] and environmental situation awareness. In the following few subsections we review our experimental findings as a way for motivating and explaining the core challenges we have identified.…”

Section: From User Modelling To Lifelong Adaptive Modelsmentioning

confidence: 99%

“…We use a generative approach to recognising the intentions of the human operator, utilising HAMMER (Hierarchical Attentive Multiple Models for Recognition and Execution) as the basis architecture [4]. HAMMER uses the concepts of inverse and forward models.…”

Section: Intention Predictionmentioning

confidence: 99%

“…The approach our work follows uses HAMMER's distributed networks of inverse and forward models [4] as a starting point for these approximations, as these have proven to be very useful in the fields of motor control, action recognition and intention interpretation. Our systems attempt to form and customise hierarchical combinations of primitive inverse models, and compare them against the user's performance to determine its ZPD.…”

Section: Approximating the User's Zone Of Proximal Developmentmentioning

confidence: 99%

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Lifelong robot-assisted mobility: models, tools, and challenges

Demiris¹,

Carlson²

2009

IET Seminar Digests

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Increasing the autonomy of users with disabilities through robot-assisted mobility has the potential of facilitating their sensorimotor and social development, as well as reducing the burden of caring for such populations in both inpatient and outpatient settings. While techniques for task-specific assistance exist, they are largely focused on satisfying shortterm goals, utilising stationary user models. For lifelong users and particularly for those with rapidly changing sensorimotor skills (for example very young children), adaptive models that take into consideration these developmental trajectories are becoming very important. In this paper, we present our approach to lifelong user models for robot-assisted mobility, and discuss existing models and tools, as well as challenges that remain ahead.

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Section: From User Modelling To Lifelong Adaptive Modelsmentioning

confidence: 99%

Section: Intention Predictionmentioning

confidence: 99%

Section: Approximating the User's Zone Of Proximal Developmentmentioning

confidence: 99%

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Lifelong robot-assisted mobility: models, tools, and challenges

Demiris¹,

Carlson²

2009

IET Seminar Digests

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“…that of looking for the key). In other words, we are ultimately interested in inferring the intention of the observed act and we use the recognition of the intermediate actions as a proxy that confirms or disconfirms our beliefs, where the intention can be defined as "a plan of action the organism chooses and commits itself to the pursuit of a goal, thus including both a means (action plan) as well as a goal" (Tomasello, Carpenter, Call, Behne, & Moll, 2005 Over the last years there has been an increasing interest in discovering theoretical and computational mechanisms involved in the process of intention understanding both in artificial and natural agents (Demiris, 2007). Developing such an ability would lead to an emergence of socially intelligent agents able to interact with, and learn from their peers (Breazeal, 2004).…”

Section: Introductionmentioning

confidence: 99%

Discriminating and simulating actions with the associative self-organising map

2015

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We propose a system able to represent others' actions as well as to internally simulate their likely continuation from a partial observation. The approach presented here is the first step towards a more ambitious goal of endowing an artificial agent with the ability to recognise and predict others' intentions. Our approach is based on the associative self-organising map, a variant of the self-organising map capable of learning to associate its activity with different inputs over time, where inputs are processed observations of others' actions. We have evaluated our system in two different experimental scenarios obtaining promising results: the system demonstrated an ability to learn discriminable representations of actions, to recognise novel input, and to simulate the likely continuation of partially seen actions.

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“…Next generation warning and active safety systems for vehicle collision avoidance will have to guarantee safety in the presence of human drivers and pedestrians, whose intentions are unknown [1]. More generally, in a variety of multi-agent systems, for example assistive robotics, computer games, and robot-human interaction, the intentions of an observed agent are unknown and need to be identified for control [21].…”

Section: Introductionmentioning

confidence: 99%

Safety Control of Hidden Mode Hybrid Systems

Verma

Vecchio

2012

IEEE Trans. Automat. Contr.

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Abstract-In this paper, we consider the safety control problem for Hidden Mode Hybrid Systems (HMHSs), which are a special class of hybrid automata in which the mode is not available for control. For these systems, safety control is a problem with imperfect state information. We tackle this problem by introducing the notion of non-deterministic discrete information state and by translating the problem to one with perfect state information. The perfect state information control problem is obtained by constructing a new hybrid automaton, whose discrete state is an estimate of the HMHS mode and is, as such, available for control. This problem is solved by computing the capture set and the least restrictive control map for the new hybrid automaton. Sufficient conditions for the termination of the algorithm that computes the capture set are provided. Finally, we show that the solved perfect state information control problem is equivalent to the original problem with imperfect state information under suitable assumptions. We illustrate the application of the proposed technique to a collision avoidance problem between an autonomous vehicle and a human driven vehicle at a traffic intersection.Index Terms-Mode estimation, dynamic feedback, multiagent systems.

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Prediction of intent in robotics and multi-agent systems

Cited by 107 publications

References 57 publications

Lifelong robot-assisted mobility: models, tools, and challenges

Lifelong robot-assisted mobility: models, tools, and challenges

Discriminating and simulating actions with the associative self-organising map

Safety Control of Hidden Mode Hybrid Systems

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