Understanding human intentions via hidden markov models in autonomous mobile robots

Abstract

Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant for situations that involve collaboration among agents or detection of situations that can pose a threat. In this paper, we propose an approach that allows a robot to detect intentions of others based on experience acquired through its own sensory-motor capabilities, then using this experience while taking the perspective of the agent whose intent should be recognized. Our method uses a novel formulation of Hidden Markov Models designed to model a robot's experience and interaction with the world. The robot's capability to observe and analyze the current scene employs a novel vision-based technique for target detection and tracking, using a non-parametric recursive modeling approach. We validate this architecture with a physically embedded robot, detecting the intent of several people performing various activities.

Affective Human-Humanoid Interaction Through Cognitive Architecture

“…If the system captures the temporal extent of these signals, then it can make predictions and create expectations of their evolution. In this sense, we speak of detecting human intentions, and in a simplified manner, they are related to elementary actions of a human agent (Kelley et al, 2008). Over the last few years has changed the approach pursued in the field of HCI, shifting the focus on human-centered design for HCI, namely the creation of systems of interaction made for humans and based on models of human behavior .…”

“…These responses disambiguating the situation in which there are both artificial agent that observes and the human being observed. In the case of human-robot interaction, one of the most important aspects to be explored in the detection of human behavior is the recognition of the intent (Kelley et al, 2008): the problem is to predict the intentions of a person by direct observation of his actions and behaviors. In practice we try to infer the result of a goal-directed mental activity that is not observable, and characterizing precisely the intent.…”

“…The intent can also be clarified or better defined if we recognize the behavior. Again the context is important and how it may serve to disambiguate (Kelley et al, 2008). There are a pairs of actions that may appear identical in every aspect but have different explanations depending on their underlying intentions and the context in which they occur.…”

“…But defining the context in this way, how can we bind the contexts and intentions? There are two possible approaches: the intentions are aware of the contexts, or vice versa the intentions are aware of the contexts (Kelley et al, 2008). In the first case, we ranked every intention carries with it all possible contexts in which it applies, and real-time scenario is not applicable.…”

“…The same kind of reasoning can be done with the behaviors and habits, so think of binding (in the sense of action or sequence of actions to be carried out prototype) with the behaviors. A model of intention should be composed of two parts (Kelley et al, 2008): a model of activity, which is given for example by a particular HMM, and an associated label. This is the minimum amount of information required to enable a robot to perform disambiguation of context.…”