54,285 research outputs found
Towards Active Event Recognition
Directing robot attention to recognise activities and to anticipate events like goal-directed actions is a crucial skill for human-robot interaction. Unfortunately, issues like intrinsic time constraints, the spatially distributed nature of the entailed information sources, and the existence of a multitude of unobservable states affecting the system, like latent intentions, have long rendered achievement of such skills a rather elusive goal. The problem tests the limits of current attention control systems. It requires an integrated solution for tracking, exploration and recognition, which traditionally have been seen as separate problems in active vision.We propose a probabilistic generative framework based on a mixture of Kalman filters and information gain maximisation that uses predictions in both recognition and attention-control. This framework can efficiently use the observations of one element in a dynamic environment to provide information on other elements, and consequently enables guided exploration.Interestingly, the sensors-control policy, directly derived from first principles, represents the intuitive trade-off between finding the most discriminative clues and maintaining overall awareness.Experiments on a simulated humanoid robot observing a human executing goal-oriented actions demonstrated improvement on recognition time and precision over baseline systems
Interaction Histories and Short-Term Memory: Enactive Development of Turn-Taking Behaviours in a Childlike Humanoid Robot
In this article, an enactive architecture is described that allows a humanoid robot to learn to compose simple actions into turn-taking behaviours while playing interaction games with a human partner. The robotās action choices are reinforced by social feedback from the human in the form of visual attention and measures of behavioural synchronisation. We demonstrate that the system can acquire and switch between behaviours learned through interaction based on social feedback from the human partner. The role of reinforcement based on a short-term memory of the interaction was experimentally investigated. Results indicate that feedback based only on the immediate experience was insufficient to learn longer, more complex turn-taking behaviours. Therefore, some history of the interaction must be considered in the acquisition of turn-taking, which can be efficiently handled through the use of short-term memory.Peer reviewedFinal Published versio
Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis
Notwithstanding recent work which has demonstrated the potential of using
Twitter messages for content-specific data mining and analysis, the depth of
such analysis is inherently limited by the scarcity of data imposed by the 140
character tweet limit. In this paper we describe a novel approach for targeted
knowledge exploration which uses tweet content analysis as a preliminary step.
This step is used to bootstrap more sophisticated data collection from directly
related but much richer content sources. In particular we demonstrate that
valuable information can be collected by following URLs included in tweets. We
automatically extract content from the corresponding web pages and treating
each web page as a document linked to the original tweet show how a temporal
topic model based on a hierarchical Dirichlet process can be used to track the
evolution of a complex topic structure of a Twitter community. Using
autism-related tweets we demonstrate that our method is capable of capturing a
much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, 201
Self-Supervised Vision-Based Detection of the Active Speaker as Support for Socially-Aware Language Acquisition
This paper presents a self-supervised method for visual detection of the
active speaker in a multi-person spoken interaction scenario. Active speaker
detection is a fundamental prerequisite for any artificial cognitive system
attempting to acquire language in social settings. The proposed method is
intended to complement the acoustic detection of the active speaker, thus
improving the system robustness in noisy conditions. The method can detect an
arbitrary number of possibly overlapping active speakers based exclusively on
visual information about their face. Furthermore, the method does not rely on
external annotations, thus complying with cognitive development. Instead, the
method uses information from the auditory modality to support learning in the
visual domain. This paper reports an extensive evaluation of the proposed
method using a large multi-person face-to-face interaction dataset. The results
show good performance in a speaker dependent setting. However, in a speaker
independent setting the proposed method yields a significantly lower
performance. We believe that the proposed method represents an essential
component of any artificial cognitive system or robotic platform engaging in
social interactions.Comment: 10 pages, IEEE Transactions on Cognitive and Developmental System
Being-in-the-world-with: Presence Meets Social And Cognitive Neuroscience
In this chapter we will discuss the concepts of āpresenceā (Inner Presence) and āsocial presenceā (Co-presence) within a cognitive and ecological perspective. Specifically, we claim that the concepts of āpresenceā and āsocial presenceā are the possible links between self, action, communication and culture. In the first section we will provide a capsule view of Heideggerās work by examining the two main features of the Heideggerian concept of ābeingā: spatiality and ābeing withā. We argue that different visions from social and cognitive sciences ā Situated Cognition, Embodied Cognition, Enactive Approach, Situated Simulation, Covert Imitation - and discoveries from neuroscience ā Mirror and Canonical Neurons - have many contact points with this view. In particular, these data suggest that our conceptual system dynamically produces contextualized representations (simulations) that support grounded action in different situations. This is allowed by a common coding ā the motor code ā shared by perception, action and concepts. This common coding also allows the subject for natively recognizing actions done by other selves within the phenomenological contents. In this picture we argue that the role of presence and social presence is to allow the process of self-identification through the separation between āselfā and āother,ā and between āinternalā and āexternalā. Finally, implications of this position for communication and media studies are discussed by way of conclusion
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