1,876 research outputs found
Knowledge Representation for Robots through Human-Robot Interaction
The representation of the knowledge needed by a robot to perform complex
tasks is restricted by the limitations of perception. One possible way of
overcoming this situation and designing "knowledgeable" robots is to rely on
the interaction with the user. We propose a multi-modal interaction framework
that allows to effectively acquire knowledge about the environment where the
robot operates. In particular, in this paper we present a rich representation
framework that can be automatically built from the metric map annotated with
the indications provided by the user. Such a representation, allows then the
robot to ground complex referential expressions for motion commands and to
devise topological navigation plans to achieve the target locations.Comment: Knowledge Representation and Reasoning in Robotics Workshop at ICLP
201
Spatio-Temporal Analysis of Facial Actions using Lifecycle-Aware Capsule Networks
Most state-of-the-art approaches for Facial Action Unit (AU) detection rely
upon evaluating facial expressions from static frames, encoding a snapshot of
heightened facial activity. In real-world interactions, however, facial
expressions are usually more subtle and evolve in a temporal manner requiring
AU detection models to learn spatial as well as temporal information. In this
paper, we focus on both spatial and spatio-temporal features encoding the
temporal evolution of facial AU activation. For this purpose, we propose the
Action Unit Lifecycle-Aware Capsule Network (AULA-Caps) that performs AU
detection using both frame and sequence-level features. While at the
frame-level the capsule layers of AULA-Caps learn spatial feature primitives to
determine AU activations, at the sequence-level, it learns temporal
dependencies between contiguous frames by focusing on relevant spatio-temporal
segments in the sequence. The learnt feature capsules are routed together such
that the model learns to selectively focus more on spatial or spatio-temporal
information depending upon the AU lifecycle. The proposed model is evaluated on
the commonly used BP4D and GFT benchmark datasets obtaining state-of-the-art
results on both the datasets.Comment: Updated Figure 6 and the Acknowledgements. Corrected typos. 11 pages,
6 figures, 3 table
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