7,266 research outputs found
Learning to Act Properly: Predicting and Explaining Affordances from Images
We address the problem of affordance reasoning in diverse scenes that appear
in the real world. Affordances relate the agent's actions to their effects when
taken on the surrounding objects. In our work, we take the egocentric view of
the scene, and aim to reason about action-object affordances that respect both
the physical world as well as the social norms imposed by the society. We also
aim to teach artificial agents why some actions should not be taken in certain
situations, and what would likely happen if these actions would be taken. We
collect a new dataset that builds upon ADE20k, referred to as ADE-Affordance,
which contains annotations enabling such rich visual reasoning. We propose a
model that exploits Graph Neural Networks to propagate contextual information
from the scene in order to perform detailed affordance reasoning about each
object. Our model is showcased through various ablation studies, pointing to
successes and challenges in this complex task
Interactive Robot Learning of Gestures, Language and Affordances
A growing field in robotics and Artificial Intelligence (AI) research is
human-robot collaboration, whose target is to enable effective teamwork between
humans and robots. However, in many situations human teams are still superior
to human-robot teams, primarily because human teams can easily agree on a
common goal with language, and the individual members observe each other
effectively, leveraging their shared motor repertoire and sensorimotor
resources. This paper shows that for cognitive robots it is possible, and
indeed fruitful, to combine knowledge acquired from interacting with elements
of the environment (affordance exploration) with the probabilistic observation
of another agent's actions.
We propose a model that unites (i) learning robot affordances and word
descriptions with (ii) statistical recognition of human gestures with vision
sensors. We discuss theoretical motivations, possible implementations, and we
show initial results which highlight that, after having acquired knowledge of
its surrounding environment, a humanoid robot can generalize this knowledge to
the case when it observes another agent (human partner) performing the same
motor actions previously executed during training.Comment: code available at https://github.com/gsaponaro/glu-gesture
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