7,299 research outputs found
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
Assistive Planning in Complex, Dynamic Environments: a Probabilistic Approach
We explore the probabilistic foundations of shared control in complex dynamic
environments. In order to do this, we formulate shared control as a random
process and describe the joint distribution that governs its behavior. For
tractability, we model the relationships between the operator, autonomy, and
crowd as an undirected graphical model. Further, we introduce an interaction
function between the operator and the robot, that we call "agreeability"; in
combination with the methods developed in~\cite{trautman-ijrr-2015}, we extend
a cooperative collision avoidance autonomy to shared control. We therefore
quantify the notion of simultaneously optimizing over agreeability (between the
operator and autonomy), and safety and efficiency in crowded environments. We
show that for a particular form of interaction function between the autonomy
and the operator, linear blending is recovered exactly. Additionally, to
recover linear blending, unimodal restrictions must be placed on the models
describing the operator and the autonomy. In turn, these restrictions raise
questions about the flexibility and applicability of the linear blending
framework. Additionally, we present an extension of linear blending called
"operator biased linear trajectory blending" (which formalizes some recent
approaches in linear blending such as~\cite{dragan-ijrr-2013}) and show that
not only is this also a restrictive special case of our probabilistic approach,
but more importantly, is statistically unsound, and thus, mathematically,
unsuitable for implementation. Instead, we suggest a statistically principled
approach that guarantees data is used in a consistent manner, and show how this
alternative approach converges to the full probabilistic framework. We conclude
by proving that, in general, linear blending is suboptimal with respect to the
joint metric of agreeability, safety, and efficiency
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Spatial context-aware person-following for a domestic robot
Domestic robots are in the focus of research in
terms of service providers in households and even as robotic
companion that share the living space with humans. A major
capability of mobile domestic robots that is joint exploration
of space. One challenge to deal with this task is how could we
let the robots move in space in reasonable, socially acceptable
ways so that it will support interaction and communication
as a part of the joint exploration. As a step towards this
challenge, we have developed a context-aware following behav-
ior considering these social aspects and applied these together
with a multi-modal person-tracking method to switch between
three basic following approaches, namely direction-following,
path-following and parallel-following. These are derived from
the observation of human-human following schemes and are
activated depending on the current spatial context (e.g. free
space) and the relative position of the interacting human.
A combination of the elementary behaviors is performed in
real time with our mobile robot in different environments.
First experimental results are provided to demonstrate the
practicability of the proposed approach
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