8,989 research outputs found
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
A Unified Framework for Planning in Adversarial and Cooperative Environments
Users of AI systems may rely upon them to produce plans for achieving desired
objectives. Such AI systems should be able to compute obfuscated plans whose
execution in adversarial situations protects privacy, as well as legible plans
which are easy for team members to understand in cooperative situations. We
develop a unified framework that addresses these dual problems by computing
plans with a desired level of comprehensibility from the point of view of a
partially informed observer. For adversarial settings, our approach produces
obfuscated plans with observations that are consistent with at least k goals
from a set of decoy goals. By slightly varying our framework, we present an
approach for goal legibility in cooperative settings which produces plans that
achieve a goal while being consistent with at most j goals from a set of
confounding goals. In addition, we show how the observability of the observer
can be controlled to either obfuscate or clarify the next actions in a plan
when the goal is known to the observer. We present theoretical results on the
complexity analysis of our problems. We demonstrate the execution of obfuscated
and legible plans in a cooking domain using a physical robot Fetch. We also
provide an empirical evaluation to show the feasibility and usefulness of our
approaches using IPC domains.Comment: 8 pages, 2 figure
Context-aware Human Motion Prediction
The problem of predicting human motion given a sequence of past observations
is at the core of many applications in robotics and computer vision. Current
state-of-the-art formulate this problem as a sequence-to-sequence task, in
which a historical of 3D skeletons feeds a Recurrent Neural Network (RNN) that
predicts future movements, typically in the order of 1 to 2 seconds. However,
one aspect that has been obviated so far, is the fact that human motion is
inherently driven by interactions with objects and/or other humans in the
environment. In this paper, we explore this scenario using a novel
context-aware motion prediction architecture. We use a semantic-graph model
where the nodes parameterize the human and objects in the scene and the edges
their mutual interactions. These interactions are iteratively learned through a
graph attention layer, fed with the past observations, which now include both
object and human body motions. Once this semantic graph is learned, we inject
it to a standard RNN to predict future movements of the human/s and object/s.
We consider two variants of our architecture, either freezing the contextual
interactions in the future of updating them. A thorough evaluation in the
"Whole-Body Human Motion Database" shows that in both cases, our context-aware
networks clearly outperform baselines in which the context information is not
considered.Comment: Accepted at CVPR2
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