17,057 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
Enabling Robots to Communicate their Objectives
The overarching goal of this work is to efficiently enable end-users to
correctly anticipate a robot's behavior in novel situations. Since a robot's
behavior is often a direct result of its underlying objective function, our
insight is that end-users need to have an accurate mental model of this
objective function in order to understand and predict what the robot will do.
While people naturally develop such a mental model over time through observing
the robot act, this familiarization process may be lengthy. Our approach
reduces this time by having the robot model how people infer objectives from
observed behavior, and then it selects those behaviors that are maximally
informative. The problem of computing a posterior over objectives from observed
behavior is known as Inverse Reinforcement Learning (IRL), and has been applied
to robots learning human objectives. We consider the problem where the roles of
human and robot are swapped. Our main contribution is to recognize that unlike
robots, humans will not be exact in their IRL inference. We thus introduce two
factors to define candidate approximate-inference models for human learning in
this setting, and analyze them in a user study in the autonomous driving
domain. We show that certain approximate-inference models lead to the robot
generating example behaviors that better enable users to anticipate what it
will do in novel situations. Our results also suggest, however, that additional
research is needed in modeling how humans extrapolate from examples of robot
behavior.Comment: RSS 201
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
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