5,270 research outputs found
On Approximate Nonlinear Gaussian Message Passing On Factor Graphs
Factor graphs have recently gained increasing attention as a unified
framework for representing and constructing algorithms for signal processing,
estimation, and control. One capability that does not seem to be well explored
within the factor graph tool kit is the ability to handle deterministic
nonlinear transformations, such as those occurring in nonlinear filtering and
smoothing problems, using tabulated message passing rules. In this
contribution, we provide general forward (filtering) and backward (smoothing)
approximate Gaussian message passing rules for deterministic nonlinear
transformation nodes in arbitrary factor graphs fulfilling a Markov property,
based on numerical quadrature procedures for the forward pass and a
Rauch-Tung-Striebel-type approximation of the backward pass. These message
passing rules can be employed for deriving many algorithms for solving
nonlinear problems using factor graphs, as is illustrated by the proposition of
a nonlinear modified Bryson-Frazier (MBF) smoother based on the presented
message passing rules
A Unifying Variational Framework for Gaussian Process Motion Planning
To control how a robot moves, motion planning algorithms must compute paths
in high-dimensional state spaces while accounting for physical constraints
related to motors and joints, generating smooth and stable motions, avoiding
obstacles, and preventing collisions. A motion planning algorithm must
therefore balance competing demands, and should ideally incorporate uncertainty
to handle noise, model errors, and facilitate deployment in complex
environments. To address these issues, we introduce a framework for robot
motion planning based on variational Gaussian Processes, which unifies and
generalizes various probabilistic-inference-based motion planning algorithms.
Our framework provides a principled and flexible way to incorporate
equality-based, inequality-based, and soft motion-planning constraints during
end-to-end training, is straightforward to implement, and provides both
interval-based and Monte-Carlo-based uncertainty estimates. We conduct
experiments using different environments and robots, comparing against baseline
approaches based on the feasibility of the planned paths, and obstacle
avoidance quality. Results show that our proposed approach yields a good
balance between success rates and path quality
Social Attention: Modeling Attention in Human Crowds
Robots that navigate through human crowds need to be able to plan safe,
efficient, and human predictable trajectories. This is a particularly
challenging problem as it requires the robot to predict future human
trajectories within a crowd where everyone implicitly cooperates with each
other to avoid collisions. Previous approaches to human trajectory prediction
have modeled the interactions between humans as a function of proximity.
However, that is not necessarily true as some people in our immediate vicinity
moving in the same direction might not be as important as other people that are
further away, but that might collide with us in the future. In this work, we
propose Social Attention, a novel trajectory prediction model that captures the
relative importance of each person when navigating in the crowd, irrespective
of their proximity. We demonstrate the performance of our method against a
state-of-the-art approach on two publicly available crowd datasets and analyze
the trained attention model to gain a better understanding of which surrounding
agents humans attend to, when navigating in a crowd
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