36 research outputs found
Practical Bayesian optimization in the presence of outliers
Inference in the presence of outliers is an important field of research as
outliers are ubiquitous and may arise across a variety of problems and domains.
Bayesian optimization is method that heavily relies on probabilistic inference.
This allows outstanding sample efficiency because the probabilistic machinery
provides a memory of the whole optimization process. However, that virtue
becomes a disadvantage when the memory is populated with outliers, inducing
bias in the estimation. In this paper, we present an empirical evaluation of
Bayesian optimization methods in the presence of outliers. The empirical
evidence shows that Bayesian optimization with robust regression often produces
suboptimal results. We then propose a new algorithm which combines robust
regression (a Gaussian process with Student-t likelihood) with outlier
diagnostics to classify data points as outliers or inliers. By using an
scheduler for the classification of outliers, our method is more efficient and
has better convergence over the standard robust regression. Furthermore, we
show that even in controlled situations with no expected outliers, our method
is able to produce better results.Comment: 10 pages (2 of references), 6 figures, 1 algorith
Unscented Bayesian Optimization for Safe Robot Grasping
We address the robot grasp optimization problem of unknown objects
considering uncertainty in the input space. Grasping unknown objects can be
achieved by using a trial and error exploration strategy. Bayesian optimization
is a sample efficient optimization algorithm that is especially suitable for
this setups as it actively reduces the number of trials for learning about the
function to optimize. In fact, this active object exploration is the same
strategy that infants do to learn optimal grasps. One problem that arises while
learning grasping policies is that some configurations of grasp parameters may
be very sensitive to error in the relative pose between the object and robot
end-effector. We call these configurations unsafe because small errors during
grasp execution may turn good grasps into bad grasps. Therefore, to reduce the
risk of grasp failure, grasps should be planned in safe areas. We propose a new
algorithm, Unscented Bayesian optimization that is able to perform sample
efficient optimization while taking into consideration input noise to find safe
optima. The contribution of Unscented Bayesian optimization is twofold as if
provides a new decision process that drives exploration to safe regions and a
new selection procedure that chooses the optimal in terms of its safety without
extra analysis or computational cost. Both contributions are rooted on the
strong theory behind the unscented transformation, a popular nonlinear
approximation method. We show its advantages with respect to the classical
Bayesian optimization both in synthetic problems and in realistic robot grasp
simulations. The results highlights that our method achieves optimal and robust
grasping policies after few trials while the selected grasps remain in safe
regions.Comment: conference pape
Fully Distributed Bayesian Optimization with Stochastic Policies
Bayesian optimization has become a popular method for high-throughput
computing, like the design of computer experiments or hyperparameter tuning of
expensive models, where sample efficiency is mandatory. In these applications,
distributed and scalable architectures are a necessity. However, Bayesian
optimization is mostly sequential. Even parallel variants require certain
computations between samples, limiting the parallelization bandwidth. Thompson
sampling has been previously applied for distributed Bayesian optimization.
But, when compared with other acquisition functions in the sequential setting,
Thompson sampling is known to perform suboptimally. In this paper, we present a
new method for fully distributed Bayesian optimization, which can be combined
with any acquisition function. Our approach considers Bayesian optimization as
a partially observable Markov decision process. In this context, stochastic
policies, such as the Boltzmann policy, have some interesting properties which
can also be studied for Bayesian optimization. Furthermore, the Boltzmann
policy trivially allows a distributed Bayesian optimization implementation with
high level of parallelism and scalability. We present results in several
benchmarks and applications that shows the performance of our method
Multi-label affordance mapping from egocentric vision
Accurate affordance detection and segmentation with pixel precision is an
important piece in many complex systems based on interactions, such as robots
and assitive devices. We present a new approach to affordance perception which
enables accurate multi-label segmentation. Our approach can be used to
automatically extract grounded affordances from first person videos of
interactions using a 3D map of the environment providing pixel level precision
for the affordance location. We use this method to build the largest and most
complete dataset on affordances based on the EPIC-Kitchen dataset, EPIC-Aff,
which provides interaction-grounded, multi-label, metric and spatial affordance
annotations. Then, we propose a new approach to affordance segmentation based
on multi-label detection which enables multiple affordances to co-exists in the
same space, for example if they are associated with the same object. We present
several strategies of multi-label detection using several segmentation
architectures. The experimental results highlight the importance of the
multi-label detection. Finally, we show how our metric representation can be
exploited for build a map of interaction hotspots in spatial action-centric
zones and use that representation to perform a task-oriented navigation.Comment: International Conference on Computer Vision (ICCV) 202