121 research outputs found
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
Bayesian Optimisation for Safe Navigation under Localisation Uncertainty
In outdoor environments, mobile robots are required to navigate through
terrain with varying characteristics, some of which might significantly affect
the integrity of the platform. Ideally, the robot should be able to identify
areas that are safe for navigation based on its own percepts about the
environment while avoiding damage to itself. Bayesian optimisation (BO) has
been successfully applied to the task of learning a model of terrain
traversability while guiding the robot through more traversable areas. An
issue, however, is that localisation uncertainty can end up guiding the robot
to unsafe areas and distort the model being learnt. In this paper, we address
this problem and present a novel method that allows BO to consider localisation
uncertainty by applying a Gaussian process model for uncertain inputs as a
prior. We evaluate the proposed method in simulation and in experiments with a
real robot navigating over rough terrain and compare it against standard BO
methods.Comment: To appear in the proceedings of the 18th International Symposium on
Robotics Research (ISRR 2017
Grasp Stability Prediction for a Dexterous Robotic Hand Combining Depth Vision and Haptic Bayesian Exploration.
Grasp stability prediction of unknown objects is crucial to enable autonomous robotic manipulation in an unstructured environment. Even if prior information about the object is available, real-time local exploration might be necessary to mitigate object modelling inaccuracies. This paper presents an approach to predict safe grasps of unknown objects using depth vision and a dexterous robot hand equipped with tactile feedback. Our approach does not assume any prior knowledge about the objects. First, an object pose estimation is obtained from RGB-D sensing; then, the object is explored haptically to maximise a given grasp metric. We compare two probabilistic methods (i.e. standard and unscented Bayesian Optimisation) against random exploration (i.e. uniform grid search). Our experimental results demonstrate that these probabilistic methods can provide confident predictions after a limited number of exploratory observations, and that unscented Bayesian Optimisation can find safer grasps, taking into account the uncertainty in robot sensing and grasp execution
Sense, Think, Grasp: A study on visual and tactile information processing for autonomous manipulation
Interacting with the environment using hands is one of the distinctive
abilities of humans with respect to other species. This aptitude reflects on
the crucial role played by objects\u2019 manipulation in the world that we have
shaped for us. With a view of bringing robots outside industries for supporting
people during everyday life, the ability of manipulating objects
autonomously and in unstructured environments is therefore one of the basic
skills they need. Autonomous manipulation is characterized by great
complexity especially regarding the processing of sensors information to
perceive the surrounding environment. Humans rely on vision for wideranging
tridimensional information, prioprioception for the awareness of
the relative position of their own body in the space and the sense of touch
for local information when physical interaction with objects happens. The
study of autonomous manipulation in robotics aims at transferring similar
perceptive skills to robots so that, combined with state of the art control
techniques, they could be able to achieve similar performance in manipulating
objects. The great complexity of this task makes autonomous
manipulation one of the open problems in robotics that has been drawing
increasingly the research attention in the latest years.
In this work of Thesis, we propose possible solutions to some key components
of autonomous manipulation, focusing in particular on the perception
problem and testing the developed approaches on the humanoid robotic platform iCub. When available, vision is the first source of information
to be processed for inferring how to interact with objects. The object
modeling and grasping pipeline based on superquadric functions we designed
meets this need, since it reconstructs the object 3D model from partial
point cloud and computes a suitable hand pose for grasping the object.
Retrieving objects information with touch sensors only is a relevant skill
that becomes crucial when vision is occluded, as happens for instance during
physical interaction with the object. We addressed this problem with
the design of a novel tactile localization algorithm, named Memory Unscented
Particle Filter, capable of localizing and recognizing objects relying solely
on 3D contact points collected on the object surface. Another key point of
autonomous manipulation we report on in this Thesis work is bi-manual
coordination. The execution of more advanced manipulation tasks in fact
might require the use and coordination of two arms. Tool usage for instance
often requires a proper in-hand object pose that can be obtained via
dual-arm re-grasping. In pick-and-place tasks sometimes the initial and
target position of the object do not belong to the same arm workspace, then
requiring to use one hand for lifting the object and the other for locating it
in the new position. At this regard, we implemented a pipeline for executing
the handover task, i.e. the sequences of actions for autonomously passing an
object from one robot hand on to the other.
The contributions described thus far address specific subproblems of
the more complex task of autonomous manipulation. This actually differs
from what humans do, in that humans develop their manipulation
skills by learning through experience and trial-and-error strategy. Aproper
mathematical formulation for encoding this learning approach is given by
Deep Reinforcement Learning, that has recently proved to be successful in
many robotics applications. For this reason, in this Thesis we report also
on the six month experience carried out at Berkeley Artificial Intelligence
Research laboratory with the goal of studying Deep Reinforcement Learning
and its application to autonomous manipulation
Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects
This paper addresses the problem of simultaneously exploring an unknown
object to model its shape, using tactile sensors on robotic fingers, while also
improving finger placement to optimise grasp stability. In many situations, a
robot will have only a partial camera view of the near side of an observed
object, for which the far side remains occluded. We show how an initial grasp
attempt, based on an initial guess of the overall object shape, yields tactile
glances of the far side of the object which enable the shape estimate and
consequently the successive grasps to be improved. We propose a grasp
exploration approach using a probabilistic representation of shape, based on
Gaussian Process Implicit Surfaces. This representation enables initial partial
vision data to be augmented with additional data from successive tactile
glances. This is combined with a probabilistic estimate of grasp quality to
refine grasp configurations. When choosing the next set of finger placements, a
bi-objective optimisation method is used to mutually maximise grasp quality and
improve shape representation during successive grasp attempts. Experimental
results show that the proposed approach yields stable grasp configurations more
efficiently than a baseline method, while also yielding improved shape estimate
of the grasped object.Comment: IEEE Robotics and Automation Letters. Preprint Version. Accepted
February, 202
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
Adversarially Robust Optimization with Gaussian Processes
In this paper, we consider the problem of Gaussian process (GP) optimization
with an added robustness requirement: The returned point may be perturbed by an
adversary, and we require the function value to remain as high as possible even
after this perturbation. This problem is motivated by settings in which the
underlying functions during optimization and implementation stages are
different, or when one is interested in finding an entire region of good inputs
rather than only a single point. We show that standard GP optimization
algorithms do not exhibit the desired robustness properties, and provide a
novel confidence-bound based algorithm StableOpt for this purpose. We
rigorously establish the required number of samples for StableOpt to find a
near-optimal point, and we complement this guarantee with an
algorithm-independent lower bound. We experimentally demonstrate several
potential applications of interest using real-world data sets, and we show that
StableOpt consistently succeeds in finding a stable maximizer where several
baseline methods fail.Comment: Corrected typo
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