96,446 research outputs found
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs
This work studies the problem of stochastic dynamic filtering and state
propagation with complex beliefs. The main contribution is GP-SUM, a filtering
algorithm tailored to dynamic systems and observation models expressed as
Gaussian Processes (GP), and to states represented as a weighted sum of
Gaussians. The key attribute of GP-SUM is that it does not rely on
linearizations of the dynamic or observation models, or on unimodal Gaussian
approximations of the belief, hence enables tracking complex state
distributions. The algorithm can be seen as a combination of a sampling-based
filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by
sampling the state distribution and propagating each sample through the dynamic
system and observation models. On the other hand, it achieves effective
sampling and accurate probabilistic propagation by relying on the GP form of
the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM
outperforms several GP-Bayes and Particle Filters on a standard benchmark. We
also demonstrate its use in a pushing task, predicting with experimental
accuracy the naturally occurring non-Gaussian distributions.Comment: WAFR 2018, 16 pages, 7 figure
Policy Learning with Hypothesis based Local Action Selection
For robots to be able to manipulate in unknown and unstructured environments
the robot should be capable of operating under partial observability of the
environment. Object occlusions and unmodeled environments are some of the
factors that result in partial observability. A common scenario where this is
encountered is manipulation in clutter. In the case that the robot needs to
locate an object of interest and manipulate it, it needs to perform a series of
decluttering actions to accurately detect the object of interest. To perform
such a series of actions, the robot also needs to account for the dynamics of
objects in the environment and how they react to contact. This is a non trivial
problem since one needs to reason not only about robot-object interactions but
also object-object interactions in the presence of contact. In the example
scenario of manipulation in clutter, the state vector would have to account for
the pose of the object of interest and the structure of the surrounding
environment. The process model would have to account for all the aforementioned
robot-object, object-object interactions. The complexity of the process model
grows exponentially as the number of objects in the scene increases. This is
commonly the case in unstructured environments. Hence it is not reasonable to
attempt to model all object-object and robot-object interactions explicitly.
Under this setting we propose a hypothesis based action selection algorithm
where we construct a hypothesis set of the possible poses of an object of
interest given the current evidence in the scene and select actions based on
our current set of hypothesis. This hypothesis set tends to represent the
belief about the structure of the environment and the number of poses the
object of interest can take. The agent's only stopping criterion is when the
uncertainty regarding the pose of the object is fully resolved.Comment: RLDM abstrac
Differentiable Algorithm Networks for Composable Robot Learning
This paper introduces the Differentiable Algorithm Network (DAN), a
composable architecture for robot learning systems. A DAN is composed of neural
network modules, each encoding a differentiable robot algorithm and an
associated model; and it is trained end-to-end from data. DAN combines the
strengths of model-driven modular system design and data-driven end-to-end
learning. The algorithms and models act as structural assumptions to reduce the
data requirements for learning; end-to-end learning allows the modules to adapt
to one another and compensate for imperfect models and algorithms, in order to
achieve the best overall system performance. We illustrate the DAN methodology
through a case study on a simulated robot system, which learns to navigate in
complex 3-D environments with only local visual observations and an image of a
partially correct 2-D floor map.Comment: RSS 2019 camera ready. Video is available at
https://youtu.be/4jcYlTSJF4
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