46 research outputs found
A New Perspective and Extension of the Gaussian Filter
The Gaussian Filter (GF) is one of the most widely used filtering algorithms;
instances are the Extended Kalman Filter, the Unscented Kalman Filter and the
Divided Difference Filter. GFs represent the belief of the current state by a
Gaussian with the mean being an affine function of the measurement. We show
that this representation can be too restrictive to accurately capture the
dependences in systems with nonlinear observation models, and we investigate
how the GF can be generalized to alleviate this problem. To this end, we view
the GF from a variational-inference perspective. We analyse how restrictions on
the form of the belief can be relaxed while maintaining simplicity and
efficiency. This analysis provides a basis for generalizations of the GF. We
propose one such generalization which coincides with a GF using a virtual
measurement, obtained by applying a nonlinear function to the actual
measurement. Numerical experiments show that the proposed Feature Gaussian
Filter (FGF) can have a substantial performance advantage over the standard GF
for systems with nonlinear observation models.Comment: Will appear in Robotics: Science and Systems (R:SS) 201
The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems
Parametric filters, such as the Extended Kalman Filter and the Unscented
Kalman Filter, typically scale well with the dimensionality of the problem, but
they are known to fail if the posterior state distribution cannot be closely
approximated by a density of the assumed parametric form. For nonparametric
filters, such as the Particle Filter, the converse holds. Such methods are able
to approximate any posterior, but the computational requirements scale
exponentially with the number of dimensions of the state space. In this paper,
we present the Coordinate Particle Filter which alleviates this problem. We
propose to compute the particle weights recursively, dimension by dimension.
This allows us to explore one dimension at a time, and resample after each
dimension if necessary. Experimental results on simulated as well as real data
confirm that the proposed method has a substantial performance advantage over
the Particle Filter in high-dimensional systems where not all dimensions are
highly correlated. We demonstrate the benefits of the proposed method for the
problem of multi-object and robotic manipulator tracking
Robust Gaussian Filtering using a Pseudo Measurement
Many sensors, such as range, sonar, radar, GPS and visual devices, produce
measurements which are contaminated by outliers. This problem can be addressed
by using fat-tailed sensor models, which account for the possibility of
outliers. Unfortunately, all estimation algorithms belonging to the family of
Gaussian filters (such as the widely-used extended Kalman filter and unscented
Kalman filter) are inherently incompatible with such fat-tailed sensor models.
The contribution of this paper is to show that any Gaussian filter can be made
compatible with fat-tailed sensor models by applying one simple change: Instead
of filtering with the physical measurement, we propose to filter with a pseudo
measurement obtained by applying a feature function to the physical
measurement. We derive such a feature function which is optimal under some
conditions. Simulation results show that the proposed method can effectively
handle measurement outliers and allows for robust filtering in both linear and
nonlinear systems
Representation with Incomplete Votes
Platforms for online civic participation rely heavily on methods for
condensing thousands of comments into a relevant handful, based on whether
participants agree or disagree with them. These methods should guarantee fair
representation of the participants, as their outcomes may affect the health of
the conversation and inform impactful downstream decisions. To that end, we
draw on the literature on approval-based committee elections. Our setting is
novel in that the approval votes are incomplete since participants will
typically not vote on all comments. We prove that this complication renders
non-adaptive algorithms impractical in terms of the amount of information they
must gather. Therefore, we develop an adaptive algorithm that uses information
more efficiently by presenting incoming participants with statements that
appear promising based on votes by previous participants. We prove that this
method satisfies commonly used notions of fair representation, even when
participants only vote on a small fraction of comments. Finally, an empirical
evaluation using real data shows that the proposed algorithm provides
representative outcomes in practice
Generative Social Choice
Traditionally, social choice theory has only been applicable to choices among
a few predetermined alternatives but not to more complex decisions such as
collectively selecting a textual statement. We introduce generative social
choice, a framework that combines the mathematical rigor of social choice
theory with large language models' capability to generate text and extrapolate
preferences. This framework divides the design of AI-augmented democratic
processes into two components: first, proving that the process satisfies
rigorous representation guarantees when given access to oracle queries; second,
empirically validating that these queries can be approximately implemented
using a large language model. We illustrate this framework by applying it to
the problem of generating a slate of statements that is representative of
opinions expressed as free-form text, for instance in an online deliberative
process
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
Learning policies from previously recorded data is a promising direction for
real-world robotics tasks, as online learning is often infeasible. Dexterous
manipulation in particular remains an open problem in its general form. The
combination of offline reinforcement learning with large diverse datasets,
however, has the potential to lead to a breakthrough in this challenging domain
analogously to the rapid progress made in supervised learning in recent years.
To coordinate the efforts of the research community toward tackling this
problem, we propose a benchmark including: i) a large collection of data for
offline learning from a dexterous manipulation platform on two tasks, obtained
with capable RL agents trained in simulation; ii) the option to execute learned
policies on a real-world robotic system and a simulation for efficient
debugging. We evaluate prominent open-sourced offline reinforcement learning
algorithms on the datasets and provide a reproducible experimental setup for
offline reinforcement learning on real systems.Comment: The Eleventh International Conference on Learning Representations.
2022. Published at ICLR 2023. Datasets available at
https://github.com/rr-learning/trifinger_rl_dataset