589 research outputs found
Online Update of Safety Assurances Using Confidence-Based Predictions
Robots such as autonomous vehicles and assistive manipulators are
increasingly operating in dynamic environments and close physical proximity to
people. In such scenarios, the robot can leverage a human motion predictor to
predict their future states and plan safe and efficient trajectories. However,
no model is ever perfect -- when the observed human behavior deviates from the
model predictions, the robot might plan unsafe maneuvers. Recent works have
explored maintaining a confidence parameter in the human model to overcome this
challenge, wherein the predicted human actions are tempered online based on the
likelihood of the observed human action under the prediction model. This has
opened up a new research challenge, i.e., \textit{how to compute the future
human states online as the confidence parameter changes?} In this work, we
propose a Hamilton-Jacobi (HJ) reachability-based approach to overcome this
challenge. Treating the confidence parameter as a virtual state in the system,
we compute a parameter-conditioned forward reachable tube (FRT) that provides
the future human states as a function of the confidence parameter. Online, as
the confidence parameter changes, we can simply query the corresponding FRT,
and use it to update the robot plan. Computing parameter-conditioned FRT
corresponds to an (offline) high-dimensional reachability problem, which we
solve by leveraging recent advances in data-driven reachability analysis.
Overall, our framework enables online maintenance and updates of safety
assurances in human-robot interaction scenarios, even when the human prediction
model is incorrect. We demonstrate our approach in several safety-critical
autonomous driving scenarios, involving a state-of-the-art deep learning-based
prediction model.Comment: 7 pages, 3 figure
Parameter-Conditioned Reachable Sets for Updating Safety Assurances Online
Hamilton-Jacobi (HJ) reachability analysis is a powerful tool for analyzing
the safety of autonomous systems. However, the provided safety assurances are
often predicated on the assumption that once deployed, the system or its
environment does not evolve. Online, however, an autonomous system might
experience changes in system dynamics, control authority, external
disturbances, and/or the surrounding environment, requiring updated safety
assurances. Rather than restarting the safety analysis from scratch, which can
be time-consuming and often intractable to perform online, we propose to
compute \textit{parameter-conditioned} reachable sets. Assuming expected system
and environment changes can be parameterized, we treat these parameters as
virtual states in the system and leverage recent advances in high-dimensional
reachability analysis to solve the corresponding reachability problem offline.
This results in a family of reachable sets that is parameterized by the
environment and system factors. Online, as these factors change, the system can
simply query the corresponding safety function from this family to ensure
system safety, enabling a real-time update of the safety assurances. Through
various simulation studies, we demonstrate the capability of our approach in
maintaining system safety despite the system and environment evolution
Decentralized Learning With Limited Communications for Multi-robot Coverage of Unknown Spatial Fields
This paper presents an algorithm for a team of mobile robots to
simultaneously learn a spatial field over a domain and spatially distribute
themselves to optimally cover it. Drawing from previous approaches that
estimate the spatial field through a centralized Gaussian process, this work
leverages the spatial structure of the coverage problem and presents a
decentralized strategy where samples are aggregated locally by establishing
communications through the boundaries of a Voronoi partition. We present an
algorithm whereby each robot runs a local Gaussian process calculated from its
own measurements and those provided by its Voronoi neighbors, which are
incorporated into the individual robot's Gaussian process only if they provide
sufficiently novel information. The performance of the algorithm is evaluated
in simulation and compared with centralized approaches.Comment: Accepted IROS 202
Generative Adversarial Networks via a Composite Annealing of Noise and Diffusion
Generative adversarial network (GAN) is a framework for generating fake data
using a set of real examples. However, GAN is unstable in the training stage.
In order to stabilize GANs, the noise injection has been used to enlarge the
overlap of the real and fake distributions at the cost of increasing variance.
The diffusion (or smoothing) may reduce the intrinsic underlying dimensionality
of data but it suppresses the capability of GANs to learn high-frequency
information in the training procedure. Based on these observations, we propose
a data representation for the GAN training, called noisy scale-space (NSS),
that recursively applies the smoothing with a balanced noise to data in order
to replace the high-frequency information by random data, leading to a
coarse-to-fine training of GANs. We experiment with NSS using DCGAN and
StyleGAN2 based on benchmark datasets in which the NSS-based GANs outperforms
the state-of-the-arts in most cases
Demonstration of geometric diabatic control of quantum states
Geometric effects can play a pivotal role in streamlining quantum
manipulation. We demonstrate a geometric diabatic control, that is, perfect
tunneling between spin states in a diamond by a quadratic sweep of a driving
field. The field sweep speed for the perfect tunneling is determined by the
geometric amplitude factor and can be tuned arbitrarily. Our results are
obtained by testing a quadratic version of Berry's twisted Landau-Zener model.
This geometric tuning is robust over a wide parameter range. Our work provides
a basis for quantum control in various systems, including condensed matter
physics, quantum computation, and nuclear magnetic resonance
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