16 research outputs found
The Pioneer Anomaly
Radio-metric Doppler tracking data received from the Pioneer 10 and 11
spacecraft from heliocentric distances of 20-70 AU has consistently indicated
the presence of a small, anomalous, blue-shifted frequency drift uniformly
changing with a rate of ~6 x 10^{-9} Hz/s. Ultimately, the drift was
interpreted as a constant sunward deceleration of each particular spacecraft at
the level of a_P = (8.74 +/- 1.33) x 10^{-10} m/s^2. This apparent violation of
the Newton's gravitational inverse-square law has become known as the Pioneer
anomaly; the nature of this anomaly remains unexplained. In this review, we
summarize the current knowledge of the physical properties of the anomaly and
the conditions that led to its detection and characterization. We review
various mechanisms proposed to explain the anomaly and discuss the current
state of efforts to determine its nature. A comprehensive new investigation of
the anomalous behavior of the two Pioneers has begun recently. The new efforts
rely on the much-extended set of radio-metric Doppler data for both spacecraft
in conjunction with the newly available complete record of their telemetry
files and a large archive of original project documentation. As the new study
is yet to report its findings, this review provides the necessary background
for the new results to appear in the near future. In particular, we provide a
significant amount of information on the design, operations and behavior of the
two Pioneers during their entire missions, including descriptions of various
data formats and techniques used for their navigation and radio-science data
analysis. As most of this information was recovered relatively recently, it was
not used in the previous studies of the Pioneer anomaly, but it is critical for
the new investigation.Comment: 165 pages, 40 figures, 16 tables; accepted for publication in Living
Reviews in Relativit
Inverse KKT: Learning cost functions of manipulation tasks from demonstrations
Inverse Optimal Control (IOC) assumes that demonstrations are the solution to an optimal control problem with unknown underlying costs, and extracts parameters of these underlying costs. We propose the framework of Inverse KKT, which assumes that the demonstrations fulfill the Karush-Kuhn-Tucker conditions of an unknown underlying constrained optimization problem, and extracts parameters of this underlying problem. Using this we can exploit the latter to extract the relevant task spaces and parameters of a cost function for skills that involve contacts. For a typical linear parameterization of cost functions this reduces to a quadratic program, ensuring guaranteed and very efficient convergence, but we can deal also with arbitrary non-linear parameterizations of cost functions. We also present anonparametric variant of inverse KKT that represents the cost function as a functional in reproducing kernel Hilbert spaces. The aim of our approach is to push learning from demonstration to more complex manipulation scenarios that include the interaction with objects and therefore the realization of contacts/constraints within the motion. We demonstrate the approach on manipulation tasks such as sliding a box, closing a drawer and opening a door
Large-scale cost function learning for path planning using deep inverse reinforcement learning
We present an approach for learning spatial traversability maps for driving in complex, urban environments based on an extensive dataset demonstrating the driving behaviour of human experts. The direct end-to-end mapping from raw input data to cost bypasses the effort of manually designing parts of the pipeline, exploits a large number of data samples, and can be framed additionally to refine handcrafted cost maps produced based on manual hand-engineered features. To achieve this, we introduce a maximum-entropy-based, non-linear inverse reinforcement learning (IRL) framework which exploits the capacity of fully convolutional neural networks (FCNs) to represent the cost model underlying driving behaviours. The application of a high-capacity, deep, parametric approach successfully scales to more complex environments and driving behaviours, while at deployment being run-time independent of training dataset size. After benchmarking against state-of-the-art IRL approaches, we focus on demonstrating scalability and performance on an ambitious dataset collected over the course of 1 year including more than 25,000 demonstration trajectories extracted from over 120 km of urban driving. We evaluate the resulting cost representations by showing the advantages over a carefully, manually designed cost map and furthermore demonstrate its robustness towards systematic errors by learning accurate representations even in the presence of calibration perturbations. Importantly, we demonstrate that a manually designed cost map can be refined to more accurately handle corner cases that are scarcely seen in the environment, such as stairs, slopes and underpasses, by further incorporating human priors into the training framework