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Model Predictive Control (MPC) is a popular strategy for controlling robots but is difficult for systems with contact due to the complex nature of hybrid dynamics. To implement MPC for systems with contact, dynamic models are often simplified or contact sequences fixed in time in order to plan trajectories efficiently. In this work, we propose the Hybrid iterative Linear Quadratic Regulator (HiLQR), which extends iLQR to a class of piecewisesmooth hybrid dynamical systems with state jumps. This is accomplished by 1) allowing for changing hybrid modes in the forward pass, 2) using the saltation matrix to update the gradient information in the backwards pass, and 3) using a reference extension to account for mode mismatch. We demonstrate these changes on a variety of hybrid systems and compare the different strategies for computing the gradients. We further show how HiLQR can work in a MPC fashion (HiLQR MPC) by 1) modifying how the cost function is computed when contact modes do not align, 2) utilizing parallelizations when simulating rigid body dynamics, and 3) using efficient analytical derivative computations of the rigid body dynamics. The result is a system that can modify the contact sequence of the reference behavior and plan whole body motions cohesively – which is crucial when dealing with large perturbations. HiLQR MPC is tested on two systems: first, the hybrid cost modification is validated on a simple actuated bouncing ball hybrid system. Then HiLQR MPC is compared against methods that utilize centroidal dynamic assumptions on a quadruped robot (Unitree A1). HiLQR MPC outperforms the centroidal methods in both simulation and hardware tests. 

Entanglements like vines and branches in natural settings or cords and pipes in human spaces prevent mobile robots from accessing many environments. Legged robots should be effective in these settings, and more so than wheeled or tracked platforms, but naive controllers quickly become entangled and stuck. In this paper we present a method for proprioception aimed specifically at the task of sensing entanglements of a robot’s legs as well as a reaction strategy to disentangle legs during their swing phase as they advance to their next foothold. We demonstrate our proprioception and reaction strategy enables traversal of entanglements of many stiffnesses and geometries succeeding in 14 out of 16 trials in laboratory tests, as well as a natural outdoor environment. 

The standard Bayesian technique for searching pulsar timing data for gravitational-wave bursts with memory (BWMs) using Markov Chain Monte Carlo (MCMC) sampling is very computationally expensive to perform. In this paper, we explain the implementation of an efficient Bayesian technique for searching for BWMs. This technique makes use of the fact that the signal model for Earth-term BWMs (BWMs passing over the Earth) is fully factorizable. We estimate that this implementation reduces the computational complexity by a factor of 100. We also demonstrate that this technique gives upper limits consistent with published results using the standard Bayesian technique, and may be used to perform all of the same analyses of BWMs that standard MCMC techniques can perform.

 

In this paper, we present a method for updating robotic state belief through contact with uncertain surfaces and apply this update to a Kalman filter for more accurate state estimation. Examining how guard surface uncertainty affects the time spent in each mode, we derive a novel guard saltation matrix- which maps perturbations prior to hybrid events to perturbations after - accounting for additional variation in the resulting state. Additionally, we propose the use of parameterized reset functions - capturing how unknown parameters change how states are mapped from one mode to the next - the Jacobian of which accounts for additional uncertainty in the resulting state. The accuracy of these mappings is shown by simulating sampled distributions through uncertain transition events and comparing the resulting covariances. Finally, we integrate these additional terms into the “uncertainty aware Salted Kalman Filter”, uaSKF, and show a peak reduction in average estimation error by 24–60% on a variety of test conditions and systems. 

Abstract There is a growing interest in the use of ground-based remote sensors for Numerical Weather Prediction (NWP), which is sparked by their potential to address the currently existing observation gap within the Planetary Boundary Layer (PBL). Nevertheless, open questions still exist regarding the relative importance of and synergy among various instrument types. To shed light on these important questions, the present study examines the forecast benefits associated with several different ground-based profiling networks using 10 diverse cases from the Plains Elevated Convection at Night (PECAN) field campaign. Aggregated verification statistics reveal that a combination of in situ and remote sensing profilers leads to the largest increase in forecast skill, both in terms of the parent mesoscale convective system and the explicitly resolved bore. These statistics also indicate that it is often advantageous to collocate thermodynamic and kinematic remote sensors. By contrast, the impacts of networks consisting of single profilers appear to be flow-dependent, with thermodynamic (kinematic) remote sensors being most useful in cases with relatively low (high) convective predictability. Deficiencies in the data assimilation method as well as inherent complexities in the governing moisture dynamics are two factors shown to limit the forecast value extracted from such networks. 

Many controllers for legged robotic systems leverage open- or closed-loop control at discrete hybrid events to enhance stability. These controllers appear in several well studied phenomena such as the Raibert stepping controller, paddle juggling, and swing leg retraction. This work introduces hybrid event shaping (HES): a generalized method for analyzing and designing stable hybrid event controllers. HES utilizes the saltation matrix, which gives a closed-form equation for the effect that hybrid events have on stability. We also introduce shape parameters, which are higher order terms that can be tuned completely independently of the system dynamics to promote stability. Optimization methods are used to produce values of these parameters that optimize a stability measure. Hybrid event shaping captures previously developed control methods while also producing new optimally stable trajectories without the need for continuous-domain feedback. 

Methods for state estimation that rely on visual information are challenging on legged robots due to rapid changes in the viewing angle of onboard cameras. In this work, we show that by leveraging structure in the way that the robot locomotes, the accuracy of visual-inertial SLAM in these challenging scenarios can be increased. We present a method that takes advantage of the underlying periodic predictability often present in the motion of legged robots to improve the performance of the feature tracking module within a visual-inertial SLAM system. Our method performs multi-session SLAM on a single robot, where each session is responsible for mapping during a distinct portion of the robot’s gait cycle. Our method produces lower absolute trajectory error than several state-of-the-art methods for visual-inertial SLAM in both a simulated environment and on data collected on a quadrupedal robot executing dynamic gaits. On real-world bounding gaits, our median trajectory error was less than 35% of the error of the next best estimate provided by state-of-the-art methods.