Reentrant nu = 1 quantum Hall state in a two-dimensional hole system

We report the observation of a reentrant quantum Hall state at the Landau level filling factor nu = 1 in a two-dimensional hole system confined to a 35-nm-wide (001) GaAs quantum well. The reentrant behavior is characterized by a weakening and eventual collapse of the nu = 1 quantum Hall state in the presence of a parallel magnetic field component B||, followed by a strengthening and reemergence as B|| is further increased. The robustness of the nu = 1 quantum Hall state during the transition depends strongly on the charge distribution symmetry of the quantum well, while the magnitude of B|| needed to invoke the transition increases with the total density of the system

Attack of \u3ci\u3eUrophora Quadrifasciata\u3c/i\u3e (Meig.) (Diiptera: Tephritidae) A Biological Control Agent for Spotted Knapweed (\u3ci\u3eCentaurea Maculosa\u3c/i\u3e Lamarck) and Diffuse Knapweed (\u3ci\u3eC. Diffusa\u3c/i\u3e Lamarck) (Asteraceae) by a Parasitoid, \u3ci\u3ePteromalus\u3c/i\u3e Sp. (Hymenoptera: Pteromalidae) in Michigan

Urophora quadrifasciata (Meig.) a seedhead fly released in North America for biological control of Centaurea maculosa and C. diffusa is parasitized by a Pteromalus sp. Parasitism up to 60% of U. quadrifasciata was found in samples of seed heads of C. maculosa and C. diffusa collected from 54 of the 59 counties sampled in Michigan and in one sample of C. maculosa seed heads from Hennepin County, Minnesota. Parasitism of U. quadrifasciata has rarely been reported

Trajectory Optimization Through Contacts and Automatic Gait Discovery for Quadrupeds

In this work we present a trajectory Optimization framework for whole-body motion planning through contacts. We demonstrate how the proposed approach can be applied to automatically discover different gaits and dynamic motions on a quadruped robot. In contrast to most previous methods, we do not pre-specify contact switches, timings, points or gait patterns, but they are a direct outcome of the optimization. Furthermore, we optimize over the entire dynamics of the robot, which enables the optimizer to fully leverage the capabilities of the robot. To illustrate the spectrum of achievable motions, here we show eight different tasks, which would require very different control structures when solved with state-of-the-art methods. Using our trajectory Optimization approach, we are solving each task with a simple, high level cost function and without any changes in the control structure. Furthermore, we fully integrated our approach with the robot's control and estimation framework such that optimization can be run online. By demonstrating a rough manipulation task with multiple dynamic contact switches, we exemplarily show how optimized trajectories and control inputs can be directly applied to hardware.Comment: Video: https://youtu.be/sILuqJBsyK

Robust Whole-Body Motion Control of Legged Robots

We introduce a robust control architecture for the whole-body motion control of torque controlled robots with arms and legs. The method is based on the robust control of contact forces in order to track a planned Center of Mass trajectory. Its appeal lies in the ability to guarantee robust stability and performance despite rigid body model mismatch, actuator dynamics, delays, contact surface stiffness, and unobserved ground profiles. Furthermore, we introduce a task space decomposition approach which removes the coupling effects between contact force controller and the other non-contact controllers. Finally, we verify our control performance on a quadruped robot and compare its performance to a standard inverse dynamics approach on hardware.Comment: 8 Page