Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning

We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors. Given the uncertainty in the intent of the other agent, the objective is to collect further evidence to help discriminate potential threats. The main technical challenges are the partial observability of the agent intent, the adversary modeling, and the corresponding uncertainty modeling. Note that an adversary agent may act to mislead the autonomous agent by using a deceptive strategy that is learned from past experiences. We propose an approach that combines belief space planning, generative adversary modeling, and maximum entropy reinforcement learning to obtain a stochastic belief space policy. By accounting for various adversarial behaviors in the simulation framework and minimizing the predictability of the autonomous agent's action, the resulting policy is more robust to unmodeled adversarial strategies. This improved robustness is empirically shown against an adversary that adapts to and exploits the autonomous agent's policy when compared with a standard Chance-Constraint Partially Observable Markov Decision Process robust approach

Observable Properties Of Double-Barred Galaxies In N-Body Simulations

Although at least one quarter of early-type barred galaxies host secondary stellar bars embedded in their large-scale primary counterparts, the dynamics of such double-barred galaxies are still not well understood. Recently we reported success at simulating such systems in a repeatable way in collisionless systems. In order to further our understanding of double-barred galaxies, here we characterize the density and kinematics of the N-body simulations of these galaxies. This will facilitate comparison with observations and lead to a better understanding of the observed double-barred galaxies. We find the shape and size of our simulated secondary bars are quite reasonable compared to the observed ones. We demonstrate that an authentic decoupled secondary bar may produce only a weak twist of the kinematic minor axis in the stellar velocity field, due to the relatively large random motion of stars in the central region. We also find that the edge-on nuclear bars are probably not related to boxy peanut-shaped bulges which are most likely to be edge-on primary large-scale bars. Another kinematic feature often present in our double-barred models is a ring-like feature in the fourth-order Gauss-Hermite moment h(4) maps. Finally, we demonstrate that the non-rigid rotation of the secondary bar causes its pattern speed to not be derived with great accuracy using the Tremaine-Weinberg method. We also compare with observations of NGC 2950, a prototypical double-barred early-type galaxy, which suggest that the nuclear bar may be rotating in the opposite sense as the primary.H.J.S. fellowshipUniversity of WashingtonNSF ITR PHY-0205413McDonald Observator

Identification of the major cause of endemically poor mobilities in SiC/SiO2 structures

Materials with good carrier mobilities are desired for device applications, but in real devices the mobilities are usually limited by the presence of interfaces and contacts. Mobility degradation at semiconductor-dielectric interfaces is generally attributed to defects at the interface or inside the dielectric, as is the case in Si/SiO2 structures, where processing does not introduce detrimental defects in the semiconductor. In the case of SiC/SiO2 structures, a decade of research focused on reducing or passivating interface and oxide defects, but the low mobilities have persisted. By invoking theoretical results and available experimental evidence, we show that thermal oxidation generates carbon di-interstitial defects inside the semiconductor substrate and that they are a major cause of the poor mobility in SiC/SiO2 structures

Transferable Pedestrian Motion Prediction Models at Intersections

One desirable capability of autonomous cars is to accurately predict the pedestrian motion near intersections for safe and efficient trajectory planning. We are interested in developing transfer learning algorithms that can be trained on the pedestrian trajectories collected at one intersection and yet still provide accurate predictions of the trajectories at another, previously unseen intersection. We first discussed the feature selection for transferable pedestrian motion models in general. Following this discussion, we developed one transferable pedestrian motion prediction algorithm based on Inverse Reinforcement Learning (IRL) that infers pedestrian intentions and predicts future trajectories based on observed trajectory. We evaluated our algorithm on a dataset collected at two intersections, trained at one intersection and tested at the other intersection. We used the accuracy of augmented semi-nonnegative sparse coding (ASNSC), trained and tested at the same intersection as a baseline. The result shows that the proposed algorithm improves the baseline accuracy by 40% in the non-transfer task, and 16% in the transfer task