Role Playing Learning for Socially Concomitant Mobile Robot Navigation

In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks (NN) are constructed to parameterize a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, we call this process Role Playing Learning, which is formulated under a reinforcement learning (RL) framework. The NN policy is optimized end-to-end using Trust Region Policy Optimization (TRPO), with consideration of the imperfectness of robot's sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of our method

Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach

10.1109/TNN.2008.2003290IEEE Transactions on Neural Networks19111873-1886ITNN

Effective Field Theories and Finite-temperature Properties of Zero-dimensional Superradiant Quantum Phase Transitions

The existence of zero-dimensional superradiant quantum phase transitions seems inconsistent with conventional statistical physics, which has not been explained so far. Here we demonstrate the corresponding effective field theories and finite-temperature properties of light-matter interacting systems, and show how this zero-dimensional quantum phase transition occurs. We first focus on the Rabi model, which is a minimum model that hosts a superradiant quantum phase transition. With the path integral method, we derive the imaginary-time action of the photon degrees of freedom. We also define a dynamical exponent as the rescaling between the temperature and the photon frequency, and perform dimensional analysis to the effective action. Our results show that the effective theory becomes a free scalar field or $\phi^4$-theory for a proper dynamical exponent, where a true second-order quantum phase transition emerges. These results are also verified by numerical simulations of imaginary-time correlation functions of the order parameter. Furthermore, we also generalize this method to the Dicke model. Our results make the zero-dimensional superradiant quantum phase transition compatible with conventional statistical physics, and pave the way to understand it in the perspective of effective field theories.Comment: 6+4 pages, 2 figure

Winning over Grassroots Consumers: An Empowerment Perspective of Yu’E Bao

Recent years have witnessed the great potential of technological innovations in helping developing economies tackle financial exclusion, a key obstacle in reducing poverty and accelerating economic growth. Yet, the way that technology innovations should be designed and utilized to deliver inclusive finance has remained obscure. In this study, we undertook an in-depth qualitative case study of Yu’E Bao, an online market fund, which has revolutionized the online finance sector in China. The incredible feat of Yu’E Bao has made financial institutions in China start focusing on potential cumulative wealth of the large population of financially underserved “grassroots consumers”. Drawing insights from empowerment theory, we explicate the mechanisms through which an IT-enabled innovation can successfully engage and leverage the financially underprivileged population to progress towards financial inclusion. Our findings underline important theoretical, economic and societal contributions, viz. exploitation of empowerment mechanisms, acknowledgment of grassroots’ economic potential, and IT-enabled financial inclusion