arXiv subjects

Anqi Li

Publications and source records attributed to Anqi Li.

117 records · Page 7Linked to original sources

arXiv2020

Optimization Fabrics

This paper presents a theory of optimization fabrics, second-order differential equations that encode nominal behaviors on a space and can be used to define the behavior of a smooth optimizer. Optimization fabrics can encode commonalities among optimization problems that reflect the structure of the space itself, enabling smooth optimization processes to intelligently navigate each problem even when optimizing simple naive potential functions. Importantly, optimization over a fabric is inherently asymptotically stable. The majority of this paper is dedicated to the development of a tool set for the design and use of a broad class of fabrics called geometric fabrics. Geometric fabrics encode behavior as general nonlinear geometries which are covariant second-order differential equations with a special homogeneity property that ensures their behavior is independent of the system's speed through the medium. A class of Finsler Lagrangian energies can be used to both define how these nonlinear geometries combine with one another and how they react when potential functions force them from their nominal paths. Furthermore, these geometric fabrics are closed under the standard operations of pullback and combination on a transform tree. For behavior representation, this class of geometric fabrics constitutes a broad class of spectral semi-sprays (specs), also known as Riemannian Motion Policies (RMPs) in the context of robotic motion generation, that captures both the intuitive separation between acceleration policy and priority metric critical for modular design and are inherently stable. Therefore, geometric fabrics are safe and easier to use by less experienced behavioral designers. Application of this theory to policy representation and generalization in learning are discussed as well.

cs.RO↗

arXiv2020

A Sequential Composition Framework for Coordinating Multi-Robot Behaviors

A number of coordinated behaviors have been proposed for achieving specific tasks for multi-robot systems. However, since most applications require more than one such behavior, one needs to be able to compose together sequences of behaviors while respecting local information flow constraints. Specifically, when the inter-agent communication depends on inter-robot distances, these constraints translate into particular configurations that must be reached in finite time in order for the system to be able to transition between the behaviors. To this end, we develop a distributed framework based on finite-time convergence control barrier functions that enables a team of robots to adjust its configuration in order to meet the communication requirements for the different tasks. In order to demonstrate the significance of the proposed framework, we implemented a full-scale scenario where a team of eight planar robots explore an urban environment in order to localize and rescue a subject.

cs.RO↗

arXiv2020

Inferring and Learning Multi-Robot Policies by Observing an Expert

We present a technique for learning how to solve a multi-robot mission that requires interaction with an external environment by observing an expert system executing the same mission. We define the expert system as a team of robots equipped with a library of controllers, each designed to solve a specific task, supervised by an expert policy that appropriately selects controllers based on the states of robots and environment. The objective is for an un-trained team of robots (i.e., imitator system) equipped with the same library of controllers, but agnostic to the expert policy, to execute the mission, with performances comparable to those of the expert system. From un-annotated observations of the expert system, a multi-hypothesis filtering technique is used to estimate individual controllers executed by the expert policy. Then, the history of estimated controllers and environmental states is used to train a neural network policy for the imitator system. Considering a perimeter protection scenario on a team of differential-drive robots, we show that the learned policy endows the imitator system with performances comparable to those of the expert system.

cs.RO↗

arXiv2020

Intermediated Implementation

We examine problems of ``intermediated implementation,'' in which a single principal can only regulate limited aspects of the consumption bundles traded between intermediaries and agents with hidden characteristics. An example is sales, in which retailers offer menus of consumption bundles to customers with hidden tastes, whereas a manufacturer with a potentially different goal from retailers' is limited to regulating sold consumption goods but not retail prices by legal barriers. We study how the principal can implement through intermediaries any social choice rule that is incentive compatible and individually rational for agents. We demonstrate the effectiveness of per-unit fee schedules and distribution regulations, which hinges on whether intermediaries have private or interdependent values. We give further applications to healthcare regulation and income redistribution.

econ.TH↗

arXiv2019

Optimal Incentive Contract with Endogenous Monitoring Technology

Recent technology advances have enabled firms to flexibly process and analyze sophisticated employee performance data at a reduced and yet significant cost. We develop a theory of optimal incentive contracting where the monitoring technology that governs the above procedure is part of the designer's strategic planning. In otherwise standard principal-agent models with moral hazard, we allow the principal to partition agents' performance data into any finite categories and to pay for the amount of information the output signal carries. Through analysis of the trade-off between giving incentives to agents and saving the monitoring cost, we obtain characterizations of optimal monitoring technologies such as information aggregation, strict MLRP, likelihood ratio-convex performance classification, group evaluation in response to rising monitoring costs, and assessing multiple task performances according to agents' endogenous tendencies to shirk. We examine the implications of these results for workforce management and firms' internal organizations.

econ.TH↗

arXiv2019

Multi-Objective Policy Generation for Multi-Robot Systems Using Riemannian Motion Policies

In many applications, multi-robot systems are required to achieve multiple objectives. For these multi-objective tasks, it is oftentimes hard to design a single control policy that fulfills all the objectives simultaneously. In this paper, we focus on multi-objective tasks that can be decomposed into a set of simple subtasks. Controllers for these subtasks are individually designed and then combined into a control policy for the entire team. One significant feature of our work is that the subtask controllers are designed along with their underlying manifolds. When a controller is combined with other controllers, their associated manifolds are also taken into account. This formulation yields a policy generation framework for multi-robot systems that can combine controllers for a variety of objectives while implicitly handling the interaction among robots and subtasks. To describe controllers on manifolds, we adopt Riemannian Motion Policies (RMPs), and propose a collection of RMPs for common multi-robot subtasks. Centralized and decentralized algorithms are designed to combine these RMPs into a final control policy. Theoretical analysis shows that the system under the control policy is stable. Moreover, we prove that many existing multi-robot controllers can be closely approximated by the framework. The proposed algorithms are validated through both simulated tasks and robotic implementations.

cs.RO↗

arXiv2019

Stable, Concurrent Controller Composition for Multi-Objective Robotic Tasks

Robotic systems often need to consider multiple tasks concurrently. This challenge calls for controller synthesis algorithms that fulfill multiple control specifications while maintaining the stability of the overall system. In this paper, we decompose multi-objective tasks into subtasks, where individual subtask controllers are designed independently and then combined to generate the overall control policy. In particular, we adopt Riemannian Motion Policies (RMPs), a recently proposed controller structure in robotics, and, RMPflow, its associated computational framework for combining RMP controllers. We re-establish and extend the stability results of RMPflow through a rigorous Control Lyapunov Function (CLF) treatment. We then show that RMPflow can stably combine individually designed subtask controllers that satisfy certain CLF constraints. This new insight leads to an efficient CLF-based computational framework to generate stable controllers that consider all the subtasks simultaneously. Compared with the original usage of RMPflow, our framework provides users the flexibility to incorporate design heuristics through nominal controllers for the subtasks. We validate the proposed computational framework through numerical simulation and robotic implementation.

eess.SY↗

arXiv2019

The Politics of Attention

We develop an equilibrium theory of attention and politics. In a spatial model of electoral competition where candidates have varying policy preferences, we examine what kinds of political behaviors capture voters' limited attention and how this concern affects the overall political outcomes. Following the seminal works of Downs (1957) and Sims (1998), we assume that voters are rationally inattentive and can process information about the policies at a cost proportional to entropy reduction. The main finding is an equilibrium phenomenon called attention- and media-driven extremism, namely as we increase the attention cost or garble the news technology, a truncated set of the equilibria captures voters' attention through enlarging the policy differentials between the varying types of the candidates. We supplement our analysis with historical accounts, and discuss its relevance in the new era featured with greater media choices and distractions, as well as the rise of partisan media and fake news.

econ.GN↗

arXiv2018

Planetary Nebulae in the Circumnuclear Region of M31: A Spectroscopic Sample

Planetary nebulae (PNe) are an important tool for studying the dynamics and chemical evolution of galaxies in the Local Universe, given their characteristic, bright emission line spectra. The Andromeda Galaxy (M31) provides a unique laboratory for studying PNe in the circumnuclear region, thanks to its proximity and almost uniformly low line-of-sight extinction that ensures observations with high resolution and sensitivity. Using the WIYN/Hydra multi-fiber spectrograph, we have obtained optical (4119--6882Å) spectra of 77 PN candidates selected from Hubble Space Telescope narrow-band imaging, which are located within the central $\sim$500pc region of M31. Among these candidates, 49 (64%) are spectroscopically observed for the first time. The spectra of 300 previously known PNe and H II regions, which primarily reside in the disk, are also taken for comparison. All 77 circumnuclear PN candidates exhibit prominent emission lines, including [O III] $λ$5007, [N II] $λ$6583, H$α$ and H$β$, strongly suggesting that they are genuine PNe. We measured the line fluxes, radial velocities and line widths for all objects, and found that the radial velocities of the circumnuclear PNe generally trace rotation of the inner bulge. We also estimated a dynamical mass of $\sim$6.4$\pm$0.5$\times$10$^{9}$$M_{\odot}$ enclosed within an effective galactocentric radius of 340pc, which is compatible with the previously estimated total stellar mass in this region.

astro-ph.GA↗