arXiv subjects

Dylan A. Shell

Publications and source records attributed to Dylan A. Shell.

25 records · Page 2Linked to original sources

arXiv2019

Decoupling stochastic optimal control problems for efficient solution: insights from experiments across a wide range of noise regimes

We consider the problem of robotic planning under uncertainty in this paper. This problem may be posed as a stochastic optimal control problem, a solution to which is fundamentally intractable owing to the infamous "curse of dimensionality". Hence, we consider the extension of a "decoupling principle" that was recently proposed by some of the authors, wherein a nominal open-loop problem is solved followed by a linear feedback design around the open-loop, and which was shown to be near-optimal to second order in terms of a "small noise" parameter, to a much wider range of noise levels. Our empirical evidence suggests that this allows for tractable planning over a wide range of uncertainty conditions without unduly sacrificing performance.

eess.SY↗

arXiv2019

Reality as a simulation of reality: robot illusions, fundamental limits, and a physical demonstration

We consider problems in which robots conspire to present a view of the world that differs from reality. The inquiry is motivated by the problem of validating robot behavior physically despite there being a discrepancy between the robots we have at hand and those we wish to study, or the environment for testing that is available versus that which is desired, or other potential mismatches in this vein. After formulating the concept of a convincing illusion, essentially a notion of system simulation that takes place in the real world, we examine the implications of this type of simulability in terms of infrastructure requirements. Time is one important resource: some robots may be able to simulate some others but, perhaps, only at a rate that is slower than real-time. This difference gives a way of relating the simulating and the simulated systems in a form that is relative. We establish some theorems, including one with the flavor of an impossibility result, and providing several examples throughout. Finally, we present data from a simple multi-robot experiment based on this theory, with a robot navigating amid an unbounded field of obstacles.

cs.RO↗

arXiv2019

Finding plans subject to stipulations on what information they divulge

Motivated by applications where privacy is important, we consider planning problems for robots acting in the presence of an observer. We first formulate and then solve planning problems subject to stipulations on the information divulged during plan execution --- the appropriate solution concept being both a plan and an information disclosure policy. We pose this class of problem under a worst-case model within the framework of procrustean graphs, formulating the disclosure policy as a particular type of map on edge labels. We devise algorithms that, given a planning problem supplemented with an information stipulation, can find a plan, associated disclosure policy, or both if some exists. Both the plan and associated disclosure policy may depend subtlety on additional information available to the observer, such as whether the observer knows the robot's plan (e.g., leaked via a side-channel). Our implementation finds a plan and a suitable disclosure policy, jointly, when any such pair exists, albeit for small problem instances.

cs.RO↗

arXiv2018

What does my knowing your plans tell me?

For robots acting in the presence of observers, we examine the information that is divulged if the observer is party to the robot's plan. Privacy constraints are specified as the stipulations on what can be inferred during plan execution. We imagine a case in which the robot's plan is divulged beforehand, so that the observer can use this {\em a priori} information along with the disclosed executions. The divulged plan, which can be represented by a procrustean graph, is shown to undermine privacy precisely to the extent that it can eliminate action-observation sequences that will never appear in the plan. Future work will consider how the divulged plan might be sought as the output of a planning procedure.

cs.RO↗

arXiv2018

Toward a language-theoretic foundation for planning and filtering

We address problems underlying the algorithmic question of automating the co-design of robot hardware in tandem with its apposite software. Specifically, we consider the impact that degradations of a robot's sensor and actuation suites may have on the ability of that robot to complete its tasks. We introduce a new formal structure that generalizes and consolidates a variety of well-known structures including many forms of plans, planning problems, and filters, into a single data structure called a procrustean graph, and give these graph structures semantics in terms of ideas based in formal language theory. We describe a collection of operations on procrustean graphs (both semantics-preserving and semantics-mutating), and show how a family of questions about the destructiveness of a change to the robot hardware can be answered by applying these operations. We also highlight the connections between this new approach and existing threads of research, including combinatorial filtering, Erdmann's strategy complexes, and hybrid automata.

cs.AI↗

arXiv2018

Robot Design: Formalisms, Representations, and the Role of the Designer

The objective of this paper is to distill the following essential idea from the RSS 2016 Workshop on Minimality and Design Automation and the RSS 2017 Workshop on Minimality and Trade-offs in Automated Robot Design: The information abstractions popular within robotics, designed as they were to address insulated sub-problems, are currently inadequate for design automation. This paper's first aim is to draw together multiple threads---specifically those of formalization, minimality, automation, and integration---and to argue that robot design questions involve some of the most interesting and fundamental challenges for the discipline. While most efforts in automating robot design have focused on optimization of hardware, robot design is also inextricably linked to the design of the internal state of the robot, how that internal state interacts with sensors and actuators, and how task specifications are designed within this context. Focusing attention on those considerations is worthwhile for the study of robot design because they are currently in a critical intellectual sweet spot, being out of reach technically, but only just. The second ingredient of this paper forms a roadmap. It emphasizes two aspects: (1) the role of models in robot design, a reprise of the old chestnut about representation in robotics (namely, that "the world is its own best model"); (2) a consideration of the human-element within the envisioned scheme.

cs.RO↗

arXiv2018

Complete characterization of a class of privacy-preserving tracking problems

We examine the problem of target tracking whilst simultaneously preserving the target's privacy as epitomized by the robotic panda tracking scenario, which O'Kane introduced at the 2008 Workshop on the Algorithmic Foundations of Robotics in order to elegantly illustrate the utility of ignorance. The present paper reconsiders his formulation and the tracking strategy he proposed, along with its completeness. We explore how the capabilities of the robot and panda affect the feasibility of tracking with a privacy stipulation, uncovering intrinsic limits, no matter the strategy employed. This paper begins with a one-dimensional setting and, putting the trivially infeasible problems aside, analyzes the strategy space as a function of problem parameters. We show that it is not possible to actively track the target as well as protect its privacy for every nontrivial pair of tracking and privacy stipulations. Secondly, feasibility can be sensitive, in several cases, to the information available to the robot initially. Quite naturally in the one-dimensional model, one may quantify sensing power by the number of perceptual (or output) classes available to the robot. The robot's power to achieve privacy-preserving tracking is bounded, converging asymptotically with increasing sensing power. We analyze the entire space of possible tracking problems, characterizing every instance as either achievable, constructively by giving a policy where one exists (some of which depend on the initial information), or proving the instance impossible. Finally, to relate some of the impossibility results in one dimension to their higher-dimensional counterparts, including the planar panda tracking problem studied by O'Kane, we establish a connection between tracking dimensionality and the sensing power of a one-dimensional robot.

cs.RO↗