We present a computational design pipeline that allows evaluation of the robot and environment parameters in a robust manner, giving insight into interactions that can lead to mismatch between simulated behaviour and reality. Our pipeline evaluates robot designs across different design parameters in a large variety of stochastically-defined environments to robustly infer the qualitative effect of robot parameters on its performance. We then quantitatively ground this insight by selecting and building a small number of physical robots to help establish bounds on the trend in parameters observed in simulation. This combination of simulation and empirical evaluation helps narrow the sim-to-real gap without excessive expensive physical testing to augment the intuition of the human designer.

/pdf/tackling-simulation-inconsistencies-in-the-robot-design-2mq3hala.pdf

Tackling simulation inconsistencies in the robot design process by selective empirical evaluation

The problem of model counting, also known as #SAT, is to compute the number of models or satisfying assignments of a given Boolean formula $F$. Model counting is a fundamental problem in computer science with a wide range of applications. In recent years, there has been a growing interest in using hashing-based techniques for approximate model counting that provide $(\varepsilon, \delta)$-guarantees: i.e., the count returned is within a $(1+\varepsilon)$-factor of the exact count with confidence at least $1-\delta$. While hashing-based techniques attain reasonable scalability for large enough values of $\delta$, their scalability is severely impacted for smaller values of $\delta$, thereby preventing their adoption in application domains that require estimates with high confidence. The primary contribution of this paper is to address the Achilles heel of hashing-based techniques: we propose a novel approach based on rounding that allows us to achieve a significant reduction in runtime for smaller values of $\delta$. The resulting counter, called RoundMC, achieves a substantial runtime performance improvement over the current state-of-the-art counter, ApproxMC. In particular, our extensive evaluation over a benchmark suite consisting of 1890 instances shows that RoundMC solves 204 more instances than ApproxMC, and achieves a $4\times$ speedup over ApproxMC.

Rounding Meets Approximate Model Counting

Cyber-physical systems (CPSs) are complex systems comprised of computational processes, communication networks, and elements interacting with the physical world. The design of the CPSs involves many domain-specific tools and design flows created by engineers with diverse domain knowledge. As the scale of the systems increases, the heterogeneity nature of CPS design prolongs the CPS design process, making exhaustive design-space exploration infeasible. The symbiotic design methodology, in which the designers interact with optimization tools during the design process, is therefore promising to facilitate the design process by performing design exploration in a properly restricted design space. We present a symbiotic design methodology, which explores the design space iteratively and optimizes the system by exploiting the collaboration between designers and tools. The optimization tools perform the design space exploration, while the human designers use their expertise to guide the exploration by restricting the design space. Experimental results based on a robot car configuration problem and an unmanned aerial vehicle design problem show that the methodology can efficiently and effectively discover unconventional designs while optimizing the design objectives.

/pdf/symbiotic-cps-design-space-exploration-through-iterated-1rgtsf52.pdf

Symbiotic CPS Design-Space Exploration through Iterated Optimization

. In many synthesis problems, it can be essential to generate implementations which not only satisfy functional constraints but are also randomized to improve variety, robustness, or unpredictability. The recently-proposed framework of control improvisation (CI) provides techniques for the correct-by-construction synthesis of randomized systems subject to hard and soft constraints. However, prior work on CI has focused on qualitative speciﬁcations, whereas in robotic planning and other areas we often have quantitative quality metrics which can be traded against each other. For example, a designer of a patrolling security robot might want to know by how much the average patrol time needs to be increased in order to ensure that a particular aspect of the robot’s route is suﬃciently diverse and hence unpredictable. In this paper, we enable this type of application by generalizing the CI problem to support quantitative soft constraints which bound the expected value of a given cost function, and randomness constraints which enforce diversity of the generated traces with respect to a given label function. We estab-lish the basic theory of labelled quantitative CI problems, and develop eﬃcient algorithms for solving them when the speciﬁcations are encoded by ﬁnite automata. We also provide an approximate improvisation algorithm based on constraint solving for any speciﬁcations encodable as Boolean formulas. We demonstrate the utility of our problem formulation and algorithms with experiments applying them to generate diverse near-optimal plans for robotic planning problems.

Randomized Synthesis for Diversity and Cost Constraints with Control Improvisation

L'invention concerne un boitier de connexion pour fibre optique, adapte a une forme de connecteur (211, 311) standard, comportant deux parties (101, 102) aptes a etre assemblees l'une avec l'autre selon une direction (100) axiale par leurs extremites ouvertes, lequel boitier comporte : A connection box for optical fiber, adapted to form a connector (211, 311) standard, comprising two parts (101, 102) adapted to be assembled with each other in a direction (100) axial by their open ends, which housing comprises: a. at. un logement (204) cylindrique menage a l'interieur d'une des parties (101) ; a housing (204) cylindrical arranged inside one part (101); b. b. un insert (201) apte a coulisser a l'interieur dudit logement cylindrique et comprenant un jeu (401) radial par rapport audit logement ; an insert (201) slidable within said cylindrical housing and comprising a set (401) radial with respect to said housing; c. c. une interface (210) de connexion fixee audit insert (201) ; an interface (210) attached to said insert connection (201); d. d. des moyens (220) elastiques aptes a appliquer un effort axial dirige vers la deuxieme partie du boitier sur l'insert (201) ; means (220) elastic suitable for applying an axial force directed toward the second housing portion on the insert (201); e. e. une interface (302) de reception fixee a l'interieur de la seconde partie (102) du boitier ; an interface (302) for receiving fixed inside the second portion (102) of the housing; f. f. des moyens d'accouplement aptes a solidariser les deux parties du boitier et a appliquer la surface (216) de reference de l'interface de connexion contre la surface (316) de reception. coupling means adapted to secure the two housing parts and to apply the surface (216) of reference of the socket against the surface (316) for receiving.

Housing for optical connector with automatic alignment

We present a major new version of Scenic, a probabilistic programming language for writing formal models of the environments of cyber-physical systems. Scenic has been successfully used for the design and analysis of CPS in a variety of domains, but earlier versions are limited to environments which are essentially two-dimensional. In this paper, we extend Scenic with native support for 3D geometry, introducing new syntax which provides expressive ways to describe 3D configurations while preserving the simplicity and readability of the language. We replace Scenic's simplistic representation of objects as boxes with precise modeling of complex shapes, including a ray tracing-based visibility system that accounts for object occlusion. We also extend the language to support arbitrary temporal requirements expressed in LTL, and build an extensible Scenic parser generated from a formal grammar of the language. Finally, we illustrate the new application domains these features enable with case studies that would have been impossible to accurately model in Scenic 2.

Eric Vin

Papers

Symbiotic CPS Design-Space Exploration through Iterated Optimization

Randomized Synthesis for Diversity and Cost Constraints with Control Improvisation

Housing for optical connector with automatic alignment

3D Environment Modeling for Falsification and Beyond with Scenic 3.0

Tackling simulation inconsistencies in the robot design process by selective empirical evaluation