Vasumathi Raman

Reactive synthesis from signal temporal logic specifications

We present a counterexample-guided inductive synthesis approach to controller synthesis for cyber-physical systems subject to signal temporal logic (STL) specifications, operating in potentially adversarial nondeterministic environments. We encode STL specifications as mixed integer-linear constraints on the variables of a discrete-time model of the system and environment dynamics, and solve a series of optimization problems to yield a satisfying control sequence. We demonstrate how the scheme can be used in a receding horizon fashion to fulfill properties over unbounded horizons, and present experimental results for reactive controller synthesis for case studies in building climate control and autonomous driving.

Model predictive control with signal temporal logic specifications

We present a mathematical programming-based method for model predictive control of discrete-time cyber-physical systems subject to signal temporal logic (STL) specifications. We describe the use of STL to specify a wide range of properties of these systems, including safety, response and bounded liveness. For synthesis, we encode STL specifications as mixed integer-linear constraints on the system variables in the optimization problem at each step of a model predictive control framework. We present experimental results for controller synthesis for building energy and climate control.

Analyzing unsynthesizable specifications for high-level robot behavior using LTLMoP

Recent work in robotics has applied formal verification tools to automatically generate correct-by-construction controllers for autonomous robots. However, when it is not possible to create such a controller, these approaches do not provide the user with feedback on the source of failure, making the experience of debugging a specification somewhat ad hoc and unstructured, and a source of frustration for the user. This paper describes an extension to the LTLMoP toolkit for robot mission planning that encloses the control-generation process in a layer of automated reasoning to identify the cause of failure, and targets the users attention to flawed portions of the specification.

Provably correct continuous control for high-level robot behaviors with actions of arbitrary execution durations

Formal methods have recently been successfully applied to construct verifiable high-level robot control. Most approaches use a discrete abstraction of the underlying continuous domain, and make simplifying assumptions about the physical execution of actions given a discrete implementation. Relaxing these assumptions unearths a number of challenges in the continuous implementation of automatically-synthesized hybrid controllers. This paper describes a controller-synthesis framework that ensures correct continuous behaviors by explicitly modeling the activation and completion of continuous low-level controllers. The synthesized controllers exhibit desired properties like immediate reactiveness to sensor events and guaranteed safety of physical executions. The approach extends to any number of robot actions with arbitrary relative timings.

Explaining Impossible High-Level Robot Behaviors

A key challenge in robotics is the generation of controllers for autonomous, high-level robot behaviors comprising nontrivial sequences of actions, including reactive and repeated tasks. When constructing controllers to fulfill such tasks, it is often not known a priori whether the intended behavior is even feasible; plans are modified on the fly to deal with failures that occur during execution, often still without guaranteeing correct behavior. Recently, formal methods have emerged as a powerful tool to automatically generate autonomous robot controllers that guarantee desired behaviors expressed by a class of temporal logic specifications. However, when the specification cannot be fulfilled, these approaches do not provide the user with a source of failure, making the troubleshooting of specifications an unstructured and time-consuming process. This paper describes an algorithm to automatically analyze an unsynthesizable specification in order to identify causes of failure. It also introduces an interactive game to explore possible causes of unsynthesizability, in which the user attempts to fulfill the robot specification against an adversarial environment. The proposed algorithm and game are implemented as features within the LTLMoP toolkit for robot mission planning.

Provably correct reactive control from natural language

This paper presents an integrated system for generating, troubleshooting, and executing correct-by-construction controllers for autonomous robots using natural language input, allowing non-expert users to command robots to perform high-level tasks. This system unites the power of formal methods with the accessibility of natural language, providing controllers for implementable high-level task specifications, easy-to-understand feedback on those that cannot be achieved, and natural language explanation of the reason for the robot’s actions during execution. The natural language system uses domain-general components that can easily be adapted to cover the vocabulary of new applications. Generation of a linear temporal logic specification from the user’s natural language input uses a novel data structure that allows for subsequent mapping of logical propositions back to natural language, enabling natural language feedback about problems with the specification that are only identifiable in the logical form. We demonstrate the robustness of the natural language understanding system through a user study where participants interacted with a simulated robot in a search and rescue scenario. Automated analysis and user feedback on unimplementable specifications is demonstrated using an example involving a robot assistant in a hospital.

Slugs: Extensible GR(1) Synthesis

Applying reactive synthesis in practice often requires modifications of the synthesis algorithm in order to obtain useful implementations. We present slugs, a generalized reactivity(1) synthesis tool that has a powerful plugin architecture for modifying any aspect of the synthesis process to fit the application. Slugs comes pre-equipped with a variety of plugins that improve the quality of the synthesized solutions along criteria such as quick response, cost-optimality, and error-resilience. We demonstrate the utility and scalability of the tool on an example from robotics.

Automated feedback for unachievable high-level robot behaviors

One of the main challenges in robotics is the generation of controllers for autonomous, high-level robot behaviors comprising a non-trivial sequence of actions. Recently, formal methods have emerged as a powerful tool for automatically generating autonomous robot controllers that guarantee desired behaviors expressed by a class of temporal logic specifications. However, when there is no controller that fulfills the specification, these approaches do not provide the user with a source of failure, making the troubleshooting of specifications an unstructured and time-consuming process. In this paper, we describe a procedure for analyzing an unsynthesizable specification to identify causes of failure. We also provide an interactive game for exploring possible causes of failure, in which the user attempts to fulfill the robot specification against an adversarial environment. Our approach is implemented within the LTLMoP toolkit for robot mission planning.

Robust Model Predictive Control for Signal Temporal Logic Synthesis

Abstract Most automated systems operate in uncertain or adversarial conditions, and have to be capable of reliably reacting to changes in the environment. The focus of this paper is on automatically synthesizing reactive controllers for cyber-physical systems subject to signal temporal logic (STL) specifications. We build on recent work that encodes STL specifications as mixed integer linear constraints on the variables of a discrete-time model of the system and environment dynamics. To obtain a reactive controller, we present solutions to the worst-case model predictive control (MPC) problem using a suite of mixed integer linear programming techniques. We demonstrate the comparative effectiveness of several existing worst-case MPC techniques, when applied to the problem of control subject to temporal logic specifications; our empirical results emphasize the need to develop specialized solutions for this domain.

Sorry Dave, I'm Afraid I Can't Do That: Explaining Unachievable Robot Tasks using Natural Language

Abstract : This paper addresses the challenge of enabling non-expert users to command robots to perform complex highleveltasks using natural language. It describes an integrated system that combines the power of formalmethods with the accessibility of natural language, providing correct-by-construction controllers for high-levelspecifications that can be implemented, and easy-to-understand feedback to the user on those that cannot be achieved.This is among the first works to close this feedback loop, enabling users to interact with the robot in order to identifya succinct cause of failure and obtain the desired controller. The supported language and logical capabilities areillustrated using examples involving a robot assistant in a hospital.