Manish Goyal

Manish Goyal, Miheer Dewaskar, Parasara Sridhar Duggirala

We propose a new technique for performing state space exploration of closed loop control systems with neural network feedback controllers. Our approach involves approximating the sensitivity of the trajectories of the closed loop dynamics. Using such an approximator and the system simulator, we present a guided state space exploration method that can generate trajectories visiting the neighborhood of a target state at a specified time. We present a theoretical framework which establishes that our method will produce a sequence of trajectories that will reach a suitable neighborhood of the target state. We provide thorough evaluation of our approach on various systems with neural network feedback controllers of different configurations. We outperform earlier state space exploration techniques and achieve significant improvement in both the quality (explainability) and performance (convergence rate). Finally, we adopt our algorithm for the falsification of a class of temporal logic specification, assess its performance against a state-of-the-art falsification tool, and show its potential in supplementing existing falsification algorithms.

Manish Goyal, Muqsit Azeem, Kumar Madhukar et al.

The problem of checking whether two programs are semantically equivalent or not has a diverse range of applications, and is consequently of substantial importance. There are several techniques that address this problem, chiefly by constructing a product program that makes it easier to derive useful invariants. A novel addition to these is a technique that uses alignment predicates to align traces of the two programs, in order to construct a program alignment automaton. Being guided by predicates is not just beneficial in dealing with syntactic dissimilarities, but also in staying relevant to the property. However, there are also drawbacks of a trace-based technique. Obtaining traces that cover all program behaviors is difficult, and any under-approximation may lead to an incomplete product program. Moreover, an indirect construction of this kind is unaware of the missing behaviors, and has no control over the aforesaid incompleteness. This paper, addressing these concerns, presents an algorithm to construct the program alignment automaton directly instead of relying on traces.

Abolfazl Karimi, Manish Goyal, Parasara Sridhar Duggirala

In this paper, we perform safety and performance analysis of an autonomous vehicle that implements reactive planner and controller for navigating a race lap. Unlike traditional planning algorithms that have access to a map of the environment, reactive planner generates the plan purely based on the current input from sensors. Our reactive planner selects a waypoint on the local Voronoi diagram and we use a pure-pursuit controller to navigate towards the waypoint. Our safety and performance analysis has two parts. The first part demonstrates that the reactive planner computes a plan that is locally consistent with the Voronoi plan computed with full map. The second part involves modeling of the evolution of vehicle navigating along the Voronoi diagram as a hybrid automata. For proving the safety and performance specification, we compute the reachable set of this hybrid automata and employ some enhancements that make this computation easier. We demonstrate that an autonomous vehicle implementing our reactive planner and controller is safe and successfully completes a lap for five different circuits. In addition, we have implemented our planner and controller in a simulation environment as well as a scaled down autonomous vehicle and demonstrate that our planner works well for a wide variety of circuits.