Abstract This paper presents StarV, a new tool for verifying deep neural networks (DNNs) and learning-enabled Cyber-Physical Systems (Le-CPS) using the well-known star reachability. Distinguished from existing star-based verification tools such as NNV and NNENUM and others, StarV not only offers qualitative verification techniques using Star and ImageStar reachability analysis but is also the first tool to propose using ProbStar reachability for quantitative verification of DNNs with piecewise linear activation functions and Le-CPS. Notably, it introduces a novel ProbStar Temporal Logic formalism and associated algorithms, enabling the quantitative verification of DNNs and Le-CPS’s temporal behaviors. Additionally, StarV presents a novel SparseImageStar set representation and associated reachability algorithm that allows users to verify deep convolutional neural networks and semantic segmentation networks with more memory efficiency. StarV is evaluated in comparison with state-of-the-art in many challenging benchmarks. The experiments show that StarV outperforms existing tools in many aspects, such as timing performance, scalability, and memory consumption.

This paper introduces a novel quantitative verification framework for analyzing the temporal behaviors of learning-enabled systems (LES). Our approach employs ProbStar Temporal Logic (ProbStarTL) to specify LES temporal behaviors alongside advanced reachability and verification algorithms. Unlike existing qualitative methods focusing primarily on reach-avoid properties, our framework enables quantitative analysis of temporal properties. ProbStarTL, distinct from Signal Temporal Logic, operates on sequences of timed probabilistic star reachable sets, known as ProbStar signals. It features a clear syntax and dual qualitative and quantitative semantics. Our framework includes depth-first search algorithms for generating ProbStar traces and novel verification algorithms that transform ProbStarTL specifications into a computable disjunctive normal form for analysis. Our verification algorithms allow for both exact and approximate analyses. The exact scheme guarantees sound and complete results with precise satisfaction probabilities, while the approximate scheme offers sound results with maximum and minimum satisfaction probabilities at a reduced computational cost. The new verification framework is implemented using StarV, and its effectiveness is demonstrated through case studies on a learning-based adaptive cruise control system and an advanced emergency braking system.

This paper presents a novel method for modeling the shape of a continuum robot as a Neural Configuration Signed Distance Function (N-CSDF). By learning separate distance fields for each link and combining them through the kinematics chain, the learned N-CSDF provides an accurate and computationally efficient representation of the robot’s shape. The key advantage of a distance function representation of a continuum robot is that it enables efficient collision checking for motion planning in dynamic and cluttered environments, even with point-cloud observations. We integrate the N-CSDF into a Model Predictive Path Integral (MPPI) controller to generate safe trajectories for multi-segment continuum robots. The proposed approach is validated for continuum robots with various links in several simulated environments with static and dynamic obstacles.

This paper addresses the challenge of planning a sequence of tasks to be performed by multiple robots while minimizing the overall completion time subject to timing and precedence constraints. Our approach uses the Timed Partial Orders (TPO) model to specify these constraints. We translate this problem into a Traveling Salesman Problem (TSP) variant with timing and precedent constraints, and we solve it as a Mixed Integer Linear Programming (MILP) problem. Our contributions include a general planning framework for TPO specifications, a MILP formulation accommodating time windows and precedent constraints, its extension to multi-robot scenarios, and a method to quantify plan robustness. We demonstrate our framework on several case studies, including an aircraft turnaround task involving three Jackal robots, highlighting the approach’s potential applicability to important real-world problems. Our benchmark results show that our MILP method outperforms state-of-the-art open-source TSP solvers OR-Tools.

This paper introduces CBFkit, a Python/ROS toolbox for safe robotics planning and control under uncertainty. The toolbox provides a general framework for designing control barrier functions for mobility systems within both deterministic and stochastic environments. It can be connected to the ROS open-source robotics middleware, allowing for the setup of multi-robot applications, encoding of environments and maps, and integrations with predictive motion planning algorithms. Additionally, it offers multiple CBF variations and algorithms for robot control. The CBFKit is demonstrated on the Toyota Human Support Robot (HSR) in both simulation and in physical experiments.

Most deep neural network (DNN) verification research focuses on qualitative verification, which answers whether or not a DNN violates a safety/robustness property. This paper proposes an approach to convert qualitative verification into quantitative verification for neural networks. The resulting quantitative verification method not only can answer YES or NO questions but also can compute the probability of a property being violated. To do that, we introduce the concept of a probabilistic star (or shortly ProbStar), a new variant of the well-known star set, in which the predicate variables belong to a Gaussian distribution and propose an approach to compute the probability of a probabilistic star in high-dimensional space. Unlike existing works dealing with constrained input sets, our work considers the input set as a truncated multivariate normal (Gaussian) distribution, i.e., besides the constraints on the input variables, the input set has a probability of the constraints being satisfied. The input distribution is represented as a probabilistic star set and is propagated through a network to construct the output reachable set containing multiple ProbStars, which are used to verify the safety or robustness properties of the network. In case of a property is violated, the violation probability can be computed precisely by an exact verification algorithm or approximately by an overapproximate verification algorithm. The proposed approach is implemented in a tool named StarV and is evaluated using the well-known ACASXu networks and a rocket landing benchmark.

We investigate a novel method for speaker verification with nonaudible murmur (NAM) segments. NAM is recorded using a special microphone placed on the neck and is hard for other people to hear. We have already reported a method based on a support vector machine (SVM) using NAM segments to use a keyword phrase effectively. To further exploit keyword-specific features, we introduce a global alignment (GA) kernel and penalized logistic regression machine (PLRM). In the experiments using NAM from 55 speakers, our method achieved an error reduction rate of roughly 60% compared with the SVM-based method using a polynomial kernel.

We investigated a speaker verification method that uses non-audible murmur (NAM) segments using newly collected data and obtained several findings that will be useful when speaker verification systems are made in practice. NAM is recorded using a special microphone placed on the surface of the body, so it includes almost no external noise and is hard for other people to hear. By utilizing these properties, we have already reported a text-dependent method using NAM segments that can use a keyword phrase safely. This paper extends the examination with newly collected data consisting of NAM uttered by 18 male and 9 female imposter speakers and by 18 male and 10 female customer speakers. Experiments with various numbers of training utterances and sessions show that it is effective to use data recorded in multiple sessions. We also investigated the minimum number of training utterances needed in our method.