arXiv subjects

Jyotirmoy V. Deshmukh

Publications and source records attributed to Jyotirmoy V. Deshmukh.

At least 37 records · Page 2Linked to original sources

arXiv2026

When Environments Shift: Safe Planning with Generative Priors and Robust Conformal Prediction

Autonomous systems operate in environments that may change over time. An example is the control of a self-driving vehicle among pedestrians and human-controlled vehicles whose behavior may change based on factors such as traffic density, road visibility, and social norms. Therefore, the environment encountered during deployment rarely mirrors the environment and data encountered during training -- a phenomenon known as distribution shift -- which can undermine the safety of autonomous systems. Conformal prediction (CP) has recently been used along with data from the training environment to provide prediction regions that capture the behavior of the environment with a desired probability. When embedded within a model predictive controller (MPC), one can provide probabilistic safety guarantees, but only when the deployment and training environments coincide. Once a distribution shift occurs, these guarantees collapse. We propose a planning framework that is robust under distribution shifts by: (i) assuming that the underlying data distribution of the environment is parameterized by a nuisance parameter, i.e., an observable, interpretable quantity such as traffic density, (ii) training a conditional diffusion model that captures distribution shifts as a function of the nuisance parameter, (iii) observing the nuisance parameter online and generating cheap, synthetic data from the diffusion model for the observed nuisance parameter, and (iv) designing an MPC that embeds CP regions constructed from such synthetic data. Importantly, we account for discrepancies between the underlying data distribution and the diffusion model by using robust CP. Thus, the plans computed using robust CP enjoy probabilistic safety guarantees, in contrast with plans obtained from a single, static set of training data. We empirically demonstrate safety under diverse distribution shifts in the ORCA simulator.

eess.SY↗

arXiv2025

Coordinating Spinal and Limb Dynamics for Enhanced Sprawling Robot Mobility

Sprawling locomotion in vertebrates, particularly salamanders, demonstrates how body undulation and spinal mobility enhance stability, maneuverability, and adaptability across complex terrains. While prior work has separately explored biologically inspired gait design or deep reinforcement learning (DRL), these approaches face inherent limitations: open-loop gait designs often lack adaptability to unforeseen terrain variations, whereas end-to-end DRL methods are data-hungry and prone to unstable behaviors when transferring from simulation to real robots. We propose a hybrid control framework that integrates Hildebrand's biologically grounded gait design with DRL, enabling a salamander-inspired quadruped robot to exploit active spinal joints for robust crawling motion. Our evaluation across multiple robot configurations in target-directed navigation tasks reveals that this hybrid approach systematically improves robustness under environmental uncertainties such as surface irregularities. By bridging structured gait design with learning-based methodology, our work highlights the promise of interdisciplinary control strategies for developing efficient, resilient, and biologically informed spinal actuation in robotic systems.

cs.RO↗

arXiv2025

Multi-Agent Path Finding via Offline RL and LLM Collaboration

Multi-Agent Path Finding (MAPF) poses a significant and challenging problem critical for applications in robotics and logistics, particularly due to its combinatorial complexity and the partial observability inherent in realistic environments. Decentralized reinforcement learning methods commonly encounter two substantial difficulties: first, they often yield self-centered behaviors among agents, resulting in frequent collisions, and second, their reliance on complex communication modules leads to prolonged training times, sometimes spanning weeks. To address these challenges, we propose an efficient decentralized planning framework based on the Decision Transformer (DT), uniquely leveraging offline reinforcement learning to substantially reduce training durations from weeks to mere hours. Crucially, our approach effectively handles long-horizon credit assignment and significantly improves performance in scenarios with sparse and delayed rewards. Furthermore, to overcome adaptability limitations inherent in standard RL methods under dynamic environmental changes, we integrate a large language model (GPT-4o) to dynamically guide agent policies. Extensive experiments in both static and dynamically changing environments demonstrate that our DT-based approach, augmented briefly by GPT-4o, significantly enhances adaptability and performance.

cs.MA↗

arXiv2025

Conformal Predictive Monitoring for Multi-Modal Scenarios

We consider the problem of quantitative predictive monitoring (QPM) of stochastic systems, i.e., predicting at runtime the degree of satisfaction of a desired temporal logic property from the current state of the system. Since computational efficiency is key to enable timely intervention against predicted violations, several state-of-the-art QPM approaches rely on fast machine-learning surrogates to provide prediction intervals for the satisfaction values, using conformal inference to offer statistical guarantees. However, these QPM methods suffer when the monitored agent exhibits multi-modal dynamics, whereby certain modes may yield high satisfaction values while others critically violate the property. Existing QPM methods are mode-agnostic and so would yield overly conservative and uninformative intervals that lack meaningful mode-specific satisfaction information. To address this problem, we present GenQPM, a method that leverages deep generative models, specifically score-based diffusion models, to reliably approximate the probabilistic and multi-modal system dynamics without requiring explicit model access. GenQPM employs a mode classifier to partition the predicted trajectories by dynamical mode. For each mode, we then apply conformal inference to produce statistically valid, mode-specific prediction intervals. We demonstrate the effectiveness of GenQPM on a benchmark of agent navigation and autonomous driving tasks, resulting in prediction intervals that are significantly more informative (less conservative) than mode-agnostic baselines.

cs.AI↗

arXiv2025

Formal Verification and Control with Conformal Prediction

We present recent advances in formal verification and control for autonomous systems with practical safety guarantees enabled by conformal prediction (CP), a statistical tool for uncertainty quantification. This survey is particularly motivated by learning-enabled autonomous systems (LEASs), where the complexity of learning-enabled components (LECs) poses a major bottleneck for applying traditional model-based verification and control techniques. To address this challenge, we advocate for CP as a lightweight alternative and demonstrate its use in formal verification, systems and control, and robotics. CP is appealing due to its simplicity (easy to understand, implement, and adapt), generality (requires no assumptions on learned models and underlying data distributions), and efficiency (real-time capable and accurate). This survey provides an accessible introduction to CP for non-experts interested in applying CP to autonomy problems. We particularly show how CP can be used for formal verification of LECs and the design of safe control as well as offline and online verification algorithms for LEASs. We present these techniques within a unifying framework that addresses the complexity of LEASs. Our exposition spans simple specifications, such as robot navigation tasks, to complex mission requirements expressed in temporal logic. Throughout the survey, we contrast CP with other statistical techniques, including scenario optimization and PAC-Bayes theory, highlighting advantages and limitations for verification and control. Finally, we outline open problems and promising directions for future research.

eess.SY↗

arXiv2025

STL-GO: Spatio-Temporal Logic with Graph Operators for Distributed Systems with Multiple Network Topologies

Multi-agent systems (MASs) consisting of a number of autonomous agents that communicate, coordinate, and jointly sense the environment to achieve complex missions can be found in a variety of applications such as robotics, smart cities, and internet-of-things applications. Modeling and monitoring MAS requirements to guarantee overall mission objectives, safety, and reliability is an important problem. Such requirements implicitly require reasoning about diverse sensing and communication modalities between agents, analysis of the dependencies between agent tasks, and the spatial or virtual distance between agents. To capture such rich MAS requirements, we model agent interactions via multiple directed graphs, and introduce a new logic -- Spatio-Temporal Logic with Graph Operators (STL-GO). The key innovation in STL-GO are graph operators that enable us to reason about the number of agents along either the incoming or outgoing edges of the underlying interaction graph that satisfy a given property of interest; for example, the requirement that an agent should sense at least two neighboring agents whose task graphs indicate the ability to collaborate. We then propose novel distributed monitoring conditions for individual agents that use only local information to determine whether or not an STL-GO specification is satisfied. We compare the expressivity of STL-GO against existing spatio-temporal logic formalisms, and demonstrate the utility of STL-GO and our distributed monitors in a bike-sharing and a multi-drone case study.

cs.LO↗

arXiv2025

Distributionally Robust Predictive Runtime Verification under Spatio-Temporal Logic Specifications

Cyber-physical systems (CPS) designed in simulators, often consisting of multiple interacting agents (e.g. in multi-agent formations), behave differently in the real-world. We want to verify these systems during runtime when they are deployed. We thus propose robust predictive runtime verification (RPRV) algorithms for: (1) general stochastic CPS under signal temporal logic (STL) tasks, and (2) stochastic multi-agent systems (MAS) under spatio-temporal logic tasks. The RPRV problem presents the following challenges: (1) there may not be sufficient data on the behavior of the deployed CPS, (2) predictive models based on design phase system trajectories may encounter distribution shift during real-world deployment, and (3) the algorithms need to scale to the complexity of MAS and be applicable to spatio-temporal logic tasks. To address the challenges, we assume knowledge of an upper bound on the statistical distance between the trajectory distributions of the system at deployment and design time. We are motivated by our prior work [1, 2] where we proposed an accurate and an interpretable RPRV algorithm for general CPS, which we here extend to the MAS setting and spatio-temporal logic tasks. Specifically, we use a learned predictive model to estimate the system behavior at runtime and robust conformal prediction to obtain probabilistic guarantees by accounting for distribution shifts. Building on [1], we perform robust conformal prediction over the robust semantics of spatio-temporal reach and escape logic (STREL) to obtain centralized RPRV algorithms for MAS. We empirically validate our results in a drone swarm simulator, where we show the scalability of our RPRV algorithms to MAS and analyze the impact of different trajectory predictors on the verification result. To the best of our knowledge, these are the first statistically valid algorithms for MAS under distribution shift.

eess.SY↗

arXiv2025

Monitoring Spatially Distributed Cyber-Physical Systems with Alternating Finite Automata

Modern cyber-physical systems (CPS) can consist of various networked components and agents interacting and communicating with each other. In the context of spatially distributed CPS, these connections can be dynamically dependent on the spatial configuration of the various components and agents. In these settings, robust monitoring of the distributed components is vital to ensuring complex behaviors are achieved, and safety properties are maintained. To this end, we look at defining the automaton semantics for the Spatio-Temporal Reach and Escape Logic (STREL), a formal logic designed to express and monitor spatio-temporal requirements over mobile, spatially distributed CPS. Specifically, STREL reasons about spatio-temporal behavior over dynamic weighted graphs. While STREL is endowed with well defined qualitative and quantitative semantics, in this paper, we propose a novel construction of (weighted) alternating finite automata from STREL specifications that efficiently encodes these semantics. Moreover, we demonstrate how this automaton semantics can be used to perform both, offline and online monitoring for STREL specifications using a simulated drone swarm environment.

cs.LO↗

arXiv2024

Multi-agent Path Finding for Timed Tasks using Evolutionary Games

Autonomous multi-agent systems such as hospital robots and package delivery drones often operate in highly uncertain environments and are expected to achieve complex temporal task objectives while ensuring safety. While learning-based methods such as reinforcement learning are popular methods to train single and multi-agent autonomous systems under user-specified and state-based reward functions, applying these methods to satisfy trajectory-level task objectives is a challenging problem. Our first contribution is the use of weighted automata to specify trajectory-level objectives, such that, maximal paths induced in the weighted automaton correspond to desired trajectory-level behaviors. We show how weighted automata-based specifications go beyond timeliness properties focused on deadlines to performance properties such as expeditiousness. Our second contribution is the use of evolutionary game theory (EGT) principles to train homogeneous multi-agent teams targeting homogeneous task objectives. We show how shared experiences of agents and EGT-based policy updates allow us to outperform state-of-the-art reinforcement learning (RL) methods in minimizing path length by nearly 30\% in large spaces. We also show that our algorithm is computationally faster than deep RL methods by at least an order of magnitude. Additionally our results indicate that it scales better with an increase in the number of agents as compared to other methods.

cs.MA↗

arXiv2024

Survival of the Fittest: Evolutionary Adaptation of Policies for Environmental Shifts

Reinforcement learning (RL) has been successfully applied to solve the problem of finding obstacle-free paths for autonomous agents operating in stochastic and uncertain environments. However, when the underlying stochastic dynamics of the environment experiences drastic distribution shifts, the optimal policy obtained in the trained environment may be sub-optimal or may entirely fail in helping find goal-reaching paths for the agent. Approaches like domain randomization and robust RL can provide robust policies, but typically assume minor (bounded) distribution shifts. For substantial distribution shifts, retraining (either with a warm-start policy or from scratch) is an alternative approach. In this paper, we develop a novel approach called {\em Evolutionary Robust Policy Optimization} (ERPO), an adaptive re-training algorithm inspired by evolutionary game theory (EGT). ERPO learns an optimal policy for the shifted environment iteratively using a temperature parameter that controls the trade off between exploration and adherence to the old optimal policy. The policy update itself is an instantiation of the replicator dynamics used in EGT. We show that under fairly common sparsity assumptions on rewards in such environments, ERPO converges to the optimal policy in the shifted environment. We empirically demonstrate that for path finding tasks in a number of environments, ERPO outperforms several popular RL and deep RL algorithms (PPO, A3C, DQN) in many scenarios and popular environments. This includes scenarios where the RL algorithms are allowed to train from scratch in the new environment, when they are retrained on the new environment, or when they are used in conjunction with domain randomization. ERPO shows faster policy adaptation, higher average rewards, and reduced computational costs in policy adaptation.

cs.LG↗

arXiv2024

Motion Planning for Automata-based Objectives using Efficient Gradient-based Methods

In recent years, there has been increasing interest in using formal methods-based techniques to safely achieve temporal tasks, such as timed sequence of goals, or patrolling objectives. Such tasks are often expressed in real-time logics such as Signal Temporal Logic (STL), whereby, the logical specification is encoded into an optimization problem. Such approaches usually involve optimizing over the quantitative semantics, or robustness degree, of the logic over bounded horizons: the semantics can be encoded as mixed-integer linear constraints or into smooth approximations of the robustness degree. A major limitation of this approach is that it faces scalability challenges with respect to temporal complexity: for example, encoding long-term tasks requires storing the entire history of the system. In this paper, we present a quantitative generalization of such tasks in the form of symbolic automata objectives. Specifically, we show that symbolic automata can be expressed as matrix operators that lend themselves to automatic differentiation, allowing for the use of off-the-shelf gradient-based optimizers. We show how this helps solve the need to store arbitrarily long system trajectories, while efficiently leveraging the task structure encoded in the automaton.

cs.FL↗

arXiv2024

Statistical Reachability Analysis of Stochastic Cyber-Physical Systems under Distribution Shift

Reachability analysis is a popular method to give safety guarantees for stochastic cyber-physical systems (SCPSs) that takes in a symbolic description of the system dynamics and uses set-propagation methods to compute an overapproximation of the set of reachable states over a bounded time horizon. In this paper, we investigate the problem of performing reachability analysis for an SCPS that does not have a symbolic description of the dynamics, but instead is described using a digital twin model that can be simulated to generate system trajectories. An important challenge is that the simulator implicitly models a probability distribution over the set of trajectories of the SCPS; however, it is typical to have a sim2real gap, i.e., the actual distribution of the trajectories in a deployment setting may be shifted from the distribution assumed by the simulator. We thus propose a statistical reachability analysis technique that, given a user-provided threshold $1-ε$, provides a set that guarantees that any reachable state during deployment lies in this set with probability not smaller than this threshold. Our method is based on three main steps: (1) learning a deterministic surrogate model from sampled trajectories, (2) conducting reachability analysis over the surrogate model, and (3) employing {\em robust conformal inference} using an additional set of sampled trajectories to quantify the surrogate model's distribution shift with respect to the deployed SCPS. To counter conservatism in reachable sets, we propose a novel method to train surrogate models that minimizes a quantile loss term (instead of the usual mean squared loss), and a new method that provides tighter guarantees using conformal inference using a normalized surrogate error. We demonstrate the effectiveness of our technique on various case studies.

cs.RO↗

arXiv2024

Robust Conformal Prediction for STL Runtime Verification under Distribution Shift

Cyber-physical systems (CPS) designed in simulators behave differently in the real-world. Once they are deployed in the real-world, we would hence like to predict system failures during runtime. We propose robust predictive runtime verification (RPRV) algorithms under signal temporal logic (STL) tasks for general stochastic CPS. The RPRV problem faces several challenges: (1) there may not be sufficient data of the behavior of the deployed CPS, (2) predictive models are based on a distribution over system trajectories encountered during the design phase, i.e., there may be a distribution shift during deployment. To address these challenges, we assume to know an upper bound on the statistical distance (in terms of an f-divergence) between the distributions at deployment and design time, and we utilize techniques based on robust conformal prediction. Motivated by our results in [1], we construct an accurate and an interpretable RPRV algorithm. We use a trajectory prediction model to estimate the system behavior at runtime and robust conformal prediction to obtain probabilistic guarantees by accounting for distribution shifts. We precisely quantify the relationship between calibration data, desired confidence, and permissible distribution shift. To the best of our knowledge, these are the first statistically valid algorithms under distribution shift in this setting. We empirically validate our algorithms on a Franka manipulator within the NVIDIA Isaac sim environment.

eess.SY↗

arXiv2024

Superflows: A New Tool for Forensic Network Flow Analysis

Network security analysts gather data from diverse sources, from high-level summaries of network flow and traffic volumes to low-level details such as service logs from servers and the contents of individual packets. They validate and check this data against traffic patterns and historical indicators of compromise. Based on the results of this analysis, a decision is made to either automatically manage the traffic or report it to an analyst for further investigation. Unfortunately, due rapidly increasing traffic volumes, there are far more events to check than operational teams can handle for effective forensic analysis. However, just as packets are grouped into flows that share a commonality, we argue that a high-level construct for grouping network flows into a set a flows that share a hypothesis is needed to significantly improve the quality of operational network response by increasing Events Per Analysts Hour (EPAH). In this paper, we propose a formalism for describing a superflow construct, which we characterize as an aggregation of one or more flows based on an analyst-specific hypothesis about traffic behavior. We demonstrate simple superflow constructions and representations, and perform a case study to explain how the formalism can be used to reduce the volume of data for forensic analysis.

cs.NI↗

arXiv2023

Data-Driven Template-Free Invariant Generation

Automatic verification of concurrent programs faces state explosion due to the exponential possible interleavings of its sequential components coupled with large or infinite state spaces. An alternative is deductive verification, where given a candidate invariant, we establish inductive invariance and show that any state satisfying the invariant is also safe. However, learning (inductive) program invariants is difficult. To this end, we propose a data-driven procedure to synthesize program invariants, where it is assumed that the program invariant is an expression that characterizes a (hopefully tight) over-approximation of the reachable program states. The main ideas of our approach are: (1) We treat a candidate invariant as a classifier separating states observed in (sampled) program traces from those speculated to be unreachable. (2) We develop an enumerative, template-free approach to learn such classifiers from positive and negative examples. At its core, our enumerative approach employs decision trees to generate expressions that do not over-fit to the observed states (and thus generalize). (3) We employ a runtime framework to monitor program executions that may refute the candidate invariant; every refutation triggers a revision of the candidate invariant. Our runtime framework can be viewed as an instance of statistical model checking, which gives us probabilistic guarantees on the candidate invariant. We also show that such in some cases, our counterexample-guided inductive synthesis approach converges (in probability) to an overapproximation of the reachable set of states. Our experimental results show that our framework excels in learning useful invariants using only a fraction of the set of reachable states for a wide variety of concurrent programs.

cs.PL↗

arXiv2023

Formalizing and Evaluating Requirements of Perception Systems for Automated Vehicles using Spatio-Temporal Perception Logic

Automated vehicles (AV) heavily depend on robust perception systems. Current methods for evaluating vision systems focus mainly on frame-by-frame performance. Such evaluation methods appear to be inadequate in assessing the performance of a perception subsystem when used within an AV. In this paper, we present a logic -- referred to as Spatio-Temporal Perception Logic (STPL) -- which utilizes both spatial and temporal modalities. STPL enables reasoning over perception data using spatial and temporal operators. One major advantage of STPL is that it facilitates basic sanity checks on the functional performance of the perception system, even without ground-truth data in some cases. We identify a fragment of STPL which is efficiently monitorable offline in polynomial time. Finally, we present a range of specifications for AV perception systems to highlight the types of requirements that can be expressed and analyzed through offline monitoring with STPL.

cs.RO↗

arXiv2023

Signal Temporal Logic-Guided Apprenticeship Learning

Apprenticeship learning crucially depends on effectively learning rewards, and hence control policies from user demonstrations. Of particular difficulty is the setting where the desired task consists of a number of sub-goals with temporal dependencies. The quality of inferred rewards and hence policies are typically limited by the quality of demonstrations, and poor inference of these can lead to undesirable outcomes. In this letter, we show how temporal logic specifications that describe high level task objectives, are encoded in a graph to define a temporal-based metric that reasons about behaviors of demonstrators and the learner agent to improve the quality of inferred rewards and policies. Through experiments on a diverse set of robot manipulator simulations, we show how our framework overcomes the drawbacks of prior literature by drastically improving the number of demonstrations required to learn a control policy.

cs.RO↗

arXiv2023

Data-Driven Reachability Analysis of Stochastic Dynamical Systems with Conformal Inference

We consider data-driven reachability analysis of discrete-time stochastic dynamical systems using conformal inference. We assume that we are not provided with a symbolic representation of the stochastic system, but instead have access to a dataset of $K$-step trajectories. The reachability problem is to construct a probabilistic flowpipe such that the probability that a $K$-step trajectory can violate the bounds of the flowpipe does not exceed a user-specified failure probability threshold. The key ideas in this paper are: (1) to learn a surrogate predictor model from data, (2) to perform reachability analysis using the surrogate model, and (3) to quantify the surrogate model's incurred error using conformal inference in order to give probabilistic reachability guarantees. We focus on learning-enabled control systems with complex closed-loop dynamics that are difficult to model symbolically, but where state transition pairs can be queried, e.g., using a simulator. We demonstrate the applicability of our method on examples from the domain of learning-enabled cyber-physical systems.

eess.SY↗