arXiv subjects

Kartik Anand Pant

Publications and source records attributed to Kartik Anand Pant.

4 recordsLinked to original sources

arXiv2026

Signal Temporal Logic Evaluation and Synthesis Using Deep Reachability Analysis and Layered Control Architecture

We propose a signal temporal logic (STL)-based framework that checks the feasibility of a mission described in STL and synthesizes control to safely execute it. The proposed framework evaluates the safety and reliability of operation through two phases. First, the proposed framework assesses the feasibility of STL by computing a backward reachable tube (BRT), which captures all states that can satisfy the given STL. The proposed framework accommodates the multiple reach-avoid (MRA) problem to address more general STL specifications and leverages a deep neural network to alleviate the computation burden for reachability analysis, significantly reducing the inference time compared to a baseline method. We further propose a layered planning and control architecture that combines mixed-integer linear programming (MILP) for global planning with model predictive control (MPC) as a local controller for the given STL. Consequently, the proposed framework can robustly handle unexpected behavior of obstacles that are not described in the environment information or STL, thereby providing reliable mission performance. Our numerical simulations demonstrate that the proposed framework can successfully compute BRT for a given STL and perform the mission.

eess.SY↗

arXiv2026

Reachability-based Temporal Logic Verification for Reliable LLM-guided Human-Autonomy Teaming

We propose a reachability-based framework for reliable LLM-guided human-autonomy teaming (HAT) using signal temporal logic (STL). In the proposed framework, LLM is leveraged as a translator that transfers natural language commands given by a human operator into corresponding STL specifications or vice versa. An STL feasibility filter (SFF) is proposed to check the feasibility of the generated STL. The SFF first decomposes the complex and nested LLM translation into a set of simpler subformulas for parallelization and informative feedback generation. The reachability analysis method is then applied to verify if each subformula is feasible for a target dynamical system: if feasible, perform mission planning, otherwise, reject it. The proposed SFF can identify infeasible subformulas, more than simply providing the boolean verification results for the whole STL, thereby facilitating the feedback generation of LLM to request modification of the command to the human. Consequently, the proposed framework can allow more reliable HAT by enabling safe and informative communication between the human operator and the autonomous agent. Our experiments demonstrate that the proposed framework can successfully filter out infeasible subformulas and generate informative feedback based on such information.

eess.SY↗

arXiv2022

An Open-Source Gazebo Plugin for GNSS Multipath Signal Emulation in Virtual Urban Canyons

One of the major errors affecting GNSS signals in urban canyons is GNSS multipath error. In this work, we develop a Gazebo plugin which utilizes a ray tracing technique to account for multipath effects in a virtual urban canyon environment using virtual satellites. This software plugin balances accuracy and computational complexity to run the simulation in real-time for both software-in-the-loop (SITL) and hardware-in-the-loop (HITL) testing. We also construct a 3D virtual environment of Hong Kong and compare the results from our plugin with the GNSS data in the publicly available Urban-Nav dataset, to validate the efficacy of the proposed Gazebo Plugin. The plugin is openly available to all the researchers in the robotics community. https://github.com/kpant14/multipath_sim

cs.RO↗

arXiv2022

Thompson Sampling with Virtual Helping Agents

We address the problem of online sequential decision making, i.e., balancing the trade-off between exploiting the current knowledge to maximize immediate performance and exploring the new information to gain long-term benefits using the multi-armed bandit framework. Thompson sampling is one of the heuristics for choosing actions that address this exploration-exploitation dilemma. We first propose a general framework that helps heuristically tune the exploration versus exploitation trade-off in Thompson sampling using multiple samples from the posterior distribution. Utilizing this framework, we propose two algorithms for the multi-armed bandit problem and provide theoretical bounds on the cumulative regret. Next, we demonstrate the empirical improvement in the cumulative regret performance of the proposed algorithm over Thompson Sampling. We also show the effectiveness of the proposed algorithm on real-world datasets. Contrary to the existing methods, our framework provides a mechanism to vary the amount of exploration/ exploitation based on the task at hand. Towards this end, we extend our framework for two additional problems, i.e., best arm identification and time-sensitive learning in bandits and compare our algorithm with existing methods.

cs.LG↗