arXiv subjects

David Smith Sundarsingh

Publications and source records attributed to David Smith Sundarsingh.

5 recordsLinked to original sources

arXiv2026

ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guarantees

Linear Temporal Logic (LTL) is a widely used task specification language for autonomous systems. To mitigate the significant manual effort and expertise required to define LTL-encoded tasks, several methods have been proposed for translating Natural Language (NL) instructions into LTL formulas, which, however, lack correctness guarantees. To address this, we propose a new NL-to-LTL translation method, called ConformalNL2LTL that achieves user-defined translation success rates on unseen NL commands. Our method constructs LTL formulas iteratively by solving a sequence of open-vocabulary question-answering (QA) problems using large language models (LLMs). These QA tasks are handled collaboratively by a primary and an auxiliary model. The primary model answers each QA instance while quantifying uncertainty via conformal prediction; when it is insufficiently certain according to user-defined confidence thresholds, it requests assistance from the auxiliary model and, if necessary, from the user. We demonstrate theoretically and empirically that ConformalNL2LTL achieves the desired translation accuracy while minimizing user intervention.

cs.CL↗

arXiv2026

Safe Planning in Unknown Environments Using Conformalized Semantic Maps

This paper addresses semantic planning problems in unknown environments under perceptual uncertainty. The environment contains multiple unknown semantically labeled regions or objects, and the robot must reach desired locations while maintaining class-dependent distances from them. We aim to compute robot paths that complete such semantic reach-avoid tasks with user-defined probability despite uncertain perception. Existing planning algorithms either ignore perceptual uncertainty, thus lacking correctness guarantees, or assume known sensor models and noise characteristics. In contrast, we present the first planner for semantic reach-avoid tasks that achieves user-specified mission completion rates without requiring any knowledge of sensor models or noise. This is enabled by quantifying uncertainty in semantic maps, constructed on-the-fly from perceptual measurements, using conformal prediction in a model and distribution free manner. We validate our approach and the theoretical mission completion rates through extensive experiments, showing that it consistently outperforms baselines in mission success rates.

cs.RO↗

arXiv2025

Controller for Incremental Input-to-State Practical Stabilization of Partially Unknown systems with Invariance Guarantees

Incremental stability is a property of dynamical systems that ensures the convergence of trajectories with respect to each other rather than a fixed equilibrium point or a fixed trajectory. In this paper, we introduce a related stability notion called incremental input-to-state practical stability (δ-ISpS), ensuring safety guarantees. We also present a feedback linearization based control design scheme that renders a partially unknown system incrementally input-to-state practically stable and safe with formal guarantees. To deal with the unknown dynamics, we utilize Gaussian process regression to approximate the model. Finally, we implement the controller synthesized by the proposed scheme on a manipulator example

eess.SY↗

arXiv2024

Backstepping Design for Incremental Input-to-State Stabilization of Unknown Systems

Incremental stability of dynamical systems ensures the convergence of trajectories from different initial conditions towards each other rather than a fixed trajectory or equilibrium point. Here, we introduce and characterize a novel class of incremental Lyapunov functions, an incremental stability notion known as Incremental Input-to-State practical Stability (δ-ISpS). Using Gaussian Process, we learn the unknown dynamics of a class of control systems. We then present a backstepping control design scheme that provides state-feedback controllers that render the partially unknown control system δ-ISpS. To show the effectiveness of the proposed controller, we implement it in two case studies.

eess.SY↗

arXiv2023

Barrier Function-based Distributed Symbolic Controller for Multi-Agent Systems

Because of the scalability issues associated with the symbolic controller synthesis approach, employing it in a multi-agent system (MAS) framework becomes difficult. In this paper, we present a novel approach for synthesizing distributed symbolic controllers for MAS, that enforces a local Linear Temporal Logic (LTL) specification on each agent and global safety specifications on the MAS, in a computationally efficient manner by leveraging the concept of control barrier functions (CBF). In addition, we also provide an analysis on the effect of the CBF parameters on the conservatism introduced by our proposed approach in the size and domain of the synthesized controller. The effectiveness of this approach is demonstrated through a comparison with the conventional monolithic symbolic control, using simulation as well as hardware demonstrations.

eess.SY↗