arXiv subjects

Alkesh K. Srivastava

Publications and source records attributed to Alkesh K. Srivastava.

9 recordsLinked to original sources

arXiv2026

Spatial-Semantic Uncertainty in VLM-Based Target Search: Balancing Exploration and Identification

Robots searching for a target from a natural-language description must determine not only where to search, but also which observed candidate is the desired target. These decisions reflect two distinct sources of uncertainty - spatial uncertainty over candidate locations and semantic uncertainty over target identity - that are often conflated in VLM-based search systems. We introduce a spatial-semantic uncertainty formulation that maintains separate beliefs over each component and integrates probabilistic VLM evidence into a global target-identity posterior, including probability mass for undiscovered targets. This decomposition allows an information-theoretic planner to independently value candidate discovery and target disambiguation through spatial and semantic expected information gain (EIG), providing an explicit mechanism for trading broader exploration against earlier identification. We evaluate six VLM uncertainty-elicitation interfaces on 500 synthetic targets and show that similar recognition accuracy can conceal substantial differences in calibration and false confidence. In degraded-observation search-and-identify experiments, EIG-based planners reach confident decisions in 75.0%-92.5% of trials, compared with 20.0% for Random search, while different spatial-semantic weightings achieve comparable identification accuracy once confidence is attained. Increasing semantic emphasis reduces unnecessary exploration and VLM queries, demonstrating that explicitly planning over semantic uncertainty can accelerate target resolution without sacrificing decision quality. These results highlight the distinct roles of uncertainty representation and uncertainty-driven planning in embodied VLM systems.

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arXiv2026

When Information is Worth the Risk: Behavioral Valuation for Hazardous Robotic Exploration

Hazardous robotic exploration requires robots to map spatial risks, such as unsafe terrain, radiation, fire, mines, or structural damage, while operating where collecting information can itself cause failure. A highly informative path may expose the robot to hazards, terminate execution, and prevent future observations. Hazardous exploration therefore requires deciding not only where uncertainty is largest, but when reducing it is worth the risk. This paper introduces a valuation-layer view of this problem. We keep the belief update, sensor model, physical risk model, and finite-horizon informative planner fixed, and change only the scalar objective used to rank feasible paths. Within this framework, we introduce a risk-augmented Behavioral Information objective based on Prelec probability weighting, yielding an interpretable family of conservative-to-aggressive information-risk valuations. Theoretically, we show that valuation parameters create switching boundaries between high-information/high-risk and lower-information/lower-risk paths, and induce a transformed Pareto-frontier structure over feasible exploration policies. Large-scale failure-truncated grid-world experiments show that valuation alone reshapes the information-risk frontier. Shannon information planning remains a strong raw-information baseline, while risk-aware objectives can reduce hazard exposure and robot losses by avoiding failures that truncate future sensing. Risk-augmented Behavioral valuation is Pareto-competitive with standard risk-aware baselines and provides interpretable conservative and intermediate regimes. These results support a framework in which robots reason not only about how much uncertainty an action reduces, but whether that reduction is worth the risk required to obtain it.

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arXiv2026

Bayesian Networks for Path-Based Sensors: Gathering Information and Path Planning in Communication Denied Environments

A "path-based sensor" produces a single observation along a continuous path. For example, a boolean path-based sensor returns a single "1" if an event of interest is detected at any point along the path and a "0" otherwise. Notably, a "1" provides no direct information about where along the path the event(s) may have occurred. Previous work has demonstrated that observations from multiple path-based sensors can be fused to create a Bayesian belief map over the spatial locations of the underlying event or phenomenon. Moreover, path planning can employ Shannon information theory to accelerate the rate of convergence of the belief map. In this paper, we present a new method to update the belief map based on a path-based sensor observation, and then plan paths to increase information gain. In contrast to prior work that approximates the posterior by averaging over the alternative event histories, we introduce a Bayesian Network (BN) formulation that models the probabilistic relationships between the latent variables and path-based sensor measurements, enabling a more principled Bayesian belief update. We consider static hazard detection in a communication-denied environment as a representative problem setting. The event of a robot returning from its path corresponds to a path-based hazard sensor reading of "0" (hazard not detected), while a robot failing to return corresponds to a reading of "1" (hazard detected). We consider false positives and false negatives. We find that the new method leads to quicker convergence of the belief map than prior work in both single- and multi-robot cases.

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arXiv2026

Relay-Based Coordination for Energy-Efficient Multi-Robot Pickup and Delivery

We consider the problem of delivering multiple packages from a single depot to distinct goal locations using a homogeneous fleet of robots with limited carrying capacity. We propose VCST-RCP, a Voronoi-Constrained Steiner Tree Relay Coordination Planning framework that explicitly treats inter-robot relays as a design primitive. The approach operates in two stages: (i) constructing a sparse relay backbone by combining Voronoi-derived exchange interfaces with Steiner tree optimization, and (ii) synthesizing robot-level pickup, relay, and delivery schedules under capacity and service-time constraints. Unlike traditional methods that rely on direct source-to-destination transport, our framework organizes package flow through a shared relay network, reducing redundant long-haul motion. Extensive experiments across multiple scales show that VCST-RCP reduces total fleet travel distance by an average of 31% (up to nearly 50%) compared to Hungarian assignment and significantly outperforms OR-Tools CVRP, with statistically significant improvements (p < 10^{-3}). These gains translate into over 50% higher delivery efficiency (packages per kilometer), directly improving energy utilization. An ablation study further reveals that optimizing relay placement yields substantially larger improvements than adapting spatial partitioning alone, establishing relay design as the dominant factor governing system performance. Overall, the results demonstrate that relay-based coordination provides a scalable and effective framework for energy-aware multi-robot delivery in real-world logistics settings.

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arXiv2026

A Kinetic Theory of Encounter-Based Information Propagation in Multi-Robot Systems

Multi-robot systems cannot assume persistent network connectivity. We study this problem through target tracking, where performance depends on how quickly target information is sensed, transported through the team, and used before it becomes stale. When robots exchange information only through physical encounters, tracking becomes a kinetic information-transport problem: robot motion induces encounters, encounters carry target-state estimates, information age determines staleness, and stale information produces tracking error. This paper develops a kinetic theory of encounter-based information propagation and identifies three limits. The first is an access limit -- information cannot support team-level coordination unless it spreads beyond the robots that sensed it. The second is a staleness limit -- even propagated information loses value as the target moves. The third is a geometry limit -- when target motion outpaces information transport, tracking error approaches a saturation regime where communication improvements alone have diminishing returns. We evaluate the theory through large-scale simulations varying team size, operating area, communication range, and target speed. Results support the proposed access-staleness-geometry decomposition: communication coverage governs the access transition; once information is accessible, tracking error is shaped by target displacement; and this response is locally linear in restricted regimes but nonlinear over broader ranges because of sensing refreshes and bounded geometry. Across controlled sweeps and joint variation, the derived access and staleness coordinates reliably describe tracking performance. Together, these results establish a kinetic-theoretic framework for predicting and designing encounter-based multi-robot systems.

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arXiv2025

DELIVER: A System for LLM-Guided Coordinated Multi-Robot Pickup and Delivery using Voronoi-Based Relay Planning

We present DELIVER (Directed Execution of Language-instructed Item Via Engineered Relay), a fully integrated framework for cooperative multi-robot pickup and delivery driven by natural language commands. DELIVER unifies natural language understanding, spatial decomposition, relay planning, and motion execution to enable scalable, collision-free coordination in real-world settings. Given a spoken or written instruction, a lightweight instance of LLaMA3 interprets the command to extract pickup and delivery locations. The environment is partitioned using a Voronoi tessellation to define robot-specific operating regions. Robots then compute optimal relay points along shared boundaries and coordinate handoffs. A finite-state machine governs each robot's behavior, enabling robust execution. We implement DELIVER on the MultiTRAIL simulation platform and validate it in both ROS2-based Gazebo simulations and real-world hardware using TurtleBot3 robots. Empirical results show that DELIVER maintains consistent mission cost across varying team sizes while reducing per-agent workload by up to 55% compared to a single-agent system. Moreover, the number of active relay agents remains low even as team size increases, demonstrating the system's scalability and efficient agent utilization. These findings underscore DELIVER's modular and extensible architecture for language-guided multi-robot coordination, advancing the frontiers of cyber-physical system integration.

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arXiv2025

Behaviorally Adaptive Multi-Robot Hazard Localization in Failure-Prone, Communication-Denied Environments

We address the challenge of multi-robot autonomous hazard mapping in high-risk, failure-prone, communication-denied environments such as post-disaster zones, underground mines, caves, and planetary surfaces. In these missions, robots must explore and map hazards while minimizing the risk of failure due to environmental threats or hardware limitations. We introduce a behavior-adaptive, information-theoretic planning framework for multi-robot teams grounded in the concept of Behavioral Entropy (BE), that generalizes Shannon entropy (SE) to capture diverse human-like uncertainty evaluations. Building on this formulation, we propose the Behavior-Adaptive Path Planning (BAPP) framework, which modulates information gathering strategies via a tunable risk-sensitivity parameter, and present two planning algorithms: BAPP-TID for intelligent triggering of high-fidelity robots, and BAPP-SIG for safe deployment under high risk. We provide theoretical insights on the informativeness of the proposed BAPP framework and validate its effectiveness through both single-robot and multi-robot simulations. Our results show that the BAPP stack consistently outperforms Shannon-based and random strategies: BAPP-TID accelerates entropy reduction, while BAPP-SIG improves robot survivability with minimal loss in information gain. In multi-agent deployments, BAPP scales effectively through spatial partitioning, mobile base relocation, and role-aware heterogeneity. These findings underscore the value of behavior-adaptive planning for robust, risk-sensitive exploration in complex, failure-prone environments.

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arXiv2024

Speech-Guided Sequential Planning for Autonomous Navigation using Large Language Model Meta AI 3 (Llama3)

In social robotics, a pivotal focus is enabling robots to engage with humans in a more natural and seamless manner. The emergence of advanced large language models (LLMs) such as Generative Pre-trained Transformers (GPTs) and autoregressive models like Large Language Model Meta AI (Llamas) has driven significant advancements in integrating natural language understanding capabilities into social robots. This paper presents a system for speech-guided sequential planning in autonomous navigation, utilizing Llama3 and the Robot Operating System~(ROS). The proposed system involves using Llama3 to interpret voice commands, extracting essential details through parsing, and decoding these commands into sequential actions for tasks. Such sequential planning is essential in various domains, particularly in the pickup and delivery of an object. Once a sequential navigation task is evaluated, we employ DRL-VO, a learning-based control policy that allows a robot to autonomously navigate through social spaces with static infrastructure and (crowds of) people. We demonstrate the effectiveness of the system in simulation experiment using Turtlebot 2 in ROS1 and Turtlebot 3 in ROS2. We conduct hardware trials using a Clearpath Robotics Jackal UGV, highlighting its potential for real-world deployment in scenarios requiring flexible and interactive robotic behaviors.

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arXiv2023

Path-Based Sensors: Will the Knowledge of Correlation in Random Variables Accelerate Information Gathering?

Effective communication is crucial for deploying robots in mission-specific tasks, but inadequate or unreliable communication can greatly reduce mission efficacy, for example in search and rescue missions where communication-denied conditions may occur. In such missions, robots are deployed to locate targets, such as human survivors, but they might get trapped at hazardous locations, such as in a trapping pit or by debris. Thus, the information the robot collected is lost owing to the lack of communication. In our prior work, we developed the notion of a path-based sensor. A path-based sensor detects whether or not an event has occurred along a particular path, but it does not provide the exact location of the event. Such path-based sensor observations are well-suited to communication-denied environments, and various studies have explored methods to improve information gathering in such settings. In some missions it is typical for target elements to be in close proximity to hazardous factors that hinder the information-gathering process. In this study, we examine a similar scenario and conduct experiments to determine if additional knowledge about the correlation between hazards and targets improves the efficiency of information gathering. To incorporate this knowledge, we utilize a Bayesian network representation of domain knowledge and develop an algorithm based on this representation. Our empirical investigation reveals that such additional information on correlation is beneficial only in environments with moderate hazard lethality, suggesting that while knowledge of correlation helps, further research and development is necessary for optimal outcomes.

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