A Model for Mediating Multi-Modal Human Intent into Safe Maneuvers for UAVs

• URL: http://arxiv.org/abs/2607.11654v1
• Date: Mon, 13 Jul 2026 15:04:57 GMT
• Title: A Model for Mediating Multi-Modal Human Intent into Safe Maneuvers for UAVs
• Abstract summary: We present a requirements-governed maneuver-response model that mediates multi-modal human intent into safe UAV maneuvers.<n>We formalize the approach as a requirements-based specification model in which maneuver primitives are associated with explicit preconditions.<n>We present an initial lab-based validation demonstrating that voice and GUI-based inputs can be reliably interpreted and safely executed.
• Score: 22.211736607636002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Direct human interaction with autonomous UAV systems can be enabled through modalities such as speech, gestures, and graphical interfaces. However, interpreting such inputs as directly executable commands introduces safety risks in dynamic environments. Operator requests may conflict with terrain constraints, inter-UAV separation requirements, or flight-envelope limitations. In this paper, we present a requirements-governed maneuver-response model that mediates multi-modal human intent into safe UAV maneuvers by treating operator inputs as bounded maneuver requests rather than direct commands. Requested maneuvers are mapped to constrained motion primitives and processed through a structured request-evaluate-execute pipeline. Each request is interpreted with associated confidence, validated against terrain, separation, workspace, and flight-envelope constraints, and either constrained, rejected, or executed under continuous runtime monitoring. We further formalize the approach as a requirements-based specification model in which maneuver primitives are associated with explicit preconditions, invariants, guard conditions, and postconditions governing admissibility, execution safety, and emergency handling. These requirements support runtime verification and future reactive synthesis approaches. We present an initial lab-based validation demonstrating that voice and GUI-based inputs can be reliably interpreted and safely executed as constrained maneuver requests.

Related papers

• Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle [65.6642776933861]
  This survey examines the protective paradigm that has grown around this intervention point.<n>We find that most protections are still validated mainly against static or weakly adaptive adversaries.<n>We close by consolidating cross-stage countermeasures and open problems for robust, composable, and deployable owner-side protection.
  arXiv  Detail & Related papers  (2026-08-05T00:46:50Z)
• Position: AI Safety Requires Effective Controllability [7.789894640525689]
  We argue that AI safety requires controllability as a first-class objective.<n>Experiments with OpenClaw-based agents show that current alignment and guardrail mechanisms reduce risk, but often fail to provide persistent, authoritative, and enforceable runtime control.
  arXiv  Detail & Related papers  (2026-05-26T14:53:24Z)
• Unlocking Proactivity in Task-Oriented Dialogue [21.99795860301162]
  Proactive task-oriented dialogue (TOD) demands a persuasive agent that actively probes the user's concerns and steers the conversation toward acceptance.<n>We show that conditioning on the user's latent concerns unlocks proactive capability that no amount of sampling can undermine.
  arXiv  Detail & Related papers  (2026-05-21T09:46:25Z)
• Selective Safety Steering via Value-Filtered Decoding [54.87935112120107]
  Large language models (LLMs) are trained to align with human values, but their generations may still violate safety constraints.<n>Existing decoding-time steering methods often intervene unnecessarily, modifying generations that would have been safe under the base model.<n>We propose a new test-time steering method designed to reduce such unnecessary interventions while improving the safety of unsafe responses.
  arXiv  Detail & Related papers  (2026-05-14T12:13:08Z)
• Containment Verification: AI Safety Guarantees Independent of Alignment [13.629487815782355]
  We introduce containment verification, which locates safety guarantees in the agentic framework itself.<n>Under havoc oracle semantics, the AI is modeled as an unconstrained oracle ranging over the entire typed action space.<n>For boundary-enforceable properties, expressed over modeled boundary events, action arguments, and state, we prove a universal guarantee.
  arXiv  Detail & Related papers  (2026-05-09T16:36:45Z)
• CORA: Conformal Risk-Controlled Agents for Safeguarded Mobile GUI Automation [68.53387633351484]
  We propose CORA (COnformal Risk-controlled GUI Agent), a post-policy, pre-action safeguarding framework that provides statistical guarantees on harmful executed actions.<n> CORA reformulates safety as selective action execution: we train a Guardian model to estimate action-conditional risk for each proposed step.<n>To rigorously evaluate this paradigm, we introduce Phone-Harm, a new benchmark of mobile safety violations with step-level harm labels.
  arXiv  Detail & Related papers  (2026-04-10T09:41:21Z)
• Autonomous Action Runtime Management(AARM):A System Specification for Securing AI-Driven Actions at Runtime [0.0]
  This paper introduces Autonomous Action Management (AARM), an open specification for securing AI-driven actions at runtime.<n>AARM intercepts actions before execution, accumulates session context, evaluates against policy and intent alignment, enforces authorization decisions, and records tamper-evident receipts for forensic reconstruction.<n>AARM is model-agnostic, framework-agnostic, and vendor-neutral, treating action execution as the stable security boundary.
  arXiv  Detail & Related papers  (2026-02-10T05:57:30Z)
• SPACeR: Self-Play Anchoring with Centralized Reference Models [50.55045557371374]
  Sim agent policies are realistic, human-like, fast, and scalable in multi-agent settings.<n>Recent progress in imitation learning with large diffusion-based or tokenized models has shown that behaviors can be captured directly from human driving data.<n>We propose SPACeR, a framework that leverages a pretrained tokenized autoregressive motion model as a central reference policy.
  arXiv  Detail & Related papers  (2025-10-20T19:53:02Z)
• Constrained Decoding for Robotics Foundation Models [12.916330118607918]
  We introduce SafeDec, a constrained decoding framework for autoregressive robot foundation models.<n>Task-specific safety rules are expressed as Signal Temporal Logic (STL) formulas and are enforced at inference time with minimal overhead.<n>Our method ensures that generated actions provably satisfy STL specifications under assumed dynamics at runtime without retraining.
  arXiv  Detail & Related papers  (2025-09-01T19:17:40Z)
• DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents [52.92354372596197]
  Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities.<n>This interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent's behavior.<n>We propose a Dynamic Rule-based Isolation Framework for Trustworthy agentic systems, which enforces both control and data-level constraints.
  arXiv  Detail & Related papers  (2025-06-13T05:01:09Z)
• Diffusion Predictive Control with Constraints [51.91057765703533]
  Diffusion predictive control with constraints (DPCC) is an algorithm for diffusion-based control with explicit state and action constraints.<n>We show through simulations of a robot manipulator that DPCC outperforms existing methods in satisfying novel test-time constraints.
  arXiv  Detail & Related papers  (2024-12-12T15:10:22Z)
• Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues [54.81155589931697]
  Collaborative Instance object Navigation (CoIN) is a new task setting where the agent actively resolve uncertainties about the target instance.<n>We propose a novel training-free method, Agent-user Interaction with UncerTainty Awareness (AIUTA)<n>First, upon object detection, a Self-Questioner model initiates a self-dialogue within the agent to obtain a complete and accurate observation description.<n>An Interaction Trigger module determines whether to ask a question to the human, continue or halt navigation.
  arXiv  Detail & Related papers  (2024-12-02T08:16:38Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.