Safety Aware Task Planning via Large Language Models in Robotics

• URL: http://arxiv.org/abs/2503.15707v1
• Date: Wed, 19 Mar 2025 21:41:10 GMT
• Title: Safety Aware Task Planning via Large Language Models in Robotics
• Abstract summary: This paper introduces SAFER (Safety-Aware Framework for Execution in Robotics), a multi-LLM framework designed to embed safety awareness into robotic task planning.<n>Our framework integrates safety feedback at multiple stages of execution, enabling real-time risk assessment, proactive error correction, and transparent safety evaluation.
• Score: 22.72668275829238
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of large language models (LLMs) into robotic task planning has unlocked better reasoning capabilities for complex, long-horizon workflows. However, ensuring safety in LLM-driven plans remains a critical challenge, as these models often prioritize task completion over risk mitigation. This paper introduces SAFER (Safety-Aware Framework for Execution in Robotics), a multi-LLM framework designed to embed safety awareness into robotic task planning. SAFER employs a Safety Agent that operates alongside the primary task planner, providing safety feedback. Additionally, we introduce LLM-as-a-Judge, a novel metric leveraging LLMs as evaluators to quantify safety violations within generated task plans. Our framework integrates safety feedback at multiple stages of execution, enabling real-time risk assessment, proactive error correction, and transparent safety evaluation. We also integrate a control framework using Control Barrier Functions (CBFs) to ensure safety guarantees within SAFER's task planning. We evaluated SAFER against state-of-the-art LLM planners on complex long-horizon tasks involving heterogeneous robotic agents, demonstrating its effectiveness in reducing safety violations while maintaining task efficiency. We also verify the task planner and safety planner through actual hardware experiments involving multiple robots and a human.

Related papers

• AgentLens: Interpretable Safety Steering via Mechanistic Subspaces for Multi-Turn Coding Agent [53.82005364479556]
  Coding agents based on large language models (LLMs) demonstrate remarkable autonomous capabilities.<n>Existing safety mechanisms mainly rely on external guardrails.<n>We propose AgentLens, a white-box defense framework that performs runtime safety detection and representation-level mitigation.
  arXiv  Detail & Related papers  (2026-06-21T21:13:55Z)
• LPS-Bench: Benchmarking Safety Awareness of Computer-Use Agents in Long-Horizon Planning under Benign and Adversarial Scenarios [51.52395368061729]
  We present LPS-Bench, a benchmark that evaluates the planning-time safety awareness of MCP-based CUAs under long-horizon tasks.<n> Experiments reveal substantial deficiencies in existing CUAs' ability to maintain safe behavior.<n>We propose mitigation strategies to improve long-horizon planning safety in MCP-based CUA systems.
  arXiv  Detail & Related papers  (2026-02-03T08:40:24Z)
• PROTEA: Securing Robot Task Planning and Execution [8.03349484209617]
  Recent work on adversarial attacks has revealed significant vulnerabilities in existing robot task planners.<n>We introduce PROTEA, an LLM-as-a-Judge defense mechanism, to evaluate the security of task plans.<n>Our results provide actionable insights for robotic system practitioners seeking to enhance robustness and security of their task planning systems.
  arXiv  Detail & Related papers  (2026-01-12T04:13:46Z)
• RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic [56.38397499463889]
  Embodied agents powered by vision-language models (VLMs) are increasingly capable of executing complex real-world tasks.<n>However, they remain vulnerable to hazardous instructions that may trigger unsafe behaviors.<n>We propose RoboSafe, a runtime safeguard for embodied agents through executable predicate-based safety logic.
  arXiv  Detail & Related papers  (2025-12-24T15:01:26Z)
• MADRA: Multi-Agent Debate for Risk-Aware Embodied Planning [3.058137447286947]
  Existing methods often suffer from either high computational costs due to preference alignment training or over-rejection when using single-agent safety prompts.<n>We propose MADRA, a training-free Multi-Agent Debate Risk Assessment framework.<n>Our work provides a scalable, model-agnostic solution for building trustworthy embodied agents.
  arXiv  Detail & Related papers  (2025-11-26T14:51:37Z)
• UpSafe$^\circ$C: Upcycling for Controllable Safety in Large Language Models [67.91151588917396]
  Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful content generation and jailbreak attacks.<n>We propose UpSafe$circ$C, a unified framework for enhancing LLM safety through safety-aware upcycling.<n>Our results highlight a new direction for LLM safety: moving from static alignment toward dynamic, modular, and inference-aware control.
  arXiv  Detail & Related papers  (2025-10-02T16:43:33Z)
• SafeMind: Benchmarking and Mitigating Safety Risks in Embodied LLM Agents [7.975014390527644]
  Embodied agents powered by large language models (LLMs) inherit advanced planning capabilities; however, their direct interaction with the physical world exposes them to safety vulnerabilities.<n>We present SafeMindBench, a multimodal benchmark with 5,558 samples spanning four task categories (Instr-Risk, Env-Risk, Order-Fix, Req-Align) across high-risk scenarios such as sabotage, harm, privacy, and illegal behavior.<n>We introduce SafeMindAgent, a modular Planner-Executor architecture integrated with three cascaded safety modules, which incorporate safety constraints into the reasoning process.
  arXiv  Detail & Related papers  (2025-09-30T07:24:04Z)
• AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions [76.74726258534142]
  We propose AGENTSAFE, the first benchmark for evaluating the safety of embodied VLM agents under hazardous instructions.<n> AGENTSAFE simulates realistic agent-environment interactions within a simulation sandbox.<n> benchmark includes 45 adversarial scenarios, 1,350 hazardous tasks, and 8,100 hazardous instructions.
  arXiv  Detail & Related papers  (2025-06-17T16:37:35Z)
• SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator [77.86600052899156]
  Large Language Model (LLM)-based agents are increasingly deployed in real-world applications.<n>We propose AutoSafe, the first framework that systematically enhances agent safety through fully automated synthetic data generation.<n>We show that AutoSafe boosts safety scores by 45% on average and achieves a 28.91% improvement on real-world tasks.
  arXiv  Detail & Related papers  (2025-05-23T10:56:06Z)
• A Framework for Benchmarking and Aligning Task-Planning Safety in LLM-Based Embodied Agents [13.225168384790257]
  Large Language Models (LLMs) exhibit substantial promise in enhancing task-planning capabilities within embodied agents. We present Safe-BeAl, an integrated framework for the measurement (SafePlan-Bench) and alignment (Safe-Align) of LLM-based embodied agents' behaviors. Our empirical analysis reveals that even in the absence of adversarial inputs or malicious intent, LLM-based agents can exhibit unsafe behaviors.
  arXiv  Detail & Related papers  (2025-04-20T15:12:14Z)
• Graphormer-Guided Task Planning: Beyond Static Rules with LLM Safety Perception [4.424170214926035]
  We propose a risk-aware task planning framework that combines large language models with structured safety modeling.<n>Our approach constructs a dynamic-semantic safety graph, capturing spatial and contextual risk factors.<n>Unlike existing methods that rely on predefined safety constraints, our framework introduces a context-aware risk perception module.
  arXiv  Detail & Related papers  (2025-03-10T02:43:54Z)
• Safe LLM-Controlled Robots with Formal Guarantees via Reachability Analysis [0.6749750044497732]
  This paper introduces a safety assurance framework for Large Language Models (LLMs)-controlled robots based on data-driven reachability analysis.<n>Our approach provides rigorous safety guarantees against unsafe behaviors without relying on explicit analytical models.
  arXiv  Detail & Related papers  (2025-03-05T21:23:15Z)
• AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection [47.83354878065321]
  We propose AGrail, a lifelong guardrail to enhance agent safety.<n>AGrail features adaptive safety check generation, effective safety check optimization, and tool compatibility and flexibility.
  arXiv  Detail & Related papers  (2025-02-17T05:12:33Z)
• AgentGuard: Repurposing Agentic Orchestrator for Safety Evaluation of Tool Orchestration [0.3222802562733787]
  AgentGuard is a framework to autonomously discover and validate unsafe tool-use.<n>It generates safety constraints to confine the behaviors of agents, achieving the baseline of safety guarantee.<n>The framework operates through four phases: identifying unsafe, validating them in real-world execution, generating safety constraints, and validating constraint efficacy.
  arXiv  Detail & Related papers  (2025-02-13T23:00:33Z)
• SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents [58.65256663334316]
  We present SafeAgentBench -- the first benchmark for safety-aware task planning of embodied LLM agents in interactive simulation environments.<n>SafeAgentBench includes: (1) an executable, diverse, and high-quality dataset of 750 tasks, rigorously curated to cover 10 potential hazards and 3 task types; (2) SafeAgentEnv, a universal embodied environment with a low-level controller, supporting multi-agent execution with 17 high-level actions for 9 state-of-the-art baselines; and (3) reliable evaluation methods from both execution and semantic perspectives.
  arXiv  Detail & Related papers  (2024-12-17T18:55:58Z)
• Global Challenge for Safe and Secure LLMs Track 1 [57.08717321907755]
  The Global Challenge for Safe and Secure Large Language Models (LLMs) is a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks.
  arXiv  Detail & Related papers  (2024-11-21T08:20:31Z)
• Defining and Evaluating Physical Safety for Large Language Models [62.4971588282174]
  Large Language Models (LLMs) are increasingly used to control robotic systems such as drones. Their risks of causing physical threats and harm in real-world applications remain unexplored. We classify the physical safety risks of drones into four categories: (1) human-targeted threats, (2) object-targeted threats, (3) infrastructure attacks, and (4) regulatory violations.
  arXiv  Detail & Related papers  (2024-11-04T17:41:25Z)
• SCANS: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering [56.92068213969036]
  Safety alignment is indispensable for Large Language Models (LLMs) to defend threats from malicious instructions. Recent researches reveal safety-aligned LLMs prone to reject benign queries due to the exaggerated safety issue. We propose a Safety-Conscious Activation Steering (SCANS) method to mitigate the exaggerated safety concerns.
  arXiv  Detail & Related papers  (2024-08-21T10:01:34Z)
• On the Vulnerability of LLM/VLM-Controlled Robotics [54.57914943017522]
  We highlight vulnerabilities in robotic systems integrating large language models (LLMs) and vision-language models (VLMs) due to input modality sensitivities.<n>Our results show that simple input perturbations reduce task execution success rates by 22.2% and 14.6% in two representative LLM/VLM-controlled robotic systems.
  arXiv  Detail & Related papers  (2024-02-15T22:01:45Z)
• TrustAgent: Towards Safe and Trustworthy LLM-based Agents [50.33549510615024]
  This paper presents an Agent-Constitution-based agent framework, TrustAgent, with a focus on improving the LLM-based agent safety. The proposed framework ensures strict adherence to the Agent Constitution through three strategic components: pre-planning strategy which injects safety knowledge to the model before plan generation, in-planning strategy which enhances safety during plan generation, and post-planning strategy which ensures safety by post-planning inspection.
  arXiv  Detail & Related papers  (2024-02-02T17:26:23Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)

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.