Context-Aware Risk Estimation in Home Environments: A Probabilistic Framework for Service Robots

• URL: http://arxiv.org/abs/2508.19788v1
• Date: Wed, 27 Aug 2025 11:14:05 GMT
• Title: Context-Aware Risk Estimation in Home Environments: A Probabilistic Framework for Service Robots
• Authors: Sena Ishii, Akash Chikhalikar, Ankit A. Ravankar, Jose Victorio Salazar Luces, Yasuhisa Hirata,
• Abstract summary: We present a novel framework for estimating accident-prone regions in everyday indoor scenes, aimed at improving real-time risk awareness in service robots.<n>Our approach models object-level risk and context through a semantic graph-based propagation algorithm.<n>Our method is validated on a dataset with human-annotated risk regions, achieving a binary risk detection accuracy of 75%.
• Score: 2.5695499302569327
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a novel framework for estimating accident-prone regions in everyday indoor scenes, aimed at improving real-time risk awareness in service robots operating in human-centric environments. As robots become integrated into daily life, particularly in homes, the ability to anticipate and respond to environmental hazards is crucial for ensuring user safety, trust, and effective human-robot interaction. Our approach models object-level risk and context through a semantic graph-based propagation algorithm. Each object is represented as a node with an associated risk score, and risk propagates asymmetrically from high-risk to low-risk objects based on spatial proximity and accident relationship. This enables the robot to infer potential hazards even when they are not explicitly visible or labeled. Designed for interpretability and lightweight onboard deployment, our method is validated on a dataset with human-annotated risk regions, achieving a binary risk detection accuracy of 75%. The system demonstrates strong alignment with human perception, particularly in scenes involving sharp or unstable objects. These results underline the potential of context-aware risk reasoning to enhance robotic scene understanding and proactive safety behaviors in shared human-robot spaces. This framework could serve as a foundation for future systems that make context-driven safety decisions, provide real-time alerts, or autonomously assist users in avoiding or mitigating hazards within home environments.

Related papers

• Beta Distribution Learning for Reliable Roadway Crash Risk Assessment [21.371420424228077]
  Roadway traffic accidents represent a global health crisis, responsible for over a million deaths annually and costing many countries up to 3% of their GDP.<n>Traditional traffic safety studies often examine risk factors in isolation, overlooking the spatial complexity and contextual interactions inherent in the built environment.<n>We introduce a novel deep learning framework that leverages satellite imagery as a comprehensive spatial input.<n>This approach enables the model to capture the nuanced spatial patterns and embedded environmental risk factors that contribute to fatal crash risks.
  arXiv  Detail & Related papers  (2025-11-07T00:08:55Z)
• ANNIE: Be Careful of Your Robots [48.89876809734855]
  We present the first systematic study of adversarial safety attacks on embodied AI systems.<n>We show attack success rates exceeding 50% across all safety categories.<n>Results expose a previously underexplored but highly consequential attack surface in embodied AI systems.
  arXiv  Detail & Related papers  (2025-09-03T15:00:28Z)
• Probabilistic modelling and safety assurance of an agriculture robot providing light-treatment [0.0]
  Continued adoption of agricultural robots postulates the farmer's trust in the reliability, robustness and safety of the new technology.<n>This paper considers a probabilistic modelling and risk analysis framework for use in the early development phases.
  arXiv  Detail & Related papers  (2025-06-24T13:39:32Z)
• 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)
• An Approach to Technical AGI Safety and Security [72.83728459135101]
  We develop an approach to address the risk of harms consequential enough to significantly harm humanity.<n>We focus on technical approaches to misuse and misalignment.<n>We briefly outline how these ingredients could be combined to produce safety cases for AGI systems.
  arXiv  Detail & Related papers  (2025-04-02T15:59:31Z)
• Don't Let Your Robot be Harmful: Responsible Robotic Manipulation via Safety-as-Policy [53.048430683355804]
  Unthinking execution of human instructions in robotic manipulation can lead to severe safety risks.<n>We present Safety-as-policy, which includes (i) a world model to automatically generate scenarios containing safety risks and conduct virtual interactions, and (ii) a mental model to infer consequences with reflections.<n>We show that Safety-as-policy can avoid risks and efficiently complete tasks in both synthetic dataset and real-world experiments.
  arXiv  Detail & Related papers  (2024-11-27T12:27:50Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Risk-aware Trajectory Prediction by Incorporating Spatio-temporal Traffic Interaction Analysis [3.7414278978078204]
  We propose to gain this information by analyzing locations and speeds that commonly correspond to high-risk interactions within the dataset. We use it within training to generate better predictions in high risk situations.
  arXiv  Detail & Related papers  (2024-07-15T11:57:06Z)
• Learning Risk-Aware Quadrupedal Locomotion using Distributional Reinforcement Learning [12.156082576280955]
  Deployment in hazardous environments requires robots to understand the risks associated with their actions and movements to prevent accidents. We propose a risk sensitive locomotion training method employing distributional reinforcement learning to consider safety explicitly. We show emergent risk sensitive locomotion behavior in simulation and on the quadrupedal robot ANYmal.
  arXiv  Detail & Related papers  (2023-09-25T16:05:32Z)
• Towards Risk Modeling for Collaborative AI [5.941104748966331]
  Collaborative AI systems aim at working together with humans in a shared space to achieve a common goal. This setting imposes potentially hazardous circumstances due to contacts that could harm human beings. We introduce a risk modeling approach tailored to Collaborative AI systems.
  arXiv  Detail & Related papers  (2021-03-12T18:53:06Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)

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.