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

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.

Enhancing Object Search in Indoor Spaces via Personalized Object-factored Ontologies

Personalization is critical for the advancement of service robots. Robots need to develop tailored understandings of the environments they are put in. Moreover, they need to be aware of changes in the environment to facilitate long-term deployment. Long-term understanding as well as personalization is necessary to execute complex tasks like prepare dinner table or tidy my room. A precursor to such tasks is that of Object Search. Consequently, this paper focuses on locating and searching multiple objects in indoor environments. In this paper, we propose two crucial novelties. Firstly, we propose a novel framework that can enable robots to deduce Personalized Ontologies of indoor environments. Our framework consists of a personalization schema that enables the robot to tune its understanding of ontologies. Secondly, we propose an Adaptive Inferencing strategy. We integrate Dynamic Belief Updates into our approach which improves performance in multi-object search tasks. The cumulative effect of personalization and adaptive inferencing is an improved capability in long-term object search. This framework is implemented on top of a multi-layered semantic map. We conduct experiments in real environments and compare our results against various state-of-the-art (SOTA) methods to demonstrate the effectiveness of our approach. Additionally, we show that personalization can act as a catalyst to enhance the performance of SOTAs. Video Link: https://bit.ly/3WHk9i9

Preferential Multi-Target Search in Indoor Environments using Semantic SLAM

In recent years, the demand for service robots capable of executing tasks beyond autonomous navigation has grown. In the future, service robots will be expected to perform complex tasks like 'Set table for dinner'. High-level tasks like these, require, among other capabilities, the ability to retrieve multiple targets. This paper delves into the challenge of locating multiple targets in an environment, termed 'Find my Objects.' We present a novel heuristic designed to facilitate robots in conducting a preferential search for multiple targets in indoor spaces. Our approach involves a Semantic SLAM framework that combines semantic object recognition with geometric data to generate a multi-layered map. We fuse the semantic maps with probabilistic priors for efficient inferencing. Recognizing the challenges introduced by obstacles that might obscure a navigation goal and render standard point-to-point navigation strategies less viable, our methodology offers resilience to such factors. Importantly, our method is adaptable to various object detectors, RGB-D SLAM techniques, and local navigation planners. We demonstrate the 'Find my Objects' task in real-world indoor environments, yielding quantitative results that attest to the effectiveness of our methodology. This strategy can be applied in scenarios where service robots need to locate, grasp, and transport objects, taking into account user preferences. For a brief summary, please refer to our video: https://tinyurl.com/PrefTargetSearch