AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments

• URL: http://arxiv.org/abs/2506.11773v3
• Date: Wed, 06 Aug 2025 15:26:58 GMT
• Title: AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments
• Abstract summary: We leverage the idea of embodied AI agents-virtual agents that perceive and act within simulated environments guided by internal world models.<n>We introduce AgentSense, a virtual data generation pipeline in which agents live out daily routines in simulated smart homes.<n>Our approach produces rich, privacy-preserving sensor data that reflects real-world diversity.
• Score: 2.9698967396161624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A major challenge in developing robust and generalizable Human Activity Recognition (HAR) systems for smart homes is the lack of large and diverse labeled datasets. Variations in home layouts, sensor configurations, and individual behaviors further exacerbate this issue. To address this, we leverage the idea of embodied AI agents-virtual agents that perceive and act within simulated environments guided by internal world models. We introduce AgentSense, a virtual data generation pipeline in which agents live out daily routines in simulated smart homes, with behavior guided by Large Language Models (LLMs). The LLM generates diverse synthetic personas and realistic routines grounded in the environment, which are then decomposed into fine-grained actions. These actions are executed in an extended version of the VirtualHome simulator, which we augment with virtual ambient sensors that record the agents' activities. Our approach produces rich, privacy-preserving sensor data that reflects real-world diversity. We evaluate AgentSense on five real HAR datasets. Models pretrained on the generated data consistently outperform baselines, especially in low-resource settings. Furthermore, combining the generated virtual sensor data with a small amount of real data achieves performance comparable to training on full real-world datasets. These results highlight the potential of using LLM-guided embodied agents for scalable and cost-effective sensor data generation in HAR.

Related papers

• OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation [51.37436817948304]
  OASIS is a simulation-data-driven framework for humanoid loco-manipulation.<n>It reconstructs realistic object assets from real-world images using a 3D generative model.<n>Under zero-shot deployment, it achieves higher success rates on most tasks than that trained on real-robot teleoperation data.
  arXiv  Detail & Related papers  (2026-06-07T10:01:53Z)
• Toward Visually Realistic Simulation: A Benchmark for Evaluating Robot Manipulation in Simulation [62.51953630639423]
  Existing benchmarks lack visual realism, creating a large domain gap between simulation and reality.<n>We propose VISER, a visually realistic benchmark for evaluating robot manipulation in simulation.<n>VISER features a high-fidelity dataset of over 1,000 3D assets with physically-based rendering (PBR) materials, along with 3D scenes created from these assets through curated layouts or generation.
  arXiv  Detail & Related papers  (2026-05-07T14:13:05Z)
• R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized Manipulation [74.41728218960465]
  We propose a real-to-real 3D data generation framework (R2RGen) that directly augments the pointcloud observation-action pairs to generate real-world data.<n>R2RGen substantially enhances data efficiency on extensive experiments and demonstrates strong potential for scaling and application on mobile manipulation.
  arXiv  Detail & Related papers  (2025-10-09T17:55:44Z)
• UrbanTwin: Building High-Fidelity Digital Twins for Sim2Real LiDAR Perception and Evaluation [3.1508266388327324]
  This tutorial introduces a reproducible workflow for building high-fidelity digital twins (HiFi DTs) to generate realistic synthetic datasets.<n>We outline practical steps for modeling static geometry, road infrastructure, and dynamic traffic using open-source resources such as satellite imagery, OpenStreetMap, and sensor specifications.<n>The resulting environments support scalable and cost-effective data generation for robust Sim2Real learning.
  arXiv  Detail & Related papers  (2025-09-03T00:12:15Z)
• How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection [0.0]
  Event cameras are well-suited for real-time object detection at traffic intersections.<n>The development of robust event-based detection models is hindered by the limited availability of annotated real-world datasets.<n>This study offers the first quantifiable analysis of the sim-to-real gap in event-based object detection using CARLAs DVS.
  arXiv  Detail & Related papers  (2025-06-16T17:27:43Z)
• SensorLM: Learning the Language of Wearable Sensors [50.95988682423808]
  We present SensorLM, a family of sensor-language foundation models that enable wearable sensor data understanding with natural language.<n>We introduce a hierarchical caption generation pipeline designed to capture statistical, structural, and semantic information from sensor data.<n>This approach enabled the curation of the largest sensor-language dataset to date, comprising over 59.7 million hours of data from more than 103,000 people.
  arXiv  Detail & Related papers  (2025-06-10T17:13:09Z)
• Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques [1.0712226955584796]
  Human activity recognition (HAR) is often limited by the scarcity of labeled datasets.<n>Recent work has explored generating virtual inertial measurement unit (IMU) data via cross-modality transfer.
  arXiv  Detail & Related papers  (2025-06-09T10:25:53Z)
• Synth It Like KITTI: Synthetic Data Generation for Object Detection in Driving Scenarios [3.30184292168618]
  We propose a dataset generation pipeline based on the CARLA simulator for 3D object detection on LiDAR point clouds.<n>We are able to train an object detector on the synthetic data and demonstrate strong generalization capabilities to the KITTI dataset.
  arXiv  Detail & Related papers  (2025-02-20T22:27:42Z)
• A Toolkit for Virtual Reality Data Collection [12.660959713097014]
  We present a versatile data collection toolkit designed to facilitate the capturing of extensive VR datasets.<n>Our toolkit seamlessly integrates with any device, either directly via OpenXR or through the use of a virtual device.
  arXiv  Detail & Related papers  (2024-12-23T11:39:26Z)
• Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation [51.20656279478878]
  MATRIX is a multi-agent simulator that automatically generates diverse text-based scenarios.<n>We introduce MATRIX-Gen for controllable and highly realistic data synthesis.<n>On AlpacaEval 2 and Arena-Hard benchmarks, Llama-3-8B-Base, post-trained on datasets synthesized by MATRIX-Gen with just 20K instruction-response pairs, outperforms Meta's Llama-3-8B-Instruct model.
  arXiv  Detail & Related papers  (2024-10-18T08:01:39Z)
• Scaling Wearable Foundation Models [54.93979158708164]
  We investigate the scaling properties of sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM. Our results establish the scaling laws of LSM for tasks such as imputation, extrapolation, both across time and sensor modalities.
  arXiv  Detail & Related papers  (2024-10-17T15:08:21Z)
• RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications [55.24463002889]
  We focus on depth data synthesis and develop a range-aware RGB-D data simulation pipeline (RaSim) In particular, high-fidelity depth data is generated by imitating the imaging principle of real-world sensors. RaSim can be directly applied to real-world scenarios without any finetuning and excel at downstream RGB-D perception tasks.
  arXiv  Detail & Related papers  (2024-04-05T08:52:32Z)
• AgentStudio: A Toolkit for Building General Virtual Agents [57.02375267926862]
  General virtual agents need to handle multimodal observations, master complex action spaces, and self-improve in dynamic, open-domain environments.<n>AgentStudio provides a lightweight, interactive environment with highly generic observation and action spaces.<n>It integrates tools for creating online benchmark tasks, annotating GUI elements, and labeling actions in videos.<n>Based on our environment and tools, we curate an online task suite that benchmarks both GUI interactions and function calling with efficient auto-evaluation.
  arXiv  Detail & Related papers  (2024-03-26T17:54:15Z)
• Augmented Reality based Simulated Data (ARSim) with multi-view consistency for AV perception networks [47.07188762367792]
  We present ARSim, a framework designed to enhance real multi-view image data with 3D synthetic objects of interest. We construct a simplified virtual scene using real data and strategically place 3D synthetic assets within it. The resulting augmented multi-view consistent dataset is used to train a multi-camera perception network for autonomous vehicles.
  arXiv  Detail & Related papers  (2024-03-22T17:49:11Z)
• Learning from synthetic data generated with GRADE [0.6982738885923204]
  We present a framework for generating realistic animated dynamic environments (GRADE) for robotics research. GRADE supports full simulation control, ROS integration, realistic physics, while being in an engine that produces high visual fidelity images and ground truth data. We show that, even training using only synthetic data, can generalize well to real-world images in the same application domain.
  arXiv  Detail & Related papers  (2023-05-07T14:13:04Z)
• Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods [0.7223361655030193]
  This work develops an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment. Three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA.
  arXiv  Detail & Related papers  (2023-04-26T09:29:58Z)
• Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level [1.1999555634662635]
  LiDAR object detection algorithms based on neural networks for autonomous driving require large amounts of data for training, validation, and testing. We show that using simulated data for the training of neural networks leads to a domain shift of training and testing data due to differences in scenes, scenarios, and distributions.
  arXiv  Detail & Related papers  (2023-03-03T12:52:01Z)
• One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers [96.51828911883456]
  Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data. Traditional UDA often assumes that there are abundant unlabeled real-world data samples available during training for the adaptation. We explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization problem, where only one real-world data sample is available.
  arXiv  Detail & Related papers  (2022-12-14T15:54:15Z)
• Learning to Simulate Realistic LiDARs [66.7519667383175]
  We introduce a pipeline for data-driven simulation of a realistic LiDAR sensor. We show that our model can learn to encode realistic effects such as dropped points on transparent surfaces. We use our technique to learn models of two distinct LiDAR sensors and use them to improve simulated LiDAR data accordingly.
  arXiv  Detail & Related papers  (2022-09-22T13:12:54Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)
• Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation [98.51313127382937]
  We focus on the use of labels in the synthetic domain alone. Our approach introduces both a way to learn neural-invariant representations and a theoretically inspired view on how to sample the data from the simulator. We showcase our approach on the bird's-eye-view vehicle segmentation task with multi-sensor data.
  arXiv  Detail & Related papers  (2021-11-15T18:37:43Z)
• IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition [12.91206329972949]
  We introduce IMUTube, an automated processing pipeline to convert videos of human activity into virtual streams of IMU data. These virtual IMU streams represent accelerometry at a wide variety of locations on the human body. We show how the virtually-generated IMU data improves the performance of a variety of models on known HAR datasets.
  arXiv  Detail & Related papers  (2020-05-29T21:50:38Z)

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.