Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies

• URL: http://arxiv.org/abs/2411.00781v1
• Date: Wed, 16 Oct 2024 19:29:14 GMT
• Title: Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies
• Authors: Zirui Song, Guangxian Ouyang, Meng Fang, Hongbin Na, Zijing Shi, Zhenhao Chen, Yujie Fu, Zeyu Zhang, Shiyu Jiang, Miao Fang, Ling Chen, Xiuying Chen,
• Abstract summary: We argue that household robots should proactively detect such hazards or anomalies within the home. We leverage foundational models instead of relying on manually labeled data to build simulated environments. We demonstrate that our generated environment outperforms others in terms of task description and scene diversity.
• Score: 26.79399508110069
• License:
• Abstract: Existing household robots have made significant progress in performing routine tasks, such as cleaning floors or delivering objects. However, a key limitation of these robots is their inability to recognize potential problems or dangers in home environments. For example, a child may pick up and ingest medication that has fallen on the floor, posing a serious risk. We argue that household robots should proactively detect such hazards or anomalies within the home, and propose the task of anomaly scenario generation. We leverage foundational models instead of relying on manually labeled data to build simulated environments. Specifically, we introduce a multi-agent brainstorming approach, where agents collaborate and generate diverse scenarios covering household hazards, hygiene management, and child safety. These textual task descriptions are then integrated with designed 3D assets to simulate realistic environments. Within these constructed environments, the robotic agent learns the necessary skills to proactively discover and handle the proposed anomalies through task decomposition, and optimal learning approach selection. We demonstrate that our generated environment outperforms others in terms of task description and scene diversity, ultimately enabling robotic agents to better address potential household hazards.

Related papers

• Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models [81.55156507635286]
  Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions. Current learning methods often struggle with generalization to the long tail of unexpected situations without heavy human supervision. We propose a system, VLM-Predictive Control (VLM-PC), combining two key components that we find to be crucial for eliciting on-the-fly, adaptive behavior selection.
  arXiv  Detail & Related papers  (2024-07-02T21:00:30Z)
• Contextual Affordances for Safe Exploration in Robotic Scenarios [1.7647943747248804]
  This paper explores the use of contextual affordances to enable safe exploration and learning in robotic scenarios targeted at the home. We propose a simple state representation that allows us to extend contextual affordances to larger state spaces. In the long term, this work could be the foundation for future explorations of human-robot interactions in complex domestic environments.
  arXiv  Detail & Related papers  (2024-05-10T12:12:38Z)
• "Don't forget to put the milk back!" Dataset for Enabling Embodied Agents to Detect Anomalous Situations [49.66220439673356]
  We have created a new dataset, which we call SafetyDetect. The SafetyDetect dataset consists of 1000 anomalous home scenes. Our approach utilizes large language models (LLMs) alongside both a graph representation of the scene and the relationships between the objects in the scene.
  arXiv  Detail & Related papers  (2024-04-12T21:56:21Z)
• RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
  RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
  arXiv  Detail & Related papers  (2023-11-02T17:59:21Z)
• Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN [77.0623472106488]
  In this paper, we explore a class of distributional instance segmentation models using latent codes. For robotic picking applications, we propose a confidence mask method to achieve the high precision necessary. We show that our method can significantly reduce critical errors in robotic systems, including our newly released dataset of ambiguous scenes.
  arXiv  Detail & Related papers  (2023-05-03T05:57:29Z)
• Robot Active Neural Sensing and Planning in Unknown Cluttered Environments [0.0]
  Active sensing and planning in unknown, cluttered environments is an open challenge for robots intending to provide home service, search and rescue, narrow-passage inspection, and medical assistance. We present the active neural sensing approach that generates the kinematically feasible viewpoint sequences for the robot manipulator with an in-hand camera to gather the minimum number of observations needed to reconstruct the underlying environment. Our framework actively collects the visual RGBD observations, aggregates them into scene representation, and performs object shape inference to avoid unnecessary robot interactions with the environment.
  arXiv  Detail & Related papers  (2022-08-23T16:56:54Z)
• Dual-Arm Adversarial Robot Learning [0.6091702876917281]
  We propose dual-arm settings as platforms for robot learning. We will discuss the potential benefits of this setup as well as the challenges and research directions that can be pursued.
  arXiv  Detail & Related papers  (2021-10-15T12:51:57Z)
• Lifelong Robotic Reinforcement Learning by Retaining Experiences [61.79346922421323]
  Many multi-task reinforcement learning efforts assume the robot can collect data from all tasks at all times. In this work, we study a practical sequential multi-task RL problem motivated by the practical constraints of physical robotic systems. We derive an approach that effectively leverages the data and policies learned for previous tasks to cumulatively grow the robot's skill-set.
  arXiv  Detail & Related papers  (2021-09-19T18:00:51Z)
• SAPIEN: A SimulAted Part-based Interactive ENvironment [77.4739790629284]
  SAPIEN is a realistic and physics-rich simulated environment that hosts a large-scale set for articulated objects. We evaluate state-of-the-art vision algorithms for part detection and motion attribute recognition as well as demonstrate robotic interaction tasks.
  arXiv  Detail & Related papers  (2020-03-19T00:11:34Z)
• Autonomous Planning Based on Spatial Concepts to Tidy Up Home Environments with Service Robots [5.739787445246959]
  We propose a novel planning method that can efficiently estimate the order and positions of the objects to be tidied up by learning the parameters of a probabilistic generative model. The model allows a robot to learn the distributions of the co-occurrence probability of the objects and places to tidy up using the multimodal sensor information collected in a tidied environment. We evaluate the effectiveness of the proposed method by an experimental simulation that reproduces the conditions of the Tidy Up Here task of the World Robot Summit 2018 international robotics competition.
  arXiv  Detail & Related papers  (2020-02-10T11:49:58Z)

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.