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insight - Robotics object search - # Commonsense scene graph-based target localization

Commonsense Scene Graph-based Target Localization for Efficient Object Search in Household Environments


Core Concepts
Integrating room-level spatial knowledge from pre-built maps and object-level commonsense knowledge from large language models into a commonsense scene graph (CSG) enables superior target localization and efficient object search for household robots.
Abstract

The paper introduces a commonsense scene graph-based target localization (CSG-TL) method that combines room-level spatial knowledge from pre-built maps and object-level commonsense knowledge from large language models (LLMs) to enhance the accuracy of target localization.

The key steps are:

  1. Constructing a commonsense scene graph (CSG) that integrates the room-level layout of stationary items and object-level commonsense knowledge obtained through LLM prompts.
  2. Developing the CSG-TL model that leverages the CSG structure to predict the likelihood of correlation between the target object and other objects, enabling efficient target localization.
  3. Incorporating the CSG-TL into a commonsense scene graph-based object search (CSG-OS) framework, which uses the target localization results to guide the robot's search strategy.

The proposed approach is evaluated on the ScanNet dataset and the AI2THOR simulator, demonstrating superior performance compared to existing methods that rely solely on statistical correlations or partial scene knowledge. The CSG-OS framework is also successfully deployed on a real-world Jackal robot, showcasing its practical applicability.

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Stats
The paper reports the following key statistics: Link prediction accuracy on the ScanNet dataset: CSG-TL achieves 89.73%, outperforming statistical methods (27.56%) and graph-based methods without commonsense (80.03%). Link prediction accuracy on the AI2THOR single-room environment: CSG-TL achieves 81.09%, compared to statistical methods (47.32%) and graph-based methods without commonsense (73.11%). Link prediction accuracy on the AI2THOR multi-room environment: CSG-TL achieves 78.21%, compared to statistical methods (31.77%) and graph-based methods without commonsense (65.21%).
Quotes
"To efficiently locate the target object, the robot needs to be equipped with knowledge at both the object and room level. However, existing approaches rely solely on one type of knowledge, leading to unsatisfactory object localization performance and, consequently, inefficient object search processes." "To leverage LLMs' strengths and address existing works' limitations, we propose a novel commonsense scene graph-based target localization method, CSG-TL. In contrast to previous works dependent on statistical correlations without object-level commonsense or struggles with capturing room-level object correlations due to limited viewpoints, our model captures both the room-level spatial layouts from pre-built maps and object-level commonsense knowledge obtained from LLMs."

Key Insights Distilled From

by Wenqi Ge,Cha... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00343.pdf
Commonsense Scene Graph-based Target Localization for Object Search

Deeper Inquiries

How can the proposed CSG-TL and CSG-OS frameworks be extended to handle more complex and dynamic household environments, such as those with movable furniture or frequently rearranged objects

To extend the CSG-TL and CSG-OS frameworks to handle more complex and dynamic household environments, such as those with movable furniture or frequently rearranged objects, several enhancements can be implemented. Dynamic Scene Updating: Incorporate a mechanism to dynamically update the scene graph (CSG) as the environment changes. This can involve real-time object detection and tracking to adjust the graph's structure based on the movement of objects. Object Interaction Modeling: Introduce interactions between objects in the scene graph to capture how movable furniture or rearranged objects affect the correlations with the target. This can help in understanding the context of the environment better. Temporal Context: Include temporal information in the scene graph to track changes over time. By considering the history of object placements and movements, the system can adapt to evolving environments. Sensor Fusion: Integrate data from multiple sensors like cameras, LiDAR, or depth sensors to enhance perception capabilities. This multi-modal approach can provide a more comprehensive understanding of the environment. Adaptive Learning: Implement adaptive learning algorithms that can update the model based on new data and environmental changes. This continuous learning process can improve the system's adaptability to dynamic environments.

What other types of commonsense knowledge, beyond the location and usage information used in this work, could be incorporated into the CSG to further enhance the target localization and object search capabilities

Incorporating additional types of commonsense knowledge beyond location and usage information can further enhance the target localization and object search capabilities of the CSG. Some potential types of commonsense knowledge to consider include: Object Affordances: Understanding the potential actions or uses associated with objects can help in predicting their likely locations. For example, knowing that a cup is often found near a water source can aid in localization. Semantic Relationships: Incorporating semantic relationships between objects can provide insights into their co-occurrences. For instance, understanding that a TV remote is commonly found near a television can improve search accuracy. User Preferences: Considering user preferences or habits in object placements can guide the search process. For instance, if a user typically places keys on a key holder, this knowledge can assist in target localization. Spatial Constraints: Factoring in spatial constraints like object size, weight, or mobility can refine the search process. This information can help in predicting where objects are likely to be placed based on physical limitations. Contextual Cues: Leveraging contextual cues from the environment, such as lighting conditions, time of day, or room temperature, can provide additional context for object localization and search tasks. By integrating these diverse forms of commonsense knowledge into the CSG, the system can achieve a more nuanced understanding of the environment, leading to improved target localization and object search outcomes.

How could the CSG-TL and CSG-OS approaches be adapted to handle more abstract or ambiguous target descriptions provided by users, beyond just object categories

Adapting the CSG-TL and CSG-OS approaches to handle more abstract or ambiguous target descriptions provided by users involves several strategies to enhance the system's interpretive capabilities: Natural Language Processing: Integrate natural language processing techniques to parse and interpret user descriptions more effectively. This can involve sentiment analysis, entity recognition, and context understanding to extract relevant information from user inputs. Contextual Inference: Develop algorithms that can infer context from ambiguous descriptions by considering the overall scene context, user history, and common patterns. This contextual inference can help in narrowing down search areas based on vague descriptions. Probabilistic Reasoning: Implement probabilistic reasoning models to handle uncertainty in ambiguous descriptions. By assigning probabilities to different interpretations of user inputs, the system can make informed decisions during target localization and object search. Interactive Feedback: Incorporate mechanisms for interactive feedback where the system can seek clarification from users when faced with ambiguous descriptions. This feedback loop can improve the system's understanding over time. Multi-Modal Fusion: Combine information from multiple modalities, such as text, images, and sensor data, to enrich the understanding of user descriptions. This multi-modal fusion can provide a more comprehensive view of the target and aid in localization. By incorporating these strategies, the CSG-TL and CSG-OS frameworks can adapt to handle a wider range of target descriptions, even those that are abstract or ambiguous, enhancing their overall effectiveness in object search tasks.
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