Abstract
Modern tools for class-agnostic image segmentation (e.g., SegmentAnything) and open-set semantic understanding (e.g., CLIP) provide unprecedented opportunities for robot perception and mapping. While traditional closed-set metric-semantic maps were restricted to tens or hundreds of semantic classes, we can now build maps with a plethora of objects and countless semantic variations. This leaves us with a fundamental question: what is the right granularity for the objects (and, more generally, for the semantic concepts) the robot has to include in its map representation? While related work implicitly chooses a level of granularity by tuning thresholds for object detection, we argue that such a choice is intrinsically task-dependent. The first contribution of this paper is to propose a task-driven 3D scene understanding problem, where the robot is given a list of tasks in natural language and has to select the granularity and the subset of objects and scene structure to retain in its map that is sufficient to complete the tasks. We show that this problem can be naturally formulated using the Information Bottleneck (IB), an established information-theoretic framework. The second contribution is an algorithm for task-driven 3D scene understanding based on an Agglomerative IB approach, that is able to cluster 3D primitives in the environment into task-relevant objects and regions and executes incrementally. The third contribution is to integrate our task-driven clustering algorithm into a real-time pipeline, named Clio, that constructs a hierarchical 3D scene graph of the environment online using only onboard compute, as the robot explores it. Our final contribution is an extensive experimental campaign showing that Clio not only allows real-time construction of compact open-set 3D scene graphs, but also improves the accuracy of task execution by limiting the map to relevant semantic concepts.
Overview
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The paper presents a novel approach to 3D scene understanding for robotics, focusing on optimizing map representations for specific tasks using the Information Bottleneck principle.
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It introduces Clio, a system that constructs real-time 3D scene graphs while minimizing computational load and focusing on task-relevant information.
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The study evaluates Clio in various real-world environments, demonstrating its effectiveness and efficiency in generating task-driven 3D scene representations.
Enhanced Task-Driven 3D Scene Understanding for Robotics Using the Information Bottleneck Principle
Introduction
This blog post discusses recent developments in the field of task-dependent 3D scene understanding for robotics, as detailed in a comprehensive study. This research addresses the challenge of how robots should represent their observations when tasked with specific goals, proposing a task-driven approach to generate minimalist yet sufficient map representations using the Information Bottleneck (IB) principle.
Problem Formulation
The paper introduces the problem of task-driven 3D scene understanding, where a robot is given a list of tasks, described in natural language, and must optimize its map to only include objects and features relevant to these tasks. This is articulated as an optimization problem using the Information Bottleneck framework, aiming to compress raw sensory data into a semantically meaningful representation that is most informative about the tasks at hand.
Methodology
- Task-Driven Clustering: Leveraging the Agglomerative Information Bottleneck method, the research proposes an algorithm for clustering 3D object primitives and regions according to task relevance. The contribution here is twofold: a formulation of the problem that explicitly considers task relevance, and an algorithmic solution that can be incrementally executed as the environment is explored.
- Real-Time Integration: The developed algorithm is encapsulated into a system named Clio, which constructs a real-time 3D scene graph while the robot navigates its environment. Clio operates onboard with only necessary computations, contrasting with other methods that require more substantial off-board processing.
Implementation and Evaluation
The paper details an extensive experimental setup, testing the system in diverse real-world environments. It provides a quantitative evaluation where Clio outperforms existing methods in terms of real-time operation and task-relevance of the constructed scene graphs. The metrics used include object and region detection accuracy related to specified tasks, along with the computational performance of the system.
- Incremental Agglomerative Information Bottleneck: This technique forms the core of the task-driven clustering in Clio, allowing for an efficient and scalable update of the scene representation as new data is received.
- Handling Large and Diverse Environments: The approach is tested in various settings, from small offices to large buildings, showcasing its adaptability and robustness.
Discussion and Implications
The incorporation of the Information Bottleneck principle in a task-driven robotic perception model introduces several theoretical and practical impacts:
- Reduction in Redundant Information: By focusing on task-relevant information, the system minimizes the computational load, which is critical for real-time applications in robotics.
- Scalability and Flexibility: The technique is not bound by a predefined set of object classes or environments, making it suitable for general applications in robotic navigation and interaction.
Future Directions
Potential future research directions might include exploring more complex task descriptions, integrating more advanced natural language processing techniques to handle multi-step or higher-level tasks, and improving the robustness of the system against varying environmental conditions and sensor noise.
Clio represents a significant step forward in task-driven robotic mapping, offering a practical solution adapted to the evolving capabilities and roles of autonomous systems in varied operational contexts.
The open-source release of Clio, along with datasets used for testing, further contributes to advancements in the field by allowing researchers to implement, test, and build upon the proposed framework.
Conclusion
This study contributes to the field of robotics by proposing a novel, task-driven methodology to 3D scene understanding that optimizes the relevance and efficiency of environmental representations, enabling more intelligent robotic autonomy in real-world applications.