Language and Sketching: An LLM-driven Interactive Multimodal Multitask Robot Navigation Framework
(2311.08244)Abstract
The socially-aware navigation system has evolved to adeptly avoid various obstacles while performing multiple tasks, such as point-to-point navigation, human-following, and -guiding. However, a prominent gap persists: in Human-Robot Interaction (HRI), the procedure of communicating commands to robots demands intricate mathematical formulations. Furthermore, the transition between tasks does not quite possess the intuitive control and user-centric interactivity that one would desire. In this work, we propose an LLM-driven interactive multimodal multitask robot navigation framework, termed LIM2N, to solve the above new challenge in the navigation field. We achieve this by first introducing a multimodal interaction framework where language and hand-drawn inputs can serve as navigation constraints and control objectives. Next, a reinforcement learning agent is built to handle multiple tasks with the received information. Crucially, LIM2N creates smooth cooperation among the reasoning of multimodal input, multitask planning, and adaptation and processing of the intelligent sensing modules in the complicated system. Extensive experiments are conducted in both simulation and the real world demonstrating that LIM2N has superior user needs understanding, alongside an enhanced interactive experience.
Overview
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A new robot navigation framework, LIM2N, simplifies communication with robots using natural language and sketch inputs.
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LIM2N utilizes LLMs to interpret multimodal inputs, enhancing a robot's understanding of complex instructions and environment.
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The framework combines LLMs and sensor data to navigate effectively, using Reinforcement Learning to execute tasks.
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Experiments in both simulated and real-world conditions show LIM2N improves user interaction and navigation performance compared to traditional methods.
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Future development includes incorporating real-time human feedback and managing inputs from multiple users to further improve the interaction.
Introduction to Multimodal Robot Navigation
The paradigm of robot navigation has traditionally rested on the ability of robots to move from point A to B, avoid obstacles, follow, and guide humans within various environments. Traditionally, to communicate with robots, complex commands in the form of codes or mathematical formulations were often required. This has been an impediment to intuitive user-robot interactions. A significant leap forward is presented in a recently developed framework, which aims to simplify this communication through natural language and simple sketch inputs.
LLM-Driven Interactive Framework
This innovative framework, LLM-driven Interactive Multimodal Multitask Robot Navigation Framework (LIM2N), leverages the power of LLMs to process and reason about multimodal user inputs—combining both language and hand-drawn sketches. LIM2N fundamentally enhances the robot's ability to comprehend complex instructions and environmental details through these natural and intuitive input methods.
Functionality and Advancements
The framework utilizes a specialized division to process the user's instructions. It includes an LLM module that interprets textual inputs to define tasks and environmental details. These details are categorized into constraints (like obstacles to avoid) and destination-related information (such as target locations). An intelligent sensing module merges these details with real-time sensor data, like laser scans, to create an environmental understanding for the robot. Additionally, using Reinforcement Learning (RL), the robot is driven to execute the navigation tasks effectively.
Extensive experiments have indicated that not only does LIM2N enhance user experience through its interactive approach, it also exhibits superior performance in understanding user needs and navigation constraints over traditional methods. The studies involved both simulated environments and real-world settings, confirming the framework's potential for navigating domestic and communal spaces with significantly improved user interaction.
Next Steps for Development
Looking ahead, the developers aim to refine LIM2N by incorporating real-time human feedback into the system, which would further adapt the navigation process according to immediate user responses. Another prospective enhancement is managing inputs from multiple users simultaneously, a challenge that presents itself when the robot services several people at once.
Conclusion
The future of robotic navigation looks promising with frameworks like LIM2N. The simplified communication afforded by LIM2N has the potential to make human-robot interaction more natural and accessible for everyday users, translating into broader and more effective use of service robots across various settings. As robots continue to integrate more deeply within human environments, the need for more sophisticated and user-friendly navigation systems becomes imperative. The LIM2N framework represents a significant step in filling that need.
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