תובנה - Robotics assistive technology - # LLM-based speech interface for physically assistive robots

Harnessing Large Language Models as Customizable Speech Interfaces for Physically Assistive Robots

Q: How can LLM-based speech interfaces be designed to better understand and execute on user-provided modifiers to customize the robot's actions?

To enhance the understanding and execution of user-provided modifiers by LLM-based speech interfaces, several design considerations can be implemented: Explicit Training Data: Training the LLM on a diverse dataset that includes a wide range of modifiers and their corresponding actions can help the model better understand the context in which modifiers are used. Contextual Understanding: Incorporating contextual information into the prompt given to the LLM can help provide additional cues for the model to interpret modifiers correctly. For example, providing information about the current state of the robot or the environment can aid in understanding the intended action. Feedback Mechanism: Implementing a feedback mechanism where the system confirms the interpretation of the modifier with the user can help in real-time correction and improvement of the model's understanding. Adaptive Learning: Continuously updating the LLM based on user interactions and feedback can improve its ability to understand and execute on user-provided modifiers over time. Natural Language Processing Techniques: Leveraging advanced NLP techniques such as semantic parsing and entity recognition can assist in extracting and interpreting modifiers more accurately. By incorporating these design strategies, LLM-based speech interfaces can better understand and execute on user-provided modifiers, leading to a more personalized and efficient interaction with the robot.

Q: How can LLM-based speech interfaces for physically assistive robots be designed to foster a more social and engaging interaction, similar to that of a human caregiver?

To create a more social and engaging interaction with physically assistive robots using LLM-based speech interfaces, the following design approaches can be implemented: Conversational Abilities: Enhance the LLM's conversational capabilities to enable more natural and engaging interactions. This can involve incorporating dialogue management systems to maintain context and continuity in conversations. Empathetic Responses: Program the LLM to provide empathetic responses to users, showing understanding and emotional support during interactions. This can help create a more human-like interaction experience. Personalization: Customize the LLM's responses and behavior based on individual user preferences and needs. Tailoring the interaction to each user can foster a stronger connection and engagement. Interactive Feedback: Implement interactive feedback mechanisms where the robot responds to user input with gestures, expressions, or vocal cues. This can make the interaction more dynamic and engaging. Social Cues: Incorporate social cues such as eye contact, body language, and turn-taking into the robot's behavior to mimic human social interactions. This can create a more natural and intuitive interaction experience. By integrating these design principles, LLM-based speech interfaces for physically assistive robots can foster a more social and engaging interaction, resembling the interaction dynamics of a human caregiver and enhancing the overall user experience.

מושגי ליבה

Integrating large language models (LLMs) as speech interfaces for physically assistive robots can enable users to naturally provide high-level commands and customized preferences, but requires careful design considerations to ensure a positive user experience.

תקציר

The paper presents an iteratively constructed framework for integrating LLMs as speech interfaces for physically assistive robots. The framework was developed and refined through multiple stages of testing, culminating in a user study with 11 older adults at an independent living facility.

The initial version of the framework identified 5 key components: Environment Description, Robot Functions, Function Applications, Code Specifications, and Safety. Through pilot testing with lab members and a demonstration with community members, the framework was expanded to include additional components such as Robot Variables, Instructional Materials, User Control Functions, and Feedback.

The final user study with older adults validated the effectiveness of the framework. Participants found the speech interface easy to learn and use, with low reported workload. However, challenges were identified around consistently executing user-provided modifiers (e.g. "feed me a larger scoop") and processing non-predefined commands (e.g. mixing foods).

The paper concludes by presenting 5 design guidelines based on the user study findings: 1) Customization, 2) Multi-Step Instruction, 3) Consistency, 4) Comparable Time to Caregiver, and 5) Social Capability. These guidelines highlight the importance of human-centric considerations when integrating LLMs as assistive speech interfaces, beyond just prompt engineering.

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כתוב מחדש עם AI

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מתוכן המקור

עבור למקור

arxiv.org

סטטיסטיקה

"I would imagine that most people would learn to use the system quickly."
"What I liked was it gave me a sense of control. For somebody who's in an incapacitated situation, that would be very important... And it was easy to talk to."
"Trying to get the amounts is a challenge"
"Maybe with the mixing of the food together... [it] didn't seem to know what mix meant."

ציטוטים

"I would imagine that most people would learn to use the system quickly."
"What I liked was it gave me a sense of control. For somebody who's in an incapacitated situation, that would be very important... And it was easy to talk to."
"Trying to get the amounts is a challenge"
"Maybe with the mixing of the food together... [it] didn't seem to know what mix meant."

תובנות מפתח מזוקקות מ:

VoicePilot

by Akhil Padman... ב- arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.04066.pdf

שאלות מעמיקות

How can LLM-based speech interfaces be designed to better understand and execute on user-provided modifiers to customize the robot's actions?

To enhance the understanding and execution of user-provided modifiers by LLM-based speech interfaces, several design considerations can be implemented:

Explicit Training Data: Training the LLM on a diverse dataset that includes a wide range of modifiers and their corresponding actions can help the model better understand the context in which modifiers are used.

Contextual Understanding: Incorporating contextual information into the prompt given to the LLM can help provide additional cues for the model to interpret modifiers correctly. For example, providing information about the current state of the robot or the environment can aid in understanding the intended action.

Feedback Mechanism: Implementing a feedback mechanism where the system confirms the interpretation of the modifier with the user can help in real-time correction and improvement of the model's understanding.

Adaptive Learning: Continuously updating the LLM based on user interactions and feedback can improve its ability to understand and execute on user-provided modifiers over time.

Natural Language Processing Techniques: Leveraging advanced NLP techniques such as semantic parsing and entity recognition can assist in extracting and interpreting modifiers more accurately.

By incorporating these design strategies, LLM-based speech interfaces can better understand and execute on user-provided modifiers, leading to a more personalized and efficient interaction with the robot.

How can LLM-based speech interfaces for physically assistive robots be designed to foster a more social and engaging interaction, similar to that of a human caregiver?

To create a more social and engaging interaction with physically assistive robots using LLM-based speech interfaces, the following design approaches can be implemented:

Conversational Abilities: Enhance the LLM's conversational capabilities to enable more natural and engaging interactions. This can involve incorporating dialogue management systems to maintain context and continuity in conversations.

Empathetic Responses: Program the LLM to provide empathetic responses to users, showing understanding and emotional support during interactions. This can help create a more human-like interaction experience.

Personalization: Customize the LLM's responses and behavior based on individual user preferences and needs. Tailoring the interaction to each user can foster a stronger connection and engagement.

Interactive Feedback: Implement interactive feedback mechanisms where the robot responds to user input with gestures, expressions, or vocal cues. This can make the interaction more dynamic and engaging.

Social Cues: Incorporate social cues such as eye contact, body language, and turn-taking into the robot's behavior to mimic human social interactions. This can create a more natural and intuitive interaction experience.

By integrating these design principles, LLM-based speech interfaces for physically assistive robots can foster a more social and engaging interaction, resembling the interaction dynamics of a human caregiver and enhancing the overall user experience.