LLM Granularity for On-the-Fly Robot Control

Peng Wang, Mattia Robbiani, Zhihao Guo·June 20, 2024

Summary

This paper investigates the potential of large language models (LLMs) for real-time control of assistive robots, transitioning from visuolinguomotor to linguomotor mode. The study, conducted with a Sawyer cobot and Turtlebot, evaluates the impact of language prompts with varying levels of granularity on robot performance. Qualitative prompts lead to cautious movements for safety but lack precision, while quantitative prompts enable more accurate positioning. Results show that LLMs struggle with unquantified commands, particularly for mobile robots. The research highlights the need for model fine-tuning and the importance of quantifying language for better control. The study provides initial insights into the feasibility of on-the-fly language-based control for assistive robotics, with future work focusing on improving precision and handling invalid actions. The demonstrations showcase the potential for more natural human-robot interaction through language-based control.

Key findings

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Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of controlling assistive robots on-the-fly using language in dynamic and unpredictable environments . This problem is not entirely new but is gaining significance due to the need for real-time adaptation and efficient human-robot interaction in assistive settings . The paper explores the necessity and feasibility of on-the-fly robot control solely through language prompts, evaluating responses to language instructions of varying granularities to enhance the quality of life for vulnerable individuals like the elderly .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to the feasibility of relying solely on language for robotic control in various scenarios on-the-fly. It aims to evaluate robot responses to human language commands of different granularities in assistive robot contexts, specifically focusing on the viability of the 'linguomotor' mode for assistive robots .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "LLM Granularity for On-the-Fly Robot Control" introduces innovative concepts and approaches in the field of assistive robotics by leveraging Large Language Models (LLMs) for real-time language control of robots . One key idea proposed in the paper is the utilization of LLMs to enable on-the-fly control of robots in dynamic and unpredictable environments, where traditional pre-programmed or learned commands may fall short . This approach emphasizes the importance of real-time language control for effective human-robot interactive assistance, particularly in scenarios requiring swift responses to language instructions to maintain efficient workflow and cooperation .

Furthermore, the paper explores the convergence of computer vision, large language models, and robotics to introduce the 'visuolinguomotor' mode for assistive robots, where visuals and linguistics are integrated to enable proactive and interactive assistance . This novel approach raises the question of whether robots can be solely controlled by language in situations where visual cues are unreliable or unavailable, highlighting the potential of the 'linguomotor' mode for assistive robots . The paper evaluates the responses of assistive robots to language prompts of varying granularities and investigates the necessity and feasibility of controlling robots on-the-fly, showcasing the potential benefits of this approach .

Moreover, the paper discusses the challenges and opportunities associated with using LLMs for assistive robotics, such as the need for fine-tuning models to address invalid actions generated by LLMs and the limitations of current models in generalizing from one case to another . It also highlights the importance of quantifying the success of robot actions to a finer level, emphasizing the need for more nuanced evaluation metrics beyond task completion, such as assessing the quality of outcomes in assistive tasks . Additionally, the paper introduces the concept of Visual Language Models (VLMs) and their applications in assistive tasks, showcasing how VLMs can be used to assist humans in task completion by generating actions based on qualitative prompts .

Overall, the paper presents a comprehensive exploration of the potential of LLMs and VLMs in enhancing the capabilities of assistive robots, emphasizing the significance of real-time language control, nuanced evaluation metrics, and the integration of visuals, language, and robotics for more effective human-robot interaction and assistance . The paper "LLM Granularity for On-the-Fly Robot Control" introduces novel characteristics and advantages compared to previous methods in the field of assistive robotics by leveraging Large Language Models (LLMs) for real-time language control of robots . One key characteristic is the emphasis on on-the-fly control of robots in dynamic and unpredictable environments, where traditional pre-programmed or learned commands may fall short, highlighting the necessity of real-time language control for adaptation . This approach enables swift responses to language instructions, essential for maintaining efficient workflow and cooperation in human-robot interactive assistance scenarios .

Moreover, the paper explores the integration of Visual Language Models (VLMs) in assistive tasks, showcasing the application of VLM-based planners to assist humans in task completion by generating actions based on qualitative prompts . This integration of visuals and language in the 'visuolinguomotor' mode for assistive robots presents a proactive and interactive approach to assistance, addressing the limitations of solely relying on visuals or language for robot control .

Additionally, the paper addresses the challenges associated with using LLMs for assistive robotics, such as the need for fine-tuning models to rectify invalid actions generated by LLMs and the limitations in generalizing from one case to another . It also highlights the importance of quantifying the success of robot actions to a finer level, beyond task completion, by evaluating the quality of outcomes in assistive tasks . This nuanced evaluation approach enhances the effectiveness of assistive robots in providing accurate and satisfactory assistance to users .

Furthermore, the paper discusses the necessity of controlling assistive robots on-the-fly using language in dynamic and unpredictable environments, where real-time language control becomes crucial for effective adaptation and human-robot interaction . By enabling on-the-fly control of robots through language, the paper addresses the unpredictability in tasks and the need for swift responses to language instructions to ensure efficient workflow and cooperation in assistive scenarios . This approach offers a promising trend in forming robot policies for spontaneous tasks, emphasizing the significance of on-the-fly language control in dealing with unpredictability .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of controlling assistive robots on-the-fly using language. Noteworthy researchers in this area include Z. Shi, E. Landrum, A. O’Connell, M. Kian, L. Pinto-Alva, K. Shrestha, X. Zhu, and M. J. Matari´c . The key to the solution mentioned in the paper involves utilizing Language and Visual Language Models (LLMs and VLMs) to enable real-time language control of robots for adaptive and efficient human-robot interaction . The research emphasizes the importance of on-the-fly language control in dynamic and unpredictable environments to ensure effective human-robot cooperation and workflow efficiency .


How were the experiments in the paper designed?

The experiments in the paper were designed using two sets of experiments involving a Sawyer cobot arm and a TurtleBot mobile robot . The experiments utilized ROS noetic and Ubuntu 20.04 LTS for robot control and the Groq API with the LLAMA3 70B LLM model for language inference . The experiments aimed to evaluate the responses of assistive robots to language prompts of varying granularities and explore the necessity and feasibility of controlling the robot on-the-fly . The Sawyer cobot experiment focused on qualitative and quantitative control of the robots, demonstrating that quantitative prompts led to more accurate control compared to qualitative prompts . The experiments also highlighted the importance of adjusting joint positions accordingly based on language prompts to achieve desired robot movements .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is based on experiments conducted on a Sawyer cobot and a Turtlebot for assessing the responses of assistive robots to language prompts of varying granularities . The code for the experiments will be released on GitHub soon to benefit the community, indicating that the code is open source .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The study evaluates the responses of assistive robots to language prompts of varying granularities and explores the necessity and feasibility of controlling the robot on-the-fly . The experiments conducted on a Sawyer cobot and a Turtlebot demonstrate the adaptation of the solution to scenarios where assistive robots need to maneuver to assist, showcasing foundational insights into relying solely on language for robotic control in various scenarios on-the-fly . The experiments cover a range of prompts from qualitative to quantitative, showing that quantitative prompts can achieve more accurate control of both robots, emphasizing the importance of precise language commands for effective robot control . Additionally, the study highlights the challenges and benefits of on-the-fly language control for robots, especially in dynamic and unpredictable environments where real-time language control is essential for adaptation and efficient human-robot interaction . The results indicate that while qualitative prompts may lead to errors and safety concerns, quantitative prompts enable more accurate and successful robot actions, supporting the hypothesis that language granularity significantly affects robot control performance . Overall, the experiments and results provide valuable insights into the viability and effectiveness of controlling assistive robots using language, contributing to the advancement of on-the-fly robot control in various assistive scenarios .


What are the contributions of this paper?

The paper "LLM Granularity for On-the-Fly Robot Control" makes several contributions in the field of assistive robotics and human-robot interaction:

  • The paper evaluates the responses of assistive robots to language prompts of varying granularities, exploring the necessity and feasibility of controlling the robot on-the-fly .
  • It investigates the importance of on-the-fly language control for effective human-robot interactive assistance, especially in scenarios where long-horizon interactions are required .
  • The research focuses on the convergence of computer vision, large language models (LLMs), and robotics to introduce the 'visuolinguomotor' mode for assistive robots, enabling proactive and interactive assistance .
  • It addresses the challenges of using qualitative prompts in assistive robotics, emphasizing the need for real-time language corrections and the limitations of current models in generalizing from one case to another .
  • The paper highlights the significance of controlling assistive robots on-the-fly using language, particularly in dynamic and unpredictable environments, where forming robot policies using LLMs is crucial for spontaneous tasks .
  • It presents experiments conducted on a Sawyer cobot to demonstrate the impact of language granularity on robot control performance, ranging from qualitative to quantitative prompts, to enhance safety and efficiency in assistance .

What work can be continued in depth?

To further advance the research in the field of controlling assistive robots on-the-fly using language, several areas can be explored in depth based on the existing work:

  1. Fine-Tuning Models for Improved Performance: Future studies can focus on fine-tuning Large Language Models (LLMs) to enhance their ability to interpret and respond to language prompts accurately, especially in scenarios where qualitative prompts may lead to errors or ambiguities .

  2. Addressing Safety Concerns: Research can delve into developing strategies to ensure the safety of assistive robots when controlled through language commands, particularly when dealing with qualitative prompts that may lack specificity, potentially leading to unsafe actions .

  3. Generalization and Scalability: Further investigations can be conducted to improve the generalization capabilities of models used for on-the-fly robot control, aiming to enhance their scalability and adaptability across different scenarios and robot platforms .

  4. Real-Time Language Corrections: Building on existing approaches that involve real-time language corrections for robotic manipulation, future work can focus on refining these correction mechanisms to enable more seamless human-robot interactions and task executions .

  5. Quantifying Language Prompts: Research efforts can explore methods to quantify qualitative language prompts effectively to provide robots with clearer and more precise instructions, ultimately improving the efficiency and success rates of assistive tasks .

By delving deeper into these areas, researchers can contribute to the advancement of on-the-fly language control for assistive robots, addressing challenges related to safety, performance, adaptability, and interaction quality in various assistive scenarios.


Introduction
Background
Emergence of large language models in robotics
Current limitations of visuolinguomotor control
Objective
To explore the potential of LLMs for real-time control transition
Assess impact of language prompts on robot performance
Method
Data Collection
Experimental Setup
Sawyer cobot and Turtlebot as test subjects
Language prompts with varying levels of granularity
Data Collection Protocol
Interaction with quantitative and qualitative prompts
Data Preprocessing
Analysis of robot performance metrics
Quantification of language commands for precision evaluation
Results and Evaluation
Quantitative vs. Qualitative Prompts
Cautiousness vs. accuracy in robot movements
Challenges with unquantified commands for mobile robots
Model Performance Analysis
Struggles with language interpretation
Effectiveness of fine-tuning on control accuracy
Implications and Limitations
Importance of quantifying language for improved control
Handling invalid actions and precision improvements
Future Directions
Enhancing model precision through fine-tuning
Research on natural human-robot interaction with language control
Demonstrations and Case Studies
Showcasing potential for more intuitive interaction
Conclusion
Initial feasibility of on-the-fly language-based control
Significance for assistive robotics and human-robot collaboration
Basic info
papers
robotics
artificial intelligence
Advanced features
Insights
What are the future directions for improving language-based control in assistive robotics, as discussed in the paper?
How do qualitative language prompts affect the robot's movement according to the paper?
What is the main challenge LLMs face with unquantified commands, as mentioned in the research?
What type of robots were used in the study for evaluating language-based control?

LLM Granularity for On-the-Fly Robot Control

Peng Wang, Mattia Robbiani, Zhihao Guo·June 20, 2024

Summary

This paper investigates the potential of large language models (LLMs) for real-time control of assistive robots, transitioning from visuolinguomotor to linguomotor mode. The study, conducted with a Sawyer cobot and Turtlebot, evaluates the impact of language prompts with varying levels of granularity on robot performance. Qualitative prompts lead to cautious movements for safety but lack precision, while quantitative prompts enable more accurate positioning. Results show that LLMs struggle with unquantified commands, particularly for mobile robots. The research highlights the need for model fine-tuning and the importance of quantifying language for better control. The study provides initial insights into the feasibility of on-the-fly language-based control for assistive robotics, with future work focusing on improving precision and handling invalid actions. The demonstrations showcase the potential for more natural human-robot interaction through language-based control.
Mind map
Interaction with quantitative and qualitative prompts
Language prompts with varying levels of granularity
Sawyer cobot and Turtlebot as test subjects
Showcasing potential for more intuitive interaction
Effectiveness of fine-tuning on control accuracy
Struggles with language interpretation
Challenges with unquantified commands for mobile robots
Cautiousness vs. accuracy in robot movements
Quantification of language commands for precision evaluation
Analysis of robot performance metrics
Data Collection Protocol
Experimental Setup
Assess impact of language prompts on robot performance
To explore the potential of LLMs for real-time control transition
Current limitations of visuolinguomotor control
Emergence of large language models in robotics
Significance for assistive robotics and human-robot collaboration
Initial feasibility of on-the-fly language-based control
Demonstrations and Case Studies
Handling invalid actions and precision improvements
Importance of quantifying language for improved control
Model Performance Analysis
Quantitative vs. Qualitative Prompts
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Future Directions
Implications and Limitations
Results and Evaluation
Method
Introduction
Outline
Introduction
Background
Emergence of large language models in robotics
Current limitations of visuolinguomotor control
Objective
To explore the potential of LLMs for real-time control transition
Assess impact of language prompts on robot performance
Method
Data Collection
Experimental Setup
Sawyer cobot and Turtlebot as test subjects
Language prompts with varying levels of granularity
Data Collection Protocol
Interaction with quantitative and qualitative prompts
Data Preprocessing
Analysis of robot performance metrics
Quantification of language commands for precision evaluation
Results and Evaluation
Quantitative vs. Qualitative Prompts
Cautiousness vs. accuracy in robot movements
Challenges with unquantified commands for mobile robots
Model Performance Analysis
Struggles with language interpretation
Effectiveness of fine-tuning on control accuracy
Implications and Limitations
Importance of quantifying language for improved control
Handling invalid actions and precision improvements
Future Directions
Enhancing model precision through fine-tuning
Research on natural human-robot interaction with language control
Demonstrations and Case Studies
Showcasing potential for more intuitive interaction
Conclusion
Initial feasibility of on-the-fly language-based control
Significance for assistive robotics and human-robot collaboration
Key findings
5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of controlling assistive robots on-the-fly using language in dynamic and unpredictable environments . This problem is not entirely new but is gaining significance due to the need for real-time adaptation and efficient human-robot interaction in assistive settings . The paper explores the necessity and feasibility of on-the-fly robot control solely through language prompts, evaluating responses to language instructions of varying granularities to enhance the quality of life for vulnerable individuals like the elderly .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis related to the feasibility of relying solely on language for robotic control in various scenarios on-the-fly. It aims to evaluate robot responses to human language commands of different granularities in assistive robot contexts, specifically focusing on the viability of the 'linguomotor' mode for assistive robots .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "LLM Granularity for On-the-Fly Robot Control" introduces innovative concepts and approaches in the field of assistive robotics by leveraging Large Language Models (LLMs) for real-time language control of robots . One key idea proposed in the paper is the utilization of LLMs to enable on-the-fly control of robots in dynamic and unpredictable environments, where traditional pre-programmed or learned commands may fall short . This approach emphasizes the importance of real-time language control for effective human-robot interactive assistance, particularly in scenarios requiring swift responses to language instructions to maintain efficient workflow and cooperation .

Furthermore, the paper explores the convergence of computer vision, large language models, and robotics to introduce the 'visuolinguomotor' mode for assistive robots, where visuals and linguistics are integrated to enable proactive and interactive assistance . This novel approach raises the question of whether robots can be solely controlled by language in situations where visual cues are unreliable or unavailable, highlighting the potential of the 'linguomotor' mode for assistive robots . The paper evaluates the responses of assistive robots to language prompts of varying granularities and investigates the necessity and feasibility of controlling robots on-the-fly, showcasing the potential benefits of this approach .

Moreover, the paper discusses the challenges and opportunities associated with using LLMs for assistive robotics, such as the need for fine-tuning models to address invalid actions generated by LLMs and the limitations of current models in generalizing from one case to another . It also highlights the importance of quantifying the success of robot actions to a finer level, emphasizing the need for more nuanced evaluation metrics beyond task completion, such as assessing the quality of outcomes in assistive tasks . Additionally, the paper introduces the concept of Visual Language Models (VLMs) and their applications in assistive tasks, showcasing how VLMs can be used to assist humans in task completion by generating actions based on qualitative prompts .

Overall, the paper presents a comprehensive exploration of the potential of LLMs and VLMs in enhancing the capabilities of assistive robots, emphasizing the significance of real-time language control, nuanced evaluation metrics, and the integration of visuals, language, and robotics for more effective human-robot interaction and assistance . The paper "LLM Granularity for On-the-Fly Robot Control" introduces novel characteristics and advantages compared to previous methods in the field of assistive robotics by leveraging Large Language Models (LLMs) for real-time language control of robots . One key characteristic is the emphasis on on-the-fly control of robots in dynamic and unpredictable environments, where traditional pre-programmed or learned commands may fall short, highlighting the necessity of real-time language control for adaptation . This approach enables swift responses to language instructions, essential for maintaining efficient workflow and cooperation in human-robot interactive assistance scenarios .

Moreover, the paper explores the integration of Visual Language Models (VLMs) in assistive tasks, showcasing the application of VLM-based planners to assist humans in task completion by generating actions based on qualitative prompts . This integration of visuals and language in the 'visuolinguomotor' mode for assistive robots presents a proactive and interactive approach to assistance, addressing the limitations of solely relying on visuals or language for robot control .

Additionally, the paper addresses the challenges associated with using LLMs for assistive robotics, such as the need for fine-tuning models to rectify invalid actions generated by LLMs and the limitations in generalizing from one case to another . It also highlights the importance of quantifying the success of robot actions to a finer level, beyond task completion, by evaluating the quality of outcomes in assistive tasks . This nuanced evaluation approach enhances the effectiveness of assistive robots in providing accurate and satisfactory assistance to users .

Furthermore, the paper discusses the necessity of controlling assistive robots on-the-fly using language in dynamic and unpredictable environments, where real-time language control becomes crucial for effective adaptation and human-robot interaction . By enabling on-the-fly control of robots through language, the paper addresses the unpredictability in tasks and the need for swift responses to language instructions to ensure efficient workflow and cooperation in assistive scenarios . This approach offers a promising trend in forming robot policies for spontaneous tasks, emphasizing the significance of on-the-fly language control in dealing with unpredictability .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field of controlling assistive robots on-the-fly using language. Noteworthy researchers in this area include Z. Shi, E. Landrum, A. O’Connell, M. Kian, L. Pinto-Alva, K. Shrestha, X. Zhu, and M. J. Matari´c . The key to the solution mentioned in the paper involves utilizing Language and Visual Language Models (LLMs and VLMs) to enable real-time language control of robots for adaptive and efficient human-robot interaction . The research emphasizes the importance of on-the-fly language control in dynamic and unpredictable environments to ensure effective human-robot cooperation and workflow efficiency .


How were the experiments in the paper designed?

The experiments in the paper were designed using two sets of experiments involving a Sawyer cobot arm and a TurtleBot mobile robot . The experiments utilized ROS noetic and Ubuntu 20.04 LTS for robot control and the Groq API with the LLAMA3 70B LLM model for language inference . The experiments aimed to evaluate the responses of assistive robots to language prompts of varying granularities and explore the necessity and feasibility of controlling the robot on-the-fly . The Sawyer cobot experiment focused on qualitative and quantitative control of the robots, demonstrating that quantitative prompts led to more accurate control compared to qualitative prompts . The experiments also highlighted the importance of adjusting joint positions accordingly based on language prompts to achieve desired robot movements .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is based on experiments conducted on a Sawyer cobot and a Turtlebot for assessing the responses of assistive robots to language prompts of varying granularities . The code for the experiments will be released on GitHub soon to benefit the community, indicating that the code is open source .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The study evaluates the responses of assistive robots to language prompts of varying granularities and explores the necessity and feasibility of controlling the robot on-the-fly . The experiments conducted on a Sawyer cobot and a Turtlebot demonstrate the adaptation of the solution to scenarios where assistive robots need to maneuver to assist, showcasing foundational insights into relying solely on language for robotic control in various scenarios on-the-fly . The experiments cover a range of prompts from qualitative to quantitative, showing that quantitative prompts can achieve more accurate control of both robots, emphasizing the importance of precise language commands for effective robot control . Additionally, the study highlights the challenges and benefits of on-the-fly language control for robots, especially in dynamic and unpredictable environments where real-time language control is essential for adaptation and efficient human-robot interaction . The results indicate that while qualitative prompts may lead to errors and safety concerns, quantitative prompts enable more accurate and successful robot actions, supporting the hypothesis that language granularity significantly affects robot control performance . Overall, the experiments and results provide valuable insights into the viability and effectiveness of controlling assistive robots using language, contributing to the advancement of on-the-fly robot control in various assistive scenarios .


What are the contributions of this paper?

The paper "LLM Granularity for On-the-Fly Robot Control" makes several contributions in the field of assistive robotics and human-robot interaction:

  • The paper evaluates the responses of assistive robots to language prompts of varying granularities, exploring the necessity and feasibility of controlling the robot on-the-fly .
  • It investigates the importance of on-the-fly language control for effective human-robot interactive assistance, especially in scenarios where long-horizon interactions are required .
  • The research focuses on the convergence of computer vision, large language models (LLMs), and robotics to introduce the 'visuolinguomotor' mode for assistive robots, enabling proactive and interactive assistance .
  • It addresses the challenges of using qualitative prompts in assistive robotics, emphasizing the need for real-time language corrections and the limitations of current models in generalizing from one case to another .
  • The paper highlights the significance of controlling assistive robots on-the-fly using language, particularly in dynamic and unpredictable environments, where forming robot policies using LLMs is crucial for spontaneous tasks .
  • It presents experiments conducted on a Sawyer cobot to demonstrate the impact of language granularity on robot control performance, ranging from qualitative to quantitative prompts, to enhance safety and efficiency in assistance .

What work can be continued in depth?

To further advance the research in the field of controlling assistive robots on-the-fly using language, several areas can be explored in depth based on the existing work:

  1. Fine-Tuning Models for Improved Performance: Future studies can focus on fine-tuning Large Language Models (LLMs) to enhance their ability to interpret and respond to language prompts accurately, especially in scenarios where qualitative prompts may lead to errors or ambiguities .

  2. Addressing Safety Concerns: Research can delve into developing strategies to ensure the safety of assistive robots when controlled through language commands, particularly when dealing with qualitative prompts that may lack specificity, potentially leading to unsafe actions .

  3. Generalization and Scalability: Further investigations can be conducted to improve the generalization capabilities of models used for on-the-fly robot control, aiming to enhance their scalability and adaptability across different scenarios and robot platforms .

  4. Real-Time Language Corrections: Building on existing approaches that involve real-time language corrections for robotic manipulation, future work can focus on refining these correction mechanisms to enable more seamless human-robot interactions and task executions .

  5. Quantifying Language Prompts: Research efforts can explore methods to quantify qualitative language prompts effectively to provide robots with clearer and more precise instructions, ultimately improving the efficiency and success rates of assistive tasks .

By delving deeper into these areas, researchers can contribute to the advancement of on-the-fly language control for assistive robots, addressing challenges related to safety, performance, adaptability, and interaction quality in various assistive scenarios.

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