Details Make a Difference: Object State-Sensitive Neurorobotic Task Planning

Xiaowen Sun, Xufeng Zhao, Jae Hee Lee, Wenhao Lu, Matthias Kerzel, Stefan Wermter·June 14, 2024

Summary

The paper investigates the use of pre-trained Large Language Models (LLMs) and Vision-Language Models (VLMs) in developing the Object State-Sensitive Agent (OSSA) for robotics. OSSA aims to handle object state-sensitive tasks by differentiating object states, applying commonsense reasoning, and considering user preferences. The study compares a modular model (OSSA-LLM-DCM) with a monolithic VLM approach (OSSA-VLM) using a novel tabletop-clearing task dataset. The monolithic VLM method outperforms the modular one, demonstrating the potential of VLMs in handling real-world scenarios with object state variations. The research highlights the importance of object state understanding in robotics and the need for models that can adapt to diverse object states and user instructions. Future work involves refining the monolithic approach and exploring the combination of object detection and language models for improved performance.

Key findings

5

Paper digest

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

The paper aims to address the problem of integrating state-sensitive knowledge into robotic systems for task planning, specifically focusing on object states and common sense reasoning . This problem is not entirely new, but the paper introduces an Object State-Sensitive Agent (OSSA) that utilizes pre-trained neural networks for robot task planning, emphasizing the importance of considering object states in planning tasks for household robots . The research highlights challenges such as identifying different objects in various states, distinguishing between object states, and employing commonsense reasoning to take state-sensitive actions based on object states and user preferences .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to state-sensitive instruction following in robotics. The study investigates two different methods: a modular model comprising an object detection module and a Large Language Model (LLM), and a monolithic Vision-Language Model (VLM) only model . The research focuses on how robots can identify object states, consider user preferences, and generate appropriate actions based on the object's state and user requirements . The paper explores the use of data-driven models, such as large language models, for commonsense reasoning and task planning in robotic scenarios .


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

The paper introduces an Object State-Sensitive Agent (OSSA) that focuses on integrating object states into robot task planning using pre-trained neural networks for commonsense reasoning . The OSSA aims to address challenges such as identifying different objects in various states and employing commonsense reasoning for state-sensitive actions without exhaustive design or user intervention . To achieve this, the paper proposes two different methods: a modular model combining an object detection module with a Large Language Model (LLM) and a monolithic approach using a Vision-Language Model (VLM) . The study explores the effectiveness of these methods in state-sensitive instruction following tasks, highlighting the superior performance of the monolithic VLM approach . Additionally, the paper emphasizes the importance of leveraging data-driven models like large language models for effective commonsense reasoning in robotic tasks . The Object State-Sensitive Agent (OSSA) proposed in the paper introduces innovative characteristics and advantages compared to previous methods in robotic task planning . The OSSA focuses on integrating object states into task planning by leveraging pre-trained neural networks for commonsense reasoning, enabling the robot to handle new objects and states effectively . One key advantage of the OSSA is its ability to identify cases where common sense should not dominate, such as considering user preferences when handling specific objects in different states . This user-centric approach ensures that the robot can adapt its actions based on individual user preferences, enhancing the overall user experience .

The paper explores two main methods within the OSSA framework: a modular model combining an object detection module with a Large Language Model (LLM) and a monolithic approach using a Vision-Language Model (VLM) . The modular model aims to integrate object detection with language models for task planning, while the monolithic approach solely relies on a VLM for generating object manipulation plans . Through experimental evaluation, the study demonstrates that the monolithic VLM approach outperforms the modular model in state-sensitive instruction following tasks, highlighting the efficiency and effectiveness of leveraging VLMs for robotic tasks .

Furthermore, the OSSA addresses the limitations of existing approaches by emphasizing the importance of data-driven models, such as large language models, for effective commonsense reasoning in robotic tasks . By utilizing pre-trained models like GPT-4V, the OSSA can generate more concrete information and perform better in ambiguity detection, destination generation, and task completion compared to traditional methods . Additionally, the OSSA-VLM variant excels in grasping and placing action generation, showcasing the superior performance of the monolithic VLM approach in various task scenarios .

In conclusion, the OSSA introduces a novel approach to object state-sensitive task planning in robotics, offering advantages such as user-centric adaptation, efficient commonsense reasoning, and superior performance in instruction following tasks compared to traditional methods . By leveraging advanced neural networks like VLMs, the OSSA demonstrates the potential for enhancing robotic capabilities in handling diverse object states and user preferences, paving the way for more sophisticated and user-friendly robotic systems in the future .


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 papers exist in the field of object state-sensitive neurorobotic task planning. Noteworthy researchers in this field include Minderer, Gritsenko, Houlsby, Nyga, Roy, Paul, Park, Pomarlan, Beetz, Radford, Kim, Hallacy, Ramesh, Goh, Agarwal, Sastry, Askell, Mishkin, Clark, Ren, Dixit, Bodrova, Singh, Tu, Brown, Sun, Huang, Xia, Xiao, Chan, Liang, Florence, Jang, Irpan, Khansari, Kappler, Ebert, Lynch, Levine, Finn, Lin, Ahmed, Azarnasab, Yang, Mousavian, Goyal, Xu, Tremblay, Song, Bohg, Rusinkiewicz, Funkhouser, and many others .

The key to the solution mentioned in the paper involves the development of an Object State-Sensitive Agent (OSSA) empowered by pre-trained neural networks. The paper proposes two methods for OSSA: a modular model consisting of a pre-trained vision processing module and a natural language processing model, and a monolithic model consisting only of a Vision-Language Model (VLM). The study evaluates the performances of these methods using tabletop scenarios where the task is to clear the table, demonstrating that both methods can be utilized for object state-sensitive tasks, with the monolithic approach outperforming the modular approach .


How were the experiments in the paper designed?

The experiments in the paper were designed to study the problem of state-sensitive instruction following in the context of object manipulation by a robot. Two different methods were investigated: . The first method involved a modular model comprising an object detection module and a Large Language Model (LLM). The second method utilized a monolithic Vision-Language Model (VLM) .

The experimental setup involved a system architecture where the robot interacted with the user, received user utterances, obtained images of the table, and performed object state-sensitive actions based on the input . The experiments aimed to evaluate the performance of the robot in identifying cases where common sense should not dominate, such as considering user preferences when handling specific objects in specific states .

Different tasks were defined for the experiments, including ambiguity detection, destination generation, and completion rate assessment . The experiments evaluated the performance of the models in object state detection accuracy, ambiguous detection accuracy, destination generation accuracy, grasping type generation accuracy, and placing type generation accuracy . The evaluation metrics included State Detection Accuracy (StaA), Ambiguous Detection Accuracy (AmbA), Destination Generation Accuracy (DesA), Grasping Type Generation Accuracy (GraA), Placing Type Generation Accuracy (PlaA), and Completion Accuracy (ComA) .


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

The dataset used for quantitative evaluation in the study is a multimodal benchmark dataset formulated for tabletop scenarios where the task involves clearing the table . The dataset was created to consider object states and was used to evaluate the proposed methods . Regarding the open-source availability of the code, the provided context does not mention whether the code used in the study is open source or not.


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 needed verification. The study focuses on state-sensitive instruction following in robotic tasks, investigating two different methods: a modular model with an object detection module and a language model, and a monolithic model based solely on a vision-language model . The experiments conducted involve formulating tabletop scenarios for table clearing tasks and evaluating the proposed methods on a multimodal benchmark dataset that considers object states .

The results of the experiments demonstrate the effectiveness of the proposed methods in handling object states and planning tasks accordingly. The study evaluates the performance of the models in generating destinations for objects, grasping actions, and placing actions based on object states, shapes, and sizes . The models show high performance levels above 90% accuracy in various aspects of the task planning process .

Furthermore, the paper acknowledges the limitations of the monolithic approach in terms of not being trained to generate bounding boxes of objects, highlighting the need for additional object detection models for object localization . The future directions outlined in the study include developing models capable of distinguishing between objects in different states and localizing their locations, with plans to apply these models in real scenarios with real robots, considering factors like cost and time for creating and executing object state-sensitive plans .

In conclusion, the experiments and results presented in the paper provide strong empirical support for the scientific hypotheses under investigation, showcasing the effectiveness of the proposed methods in addressing state-sensitive instruction following in robotic tasks and laying the groundwork for future advancements in this field .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Introducing a model that can distinguish between objects in different states and localize their locations .
  • Developing models for real scenarios with real robots, considering objectives such as cost and time for creating and executing object state-sensitive plans .
  • Acknowledging support from the China Scholarship Council (CSC) and the German Research Foundation DFG under project CML (TRR 169) .

What work can be continued in depth?

To further advance the research in object state-sensitive neurorobotic task planning, several areas can be explored in depth based on the existing work:

  • Developing a model capable of distinguishing between objects in different states and localizing their locations would be a valuable continuation .
  • Enhancing models to handle real-world scenarios with robots, considering additional objectives like cost and time for creating and executing object state-sensitive plans .
  • Addressing the challenge of identifying different objects in a scene and distinguishing between their states, crucial for tasks like 'clear the table,' where recognizing whole vs. sliced fruit or clean vs. dirty plates is essential .
  • Incorporating commonsense reasoning into robotic actions based on object states in various scenarios, considering user preferences when handling specific objects in specific states .
  • Investigating the effectiveness of modular models combining vision processing modules with natural language processing models versus monolithic vision-language models for object state-sensitive tasks .

Introduction
Background
Emergence of Large Language Models (LLMs) and Vision-Language Models (VLMs) in AI
Importance of object state understanding in robotics tasks
Objective
Develop and compare OSSA-LLM-DCM and OSSA-VLM for object state-sensitive tasks
Evaluate performance in a tabletop-clearing task
Investigate the potential of VLMs for real-world scenarios with object variations
Method
Data Collection
Novel tabletop-clearing task dataset creation
Object state variations and user instructions included
Data Preprocessing
Preparation of input data for LLMs and VLMs
Standardization and formatting for model integration
Model Development
OSSA-LLM-DCM
Modular approach using LLMs for language understanding and DCM (Domain Control Module) for task execution
OSSA-VLM
Monolithic VLM approach for joint understanding of vision and language
Performance Evaluation
Task completion rates and accuracy analysis
Comparison of OSSA-LLM-DCM and OSSA-VLM performance
Results and Discussion
Monolithic VLM's superiority in handling object state variations
Limitations and lessons learned from the modular approach
Future Work
Refining the monolithic VLM method
Exploring object detection and language model integration for enhanced performance
Potential applications and real-world implications
Basic info
papers
computation and language
robotics
artificial intelligence
Advanced features
Insights
How does the study compare OSSA-LLM-DCM and OSSA-VLM methods?
What is the significance of the monolithic VLM approach in handling object state-sensitive tasks?
What is the primary focus of the paper?
What is the main goal of the Object State-Sensitive Agent (OSSA) in robotics?

Details Make a Difference: Object State-Sensitive Neurorobotic Task Planning

Xiaowen Sun, Xufeng Zhao, Jae Hee Lee, Wenhao Lu, Matthias Kerzel, Stefan Wermter·June 14, 2024

Summary

The paper investigates the use of pre-trained Large Language Models (LLMs) and Vision-Language Models (VLMs) in developing the Object State-Sensitive Agent (OSSA) for robotics. OSSA aims to handle object state-sensitive tasks by differentiating object states, applying commonsense reasoning, and considering user preferences. The study compares a modular model (OSSA-LLM-DCM) with a monolithic VLM approach (OSSA-VLM) using a novel tabletop-clearing task dataset. The monolithic VLM method outperforms the modular one, demonstrating the potential of VLMs in handling real-world scenarios with object state variations. The research highlights the importance of object state understanding in robotics and the need for models that can adapt to diverse object states and user instructions. Future work involves refining the monolithic approach and exploring the combination of object detection and language models for improved performance.
Mind map
Monolithic VLM approach for joint understanding of vision and language
Modular approach using LLMs for language understanding and DCM (Domain Control Module) for task execution
Potential applications and real-world implications
Exploring object detection and language model integration for enhanced performance
Refining the monolithic VLM method
Limitations and lessons learned from the modular approach
Monolithic VLM's superiority in handling object state variations
Comparison of OSSA-LLM-DCM and OSSA-VLM performance
Task completion rates and accuracy analysis
OSSA-VLM
OSSA-LLM-DCM
Standardization and formatting for model integration
Preparation of input data for LLMs and VLMs
Object state variations and user instructions included
Novel tabletop-clearing task dataset creation
Investigate the potential of VLMs for real-world scenarios with object variations
Evaluate performance in a tabletop-clearing task
Develop and compare OSSA-LLM-DCM and OSSA-VLM for object state-sensitive tasks
Importance of object state understanding in robotics tasks
Emergence of Large Language Models (LLMs) and Vision-Language Models (VLMs) in AI
Future Work
Results and Discussion
Performance Evaluation
Model Development
Data Preprocessing
Data Collection
Objective
Background
Method
Introduction
Outline
Introduction
Background
Emergence of Large Language Models (LLMs) and Vision-Language Models (VLMs) in AI
Importance of object state understanding in robotics tasks
Objective
Develop and compare OSSA-LLM-DCM and OSSA-VLM for object state-sensitive tasks
Evaluate performance in a tabletop-clearing task
Investigate the potential of VLMs for real-world scenarios with object variations
Method
Data Collection
Novel tabletop-clearing task dataset creation
Object state variations and user instructions included
Data Preprocessing
Preparation of input data for LLMs and VLMs
Standardization and formatting for model integration
Model Development
OSSA-LLM-DCM
Modular approach using LLMs for language understanding and DCM (Domain Control Module) for task execution
OSSA-VLM
Monolithic VLM approach for joint understanding of vision and language
Performance Evaluation
Task completion rates and accuracy analysis
Comparison of OSSA-LLM-DCM and OSSA-VLM performance
Results and Discussion
Monolithic VLM's superiority in handling object state variations
Limitations and lessons learned from the modular approach
Future Work
Refining the monolithic VLM method
Exploring object detection and language model integration for enhanced performance
Potential applications and real-world implications
Key findings
5

Paper digest

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

The paper aims to address the problem of integrating state-sensitive knowledge into robotic systems for task planning, specifically focusing on object states and common sense reasoning . This problem is not entirely new, but the paper introduces an Object State-Sensitive Agent (OSSA) that utilizes pre-trained neural networks for robot task planning, emphasizing the importance of considering object states in planning tasks for household robots . The research highlights challenges such as identifying different objects in various states, distinguishing between object states, and employing commonsense reasoning to take state-sensitive actions based on object states and user preferences .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to state-sensitive instruction following in robotics. The study investigates two different methods: a modular model comprising an object detection module and a Large Language Model (LLM), and a monolithic Vision-Language Model (VLM) only model . The research focuses on how robots can identify object states, consider user preferences, and generate appropriate actions based on the object's state and user requirements . The paper explores the use of data-driven models, such as large language models, for commonsense reasoning and task planning in robotic scenarios .


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

The paper introduces an Object State-Sensitive Agent (OSSA) that focuses on integrating object states into robot task planning using pre-trained neural networks for commonsense reasoning . The OSSA aims to address challenges such as identifying different objects in various states and employing commonsense reasoning for state-sensitive actions without exhaustive design or user intervention . To achieve this, the paper proposes two different methods: a modular model combining an object detection module with a Large Language Model (LLM) and a monolithic approach using a Vision-Language Model (VLM) . The study explores the effectiveness of these methods in state-sensitive instruction following tasks, highlighting the superior performance of the monolithic VLM approach . Additionally, the paper emphasizes the importance of leveraging data-driven models like large language models for effective commonsense reasoning in robotic tasks . The Object State-Sensitive Agent (OSSA) proposed in the paper introduces innovative characteristics and advantages compared to previous methods in robotic task planning . The OSSA focuses on integrating object states into task planning by leveraging pre-trained neural networks for commonsense reasoning, enabling the robot to handle new objects and states effectively . One key advantage of the OSSA is its ability to identify cases where common sense should not dominate, such as considering user preferences when handling specific objects in different states . This user-centric approach ensures that the robot can adapt its actions based on individual user preferences, enhancing the overall user experience .

The paper explores two main methods within the OSSA framework: a modular model combining an object detection module with a Large Language Model (LLM) and a monolithic approach using a Vision-Language Model (VLM) . The modular model aims to integrate object detection with language models for task planning, while the monolithic approach solely relies on a VLM for generating object manipulation plans . Through experimental evaluation, the study demonstrates that the monolithic VLM approach outperforms the modular model in state-sensitive instruction following tasks, highlighting the efficiency and effectiveness of leveraging VLMs for robotic tasks .

Furthermore, the OSSA addresses the limitations of existing approaches by emphasizing the importance of data-driven models, such as large language models, for effective commonsense reasoning in robotic tasks . By utilizing pre-trained models like GPT-4V, the OSSA can generate more concrete information and perform better in ambiguity detection, destination generation, and task completion compared to traditional methods . Additionally, the OSSA-VLM variant excels in grasping and placing action generation, showcasing the superior performance of the monolithic VLM approach in various task scenarios .

In conclusion, the OSSA introduces a novel approach to object state-sensitive task planning in robotics, offering advantages such as user-centric adaptation, efficient commonsense reasoning, and superior performance in instruction following tasks compared to traditional methods . By leveraging advanced neural networks like VLMs, the OSSA demonstrates the potential for enhancing robotic capabilities in handling diverse object states and user preferences, paving the way for more sophisticated and user-friendly robotic systems in the future .


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 papers exist in the field of object state-sensitive neurorobotic task planning. Noteworthy researchers in this field include Minderer, Gritsenko, Houlsby, Nyga, Roy, Paul, Park, Pomarlan, Beetz, Radford, Kim, Hallacy, Ramesh, Goh, Agarwal, Sastry, Askell, Mishkin, Clark, Ren, Dixit, Bodrova, Singh, Tu, Brown, Sun, Huang, Xia, Xiao, Chan, Liang, Florence, Jang, Irpan, Khansari, Kappler, Ebert, Lynch, Levine, Finn, Lin, Ahmed, Azarnasab, Yang, Mousavian, Goyal, Xu, Tremblay, Song, Bohg, Rusinkiewicz, Funkhouser, and many others .

The key to the solution mentioned in the paper involves the development of an Object State-Sensitive Agent (OSSA) empowered by pre-trained neural networks. The paper proposes two methods for OSSA: a modular model consisting of a pre-trained vision processing module and a natural language processing model, and a monolithic model consisting only of a Vision-Language Model (VLM). The study evaluates the performances of these methods using tabletop scenarios where the task is to clear the table, demonstrating that both methods can be utilized for object state-sensitive tasks, with the monolithic approach outperforming the modular approach .


How were the experiments in the paper designed?

The experiments in the paper were designed to study the problem of state-sensitive instruction following in the context of object manipulation by a robot. Two different methods were investigated: . The first method involved a modular model comprising an object detection module and a Large Language Model (LLM). The second method utilized a monolithic Vision-Language Model (VLM) .

The experimental setup involved a system architecture where the robot interacted with the user, received user utterances, obtained images of the table, and performed object state-sensitive actions based on the input . The experiments aimed to evaluate the performance of the robot in identifying cases where common sense should not dominate, such as considering user preferences when handling specific objects in specific states .

Different tasks were defined for the experiments, including ambiguity detection, destination generation, and completion rate assessment . The experiments evaluated the performance of the models in object state detection accuracy, ambiguous detection accuracy, destination generation accuracy, grasping type generation accuracy, and placing type generation accuracy . The evaluation metrics included State Detection Accuracy (StaA), Ambiguous Detection Accuracy (AmbA), Destination Generation Accuracy (DesA), Grasping Type Generation Accuracy (GraA), Placing Type Generation Accuracy (PlaA), and Completion Accuracy (ComA) .


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

The dataset used for quantitative evaluation in the study is a multimodal benchmark dataset formulated for tabletop scenarios where the task involves clearing the table . The dataset was created to consider object states and was used to evaluate the proposed methods . Regarding the open-source availability of the code, the provided context does not mention whether the code used in the study is open source or not.


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 needed verification. The study focuses on state-sensitive instruction following in robotic tasks, investigating two different methods: a modular model with an object detection module and a language model, and a monolithic model based solely on a vision-language model . The experiments conducted involve formulating tabletop scenarios for table clearing tasks and evaluating the proposed methods on a multimodal benchmark dataset that considers object states .

The results of the experiments demonstrate the effectiveness of the proposed methods in handling object states and planning tasks accordingly. The study evaluates the performance of the models in generating destinations for objects, grasping actions, and placing actions based on object states, shapes, and sizes . The models show high performance levels above 90% accuracy in various aspects of the task planning process .

Furthermore, the paper acknowledges the limitations of the monolithic approach in terms of not being trained to generate bounding boxes of objects, highlighting the need for additional object detection models for object localization . The future directions outlined in the study include developing models capable of distinguishing between objects in different states and localizing their locations, with plans to apply these models in real scenarios with real robots, considering factors like cost and time for creating and executing object state-sensitive plans .

In conclusion, the experiments and results presented in the paper provide strong empirical support for the scientific hypotheses under investigation, showcasing the effectiveness of the proposed methods in addressing state-sensitive instruction following in robotic tasks and laying the groundwork for future advancements in this field .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Introducing a model that can distinguish between objects in different states and localize their locations .
  • Developing models for real scenarios with real robots, considering objectives such as cost and time for creating and executing object state-sensitive plans .
  • Acknowledging support from the China Scholarship Council (CSC) and the German Research Foundation DFG under project CML (TRR 169) .

What work can be continued in depth?

To further advance the research in object state-sensitive neurorobotic task planning, several areas can be explored in depth based on the existing work:

  • Developing a model capable of distinguishing between objects in different states and localizing their locations would be a valuable continuation .
  • Enhancing models to handle real-world scenarios with robots, considering additional objectives like cost and time for creating and executing object state-sensitive plans .
  • Addressing the challenge of identifying different objects in a scene and distinguishing between their states, crucial for tasks like 'clear the table,' where recognizing whole vs. sliced fruit or clean vs. dirty plates is essential .
  • Incorporating commonsense reasoning into robotic actions based on object states in various scenarios, considering user preferences when handling specific objects in specific states .
  • Investigating the effectiveness of modular models combining vision processing modules with natural language processing models versus monolithic vision-language models for object state-sensitive tasks .
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