Human-centered In-building Embodied Delivery Benchmark

Zhuoqun Xu, Yang Liu, Xiaoqi Li, Jiyao Zhang, Hao Dong·June 25, 2024

Summary

This paper contributes to the field of embodied intelligence by proposing a human-centered in-building delivery scenario for commercial applications, specifically focusing on a polar research station-like environment. Researchers have developed a virtual environment with autonomous characters, robots, and interactive items, creating a large dataset of 13,000 language instructions for guiding robot deliveries. The dataset aims to bridge the gap between skill scenarios and real-world delivery by emphasizing human-robot interaction in a realistic setting. The study highlights the use of multi-modal models for addressing challenges in human-centered delivery services and is part of the CVPR 2024 Embodied Workshop. Key aspects include a realistic simulation, diverse tasks, and the evaluation of AI models for navigation, manipulation, and language understanding. Future work will focus on improving human-robot interaction, expanding scenarios, and adapting to advancements in AI technology.

Key findings

11

Paper digest

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

The paper aims to address the gap between skill scenarios and real-world commercial application scenarios in the field of embodied AI . This gap is not a new problem, as it has been recognized that existing skill scenarios may not fully reflect the challenges encountered in actual commercial environments and may not accurately capture users' specific interaction needs with embodied robots . The paper suggests that exploring scenarios closer to real-world commercial applications can help advance the development of the embodied AI community .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that integrating existing skills to simulate specific commercial scenarios in human-centered in-building delivery services can drive the development of community technology towards commercialization .


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

The paper proposes a specific commercial scenario simulation called human-centered in-building embodied delivery. It introduces a brand-new virtual environment system inspired by polar research stations, featuring multi-level connected building spaces, autonomous human characters, robots with grasping and mobility capabilities, and a variety of interactive items . This environment aims to simulate human-robot interaction for commercial scenarios, focusing on precise in-building delivery services .

To support this scenario, the paper develops a delivery dataset containing 13k language instructions to guide robots in providing services. It simulates human behavior through human characters, capturing their various daily life needs and interactions with the robots . The dataset is designed to enable robots to understand human instructions, locate target items, and deliver them to designated recipients within a fixed building space .

Furthermore, the paper proposes a method centered around a large multimodal model to serve as the baseline system for this dataset. This approach emphasizes the importance of human-robot interaction in commercial scenarios and aims to bring new perspectives and exploration angles to the embodied AI community . The focus on simulating embodied commercial scenarios is intended to bridge the gap between existing skill scenarios and real-world application scenarios, fostering the development of novel topics within the embodied AI field . The paper introduces several key characteristics and advantages of the proposed method compared to previous approaches:

  1. Human-Centered Approach: The paper emphasizes a human-centered in-building embodied delivery scenario, focusing on simulating human-robot interactions in a commercial setting. This approach prioritizes understanding human needs, behaviors, and instructions to enhance the delivery service experience.

  2. Multi-Level Connected Building Spaces: The virtual environment system in the paper features multi-level connected building spaces, providing a more complex and realistic setting for the delivery scenario. This design allows for diverse navigation challenges and interactions between robots and human characters.

  3. Autonomous Human Characters: By incorporating autonomous human characters in the simulation, the method captures a more dynamic and realistic environment where robots interact with human-like entities. This feature adds complexity and richness to the simulation, enabling a more immersive experience.

  4. Dataset with Language Instructions: The paper introduces a delivery dataset containing 13k language instructions to guide robots in providing services. This dataset enables robots to understand and respond to human instructions accurately, enhancing the efficiency and effectiveness of the delivery process.

  5. Focus on Human-Robot Interaction: The method places a strong emphasis on human-robot interaction within commercial scenarios. By simulating various interactions between humans and robots, the approach aims to improve the overall service quality and user experience in delivery services.

  6. Large Multimodal Model: The proposed method leverages a large multimodal model as the baseline system for the dataset. This model integrates multiple modalities such as language instructions, visual inputs, and robot actions to enhance the understanding and performance of robots in the delivery scenario.

  7. Bridge Between Skill and Real-World Scenarios: The paper aims to bridge the gap between existing skill-based scenarios and real-world application scenarios in the embodied AI field. By focusing on commercial delivery services, the method seeks to explore new research directions and practical applications within the field.

Overall, the characteristics and advantages of the proposed method in the paper demonstrate a novel and comprehensive approach to simulating human-robot interactions in commercial delivery scenarios, offering a more realistic and immersive experience compared to previous methods.


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?

In the field of robotics and embodied delivery, there are several related research works and notable researchers mentioned in the provided context :

  • Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, and others have contributed to works such as "Rt-1: Robotics transformer for real-world control at scale" and "Do as I can, not as I say: Grounding language in robotic affordances" .
  • Peter Corke, Jesse Haviland, Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, Dhruv Batra, Matt Deitke, Winson Han, and others have also made significant contributions in this field .

The key to the solution mentioned in the paper is not explicitly provided in the context. To determine the specific solution discussed in the paper, it would be necessary to refer to the individual research works cited in the context .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the efficiency of different modules in a specific commercial scenario involving human-robot interaction for in-building delivery services. The experiment included tasks such as language parsing, object search recognition, and virtual human character search. The results were compared, and the GPT-4o-based method achieved a task success rate of 32.2% . The study aimed to integrate existing skills to simulate determined commercial scenarios, with a focus on driving the development of community technology towards commercialization .


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

To provide you with the most accurate information, I need more details about the specific project or research you are referring to. Could you please provide more context or details about the dataset and code you are inquiring about?


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 solid support for the scientific hypotheses that need to be verified. The study focuses on simulating a human-centered in-building delivery service scenario and evaluates the efficiency of various modules such as language parsing, object search recognition, and virtual human character search . The experimental results show that the GPT-4o-based method achieved a task success rate of 32.2% . This indicates that the experiments conducted in the paper effectively test and validate the specific commercial scenario with human-robot interaction at its core, supporting the scientific hypotheses related to embodied AI technology .

The paper integrates previous work on skill-learning scenarios and constructs a brand-new virtual environment system for human-centered in-building delivery services . By developing a delivery dataset containing language instructions and simulating human behavior through human characters, the study provides a comprehensive analysis of the scenario requirements and settings . This thorough approach to scenario analysis and task definition demonstrates a strong foundation for verifying the scientific hypotheses related to embodied intelligence and human-robot interaction in commercial scenarios .


What are the contributions of this paper?

The contributions of the paper "Human-centered In-building Embodied Delivery Benchmark" include:

  • Proposing a commercial scenario simulation of human-centered in-building embodied delivery, focusing on human-robot interaction for commercial scenarios .
  • Developing a brand-new virtual environment system from scratch, modeling a multi-level connected building space inspired by a polar research station, including autonomous human characters, robots, and interactive items .
  • Creating a delivery dataset with 13k language instructions to guide robots in providing services and simulating human behavior through human characters to sample various daily life needs .
  • Introducing a method centered around a large multimodal model as the baseline system for the dataset, emphasizing a virtual environment centered around human-robot interaction for commercial scenarios .
  • Hosting the work in the CVPR 2024 Embodied Workshop, contributing to the embodied community by offering new perspectives and exploration angles .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Creative projects that can be expanded upon with more ideas and iterations.
  4. Skill development activities that require continuous practice and improvement.
  5. Long-term projects that need ongoing monitoring and adjustments.

If you have a specific area of work in mind, feel free to provide more details so I can give you a more tailored response.

Tables

3

Introduction
Background
Evolution of embodied intelligence in commercial applications
Importance of human-robot collaboration in delivery systems
Objective
To propose a human-centered delivery scenario for polar research stations
Bridge the gap between simulated and real-world delivery tasks
Method
Data Collection
Virtual Environment Development
Creation of a realistic polar research station simulation
Autonomous characters, robots, and interactive items integration
Language Instruction Dataset
Collection of 13,000 language instructions for guiding robot deliveries
Emphasis on human-robot interaction and task diversity
Data Preprocessing
Cleaning and standardization of language instructions
Annotation for multi-modal understanding tasks
Multi-Modal Model Approach
Navigation
AI models for path planning and obstacle avoidance
Evaluation in the simulated environment
Manipulation
Robot manipulation skills for handling objects in the scenario
Performance analysis and improvement
Language Understanding
Development of models for interpreting and responding to instructions
Integration with navigation and manipulation tasks
Evaluation and Results
Assessment of AI models' performance in the scenario
Comparison with existing approaches in human-centered delivery
Future Work
Enhancing human-robot interaction for better collaboration
Expanding scenarios to other environments and tasks
Adapting to advancements in AI technology
CVPR 2024 Embodied Workshop Participation
Presentation and discussion of findings within the workshop community
Conclusion
Summary of contributions to embodied intelligence research
Implications for commercial applications and potential real-world impact
Basic info
papers
robotics
artificial intelligence
Advanced features
Insights
What workshop is the paper a part of, and what are some key aspects of the study?
What is the primary focus of the in-building delivery scenario proposed in the paper?
How many language instructions are included in the dataset developed by the researchers?
What field does the paper contribute to?

Human-centered In-building Embodied Delivery Benchmark

Zhuoqun Xu, Yang Liu, Xiaoqi Li, Jiyao Zhang, Hao Dong·June 25, 2024

Summary

This paper contributes to the field of embodied intelligence by proposing a human-centered in-building delivery scenario for commercial applications, specifically focusing on a polar research station-like environment. Researchers have developed a virtual environment with autonomous characters, robots, and interactive items, creating a large dataset of 13,000 language instructions for guiding robot deliveries. The dataset aims to bridge the gap between skill scenarios and real-world delivery by emphasizing human-robot interaction in a realistic setting. The study highlights the use of multi-modal models for addressing challenges in human-centered delivery services and is part of the CVPR 2024 Embodied Workshop. Key aspects include a realistic simulation, diverse tasks, and the evaluation of AI models for navigation, manipulation, and language understanding. Future work will focus on improving human-robot interaction, expanding scenarios, and adapting to advancements in AI technology.
Mind map
Emphasis on human-robot interaction and task diversity
Collection of 13,000 language instructions for guiding robot deliveries
Autonomous characters, robots, and interactive items integration
Creation of a realistic polar research station simulation
Presentation and discussion of findings within the workshop community
Integration with navigation and manipulation tasks
Development of models for interpreting and responding to instructions
Performance analysis and improvement
Robot manipulation skills for handling objects in the scenario
Evaluation in the simulated environment
AI models for path planning and obstacle avoidance
Annotation for multi-modal understanding tasks
Cleaning and standardization of language instructions
Language Instruction Dataset
Virtual Environment Development
Bridge the gap between simulated and real-world delivery tasks
To propose a human-centered delivery scenario for polar research stations
Importance of human-robot collaboration in delivery systems
Evolution of embodied intelligence in commercial applications
Implications for commercial applications and potential real-world impact
Summary of contributions to embodied intelligence research
CVPR 2024 Embodied Workshop Participation
Comparison with existing approaches in human-centered delivery
Assessment of AI models' performance in the scenario
Language Understanding
Manipulation
Navigation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Future Work
Evaluation and Results
Multi-Modal Model Approach
Method
Introduction
Outline
Introduction
Background
Evolution of embodied intelligence in commercial applications
Importance of human-robot collaboration in delivery systems
Objective
To propose a human-centered delivery scenario for polar research stations
Bridge the gap between simulated and real-world delivery tasks
Method
Data Collection
Virtual Environment Development
Creation of a realistic polar research station simulation
Autonomous characters, robots, and interactive items integration
Language Instruction Dataset
Collection of 13,000 language instructions for guiding robot deliveries
Emphasis on human-robot interaction and task diversity
Data Preprocessing
Cleaning and standardization of language instructions
Annotation for multi-modal understanding tasks
Multi-Modal Model Approach
Navigation
AI models for path planning and obstacle avoidance
Evaluation in the simulated environment
Manipulation
Robot manipulation skills for handling objects in the scenario
Performance analysis and improvement
Language Understanding
Development of models for interpreting and responding to instructions
Integration with navigation and manipulation tasks
Evaluation and Results
Assessment of AI models' performance in the scenario
Comparison with existing approaches in human-centered delivery
Future Work
Enhancing human-robot interaction for better collaboration
Expanding scenarios to other environments and tasks
Adapting to advancements in AI technology
CVPR 2024 Embodied Workshop Participation
Presentation and discussion of findings within the workshop community
Conclusion
Summary of contributions to embodied intelligence research
Implications for commercial applications and potential real-world impact
Key findings
11

Paper digest

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

The paper aims to address the gap between skill scenarios and real-world commercial application scenarios in the field of embodied AI . This gap is not a new problem, as it has been recognized that existing skill scenarios may not fully reflect the challenges encountered in actual commercial environments and may not accurately capture users' specific interaction needs with embodied robots . The paper suggests that exploring scenarios closer to real-world commercial applications can help advance the development of the embodied AI community .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that integrating existing skills to simulate specific commercial scenarios in human-centered in-building delivery services can drive the development of community technology towards commercialization .


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

The paper proposes a specific commercial scenario simulation called human-centered in-building embodied delivery. It introduces a brand-new virtual environment system inspired by polar research stations, featuring multi-level connected building spaces, autonomous human characters, robots with grasping and mobility capabilities, and a variety of interactive items . This environment aims to simulate human-robot interaction for commercial scenarios, focusing on precise in-building delivery services .

To support this scenario, the paper develops a delivery dataset containing 13k language instructions to guide robots in providing services. It simulates human behavior through human characters, capturing their various daily life needs and interactions with the robots . The dataset is designed to enable robots to understand human instructions, locate target items, and deliver them to designated recipients within a fixed building space .

Furthermore, the paper proposes a method centered around a large multimodal model to serve as the baseline system for this dataset. This approach emphasizes the importance of human-robot interaction in commercial scenarios and aims to bring new perspectives and exploration angles to the embodied AI community . The focus on simulating embodied commercial scenarios is intended to bridge the gap between existing skill scenarios and real-world application scenarios, fostering the development of novel topics within the embodied AI field . The paper introduces several key characteristics and advantages of the proposed method compared to previous approaches:

  1. Human-Centered Approach: The paper emphasizes a human-centered in-building embodied delivery scenario, focusing on simulating human-robot interactions in a commercial setting. This approach prioritizes understanding human needs, behaviors, and instructions to enhance the delivery service experience.

  2. Multi-Level Connected Building Spaces: The virtual environment system in the paper features multi-level connected building spaces, providing a more complex and realistic setting for the delivery scenario. This design allows for diverse navigation challenges and interactions between robots and human characters.

  3. Autonomous Human Characters: By incorporating autonomous human characters in the simulation, the method captures a more dynamic and realistic environment where robots interact with human-like entities. This feature adds complexity and richness to the simulation, enabling a more immersive experience.

  4. Dataset with Language Instructions: The paper introduces a delivery dataset containing 13k language instructions to guide robots in providing services. This dataset enables robots to understand and respond to human instructions accurately, enhancing the efficiency and effectiveness of the delivery process.

  5. Focus on Human-Robot Interaction: The method places a strong emphasis on human-robot interaction within commercial scenarios. By simulating various interactions between humans and robots, the approach aims to improve the overall service quality and user experience in delivery services.

  6. Large Multimodal Model: The proposed method leverages a large multimodal model as the baseline system for the dataset. This model integrates multiple modalities such as language instructions, visual inputs, and robot actions to enhance the understanding and performance of robots in the delivery scenario.

  7. Bridge Between Skill and Real-World Scenarios: The paper aims to bridge the gap between existing skill-based scenarios and real-world application scenarios in the embodied AI field. By focusing on commercial delivery services, the method seeks to explore new research directions and practical applications within the field.

Overall, the characteristics and advantages of the proposed method in the paper demonstrate a novel and comprehensive approach to simulating human-robot interactions in commercial delivery scenarios, offering a more realistic and immersive experience compared to previous methods.


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?

In the field of robotics and embodied delivery, there are several related research works and notable researchers mentioned in the provided context :

  • Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, and others have contributed to works such as "Rt-1: Robotics transformer for real-world control at scale" and "Do as I can, not as I say: Grounding language in robotic affordances" .
  • Peter Corke, Jesse Haviland, Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, Dhruv Batra, Matt Deitke, Winson Han, and others have also made significant contributions in this field .

The key to the solution mentioned in the paper is not explicitly provided in the context. To determine the specific solution discussed in the paper, it would be necessary to refer to the individual research works cited in the context .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the efficiency of different modules in a specific commercial scenario involving human-robot interaction for in-building delivery services. The experiment included tasks such as language parsing, object search recognition, and virtual human character search. The results were compared, and the GPT-4o-based method achieved a task success rate of 32.2% . The study aimed to integrate existing skills to simulate determined commercial scenarios, with a focus on driving the development of community technology towards commercialization .


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

To provide you with the most accurate information, I need more details about the specific project or research you are referring to. Could you please provide more context or details about the dataset and code you are inquiring about?


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 solid support for the scientific hypotheses that need to be verified. The study focuses on simulating a human-centered in-building delivery service scenario and evaluates the efficiency of various modules such as language parsing, object search recognition, and virtual human character search . The experimental results show that the GPT-4o-based method achieved a task success rate of 32.2% . This indicates that the experiments conducted in the paper effectively test and validate the specific commercial scenario with human-robot interaction at its core, supporting the scientific hypotheses related to embodied AI technology .

The paper integrates previous work on skill-learning scenarios and constructs a brand-new virtual environment system for human-centered in-building delivery services . By developing a delivery dataset containing language instructions and simulating human behavior through human characters, the study provides a comprehensive analysis of the scenario requirements and settings . This thorough approach to scenario analysis and task definition demonstrates a strong foundation for verifying the scientific hypotheses related to embodied intelligence and human-robot interaction in commercial scenarios .


What are the contributions of this paper?

The contributions of the paper "Human-centered In-building Embodied Delivery Benchmark" include:

  • Proposing a commercial scenario simulation of human-centered in-building embodied delivery, focusing on human-robot interaction for commercial scenarios .
  • Developing a brand-new virtual environment system from scratch, modeling a multi-level connected building space inspired by a polar research station, including autonomous human characters, robots, and interactive items .
  • Creating a delivery dataset with 13k language instructions to guide robots in providing services and simulating human behavior through human characters to sample various daily life needs .
  • Introducing a method centered around a large multimodal model as the baseline system for the dataset, emphasizing a virtual environment centered around human-robot interaction for commercial scenarios .
  • Hosting the work in the CVPR 2024 Embodied Workshop, contributing to the embodied community by offering new perspectives and exploration angles .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Creative projects that can be expanded upon with more ideas and iterations.
  4. Skill development activities that require continuous practice and improvement.
  5. Long-term projects that need ongoing monitoring and adjustments.

If you have a specific area of work in mind, feel free to provide more details so I can give you a more tailored response.

Tables
3
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