Embodied Instruction Following in Unknown Environments

Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan·June 17, 2024

Summary

This paper presents a novel embodied instruction following (EIF) method that combines a hierarchical task planner and a low-level exploration controller to enable autonomous agents to execute human instructions in unknown environments. The approach utilizes multimodal large language models, semantic representation maps with dynamic region attention, and considers task completion processes and visual clues. Key features include: 1. Hierarchical framework: A high-level planner generates step-by-step plans based on natural language instructions, scene context, and semantic maps, while a low-level controller executes actions. 2. Online semantic feature maps: Dynamic maps that adapt to changing scenes, incorporating visual features and object relationships for efficient exploration. 3. Performance: The method achieves a 45.09% success rate in complex tasks like making breakfast and tidying rooms, outperforming existing EIF methods in terms of adaptability and task completion. 4. Comparison: Experiments in ProcTHOR and AI2THOR benchmarks demonstrate the method's superiority over other approaches, especially in handling long instructions and diverse scenes. The research highlights the importance of combining pre-trained models, scene understanding, and efficient exploration for creating more adaptable and capable autonomous systems in real-world scenarios. Future work will focus on integrating the system with real robots and addressing manipulation challenges.

Key findings

10

Paper digest

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

The paper aims to address the challenge of embodied instruction following in unknown environments, where agents need to understand and execute complex human instructions in unfamiliar settings without prior knowledge of the objects present . This problem involves generating feasible plans for interacting with objects that may not be known in advance, requiring real-time scene mapping and efficient exploration to achieve human goals with minimal action cost . While the concept of embodied instruction following in unknown environments is not entirely new, the paper proposes a novel method that leverages semantic feature maps and multimodal large language models to improve performance in challenging task settings and enhance practicality in real-world deployment scenarios .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to Embodied Instruction Following (EIF) in unknown environments . The research focuses on developing a method for complex tasks in unknown environments where the agent needs to navigate and interact with objects based on human instructions . The hypothesis revolves around the effectiveness and efficiency of the proposed framework in house-level unknown environments, emphasizing the importance of generating feasible plans in real-time without prior knowledge of the scene .


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

The paper proposes a novel Embodied Instruction Following (EIF) method for complex tasks in unknown environments, addressing the limitations of existing approaches . The key innovation lies in the method's ability to navigate unknown environments to efficiently discover relevant objects for task completion, unlike conventional methods that assume prior knowledge of interactable objects . This approach enables the agent to generate feasible plans with minimal exploration cost in real-time scenarios where object properties change frequently due to human activities .

The paper introduces an end-to-end framework that leverages large language models (LLMs) for EIF tasks, aiming to complete diverse and complex instructions efficiently . Unlike modular methods that sequentially learn various components, the proposed method directly generates low-level actions from raw image input and natural language, supervised by expert trajectories . This approach enhances the agent's reasoning power and generalization ability, crucial for handling complex tasks in unknown environments .

Furthermore, the paper details the two main stages of the proposed framework. The first stage involves using GPT-4 to generate high-level planning and low-level actions based on prompts and scene information, filtering logical errors with TextWorld . The second stage focuses on executing interactions with an oracle in a simulator, grounding the generated plans and actions into the physical scene while collecting expert trajectories . This methodology ensures the quality of the training dataset and the feasibility of planning in unknown environments .

Additionally, the paper emphasizes the importance of semantic feature maps in empowering embodied agents to explore unknown environments efficiently . These maps enable agents to acquire task-relevant information for action generation with low exploration cost, enhancing the agent's ability to interpret visually-grounded navigation instructions in real environments . The integration of semantic feature maps plays a crucial role in reducing storage overhead and representing scene topology efficiently .

In summary, the paper's contributions include:

  • Proposing an EIF method for complex tasks in unknown environments, addressing the limitations of existing approaches .
  • Introducing an end-to-end framework leveraging LLMs for EIF tasks to handle diverse and complex instructions efficiently .
  • Detailing a two-stage process involving GPT-4, TextWorld, and a simulator to ensure the quality and feasibility of planning in unknown environments .
  • Highlighting the significance of semantic feature maps in empowering embodied agents to explore unknown environments effectively . The proposed Embodied Instruction Following (EIF) method in the paper offers several key characteristics and advantages compared to previous methods .
  1. Efficient Object Discovery: Unlike conventional methods that assume prior knowledge of interactable objects, the proposed method navigates unknown environments to efficiently discover relevant objects for task completion . This approach enables the agent to generate feasible plans with minimal exploration cost, enhancing its adaptability in dynamic environments where object properties change frequently due to human activities .

  2. End-to-End Framework: The paper introduces an end-to-end framework leveraging large language models (LLMs) for EIF tasks, enabling the agent to handle diverse and complex instructions efficiently . This framework directly generates low-level actions from raw image input and natural language, supervised by expert trajectories, enhancing the agent's reasoning power and generalization ability crucial for complex tasks in unknown environments .

  3. Semantic Feature Maps: The integration of semantic feature maps empowers embodied agents to explore unknown environments effectively by acquiring task-relevant information for action generation with low exploration cost . These maps reduce storage overhead and efficiently represent scene topology, enabling the agent to interpret visually-grounded navigation instructions in real environments .

  4. Real-Time Scene Mapping: In realistic deployment scenarios, the proposed method works in unknown environments without stored scene maps, accurately representing dynamic scenes where object properties change frequently due to human activities . By building real-time scene maps and generating feasible plans with minimal exploration cost, the method enhances the practicality of embodied agents in dynamic real-world scenarios .

In summary, the proposed EIF method stands out for its efficient object discovery, end-to-end framework leveraging LLMs, integration of semantic feature maps, and real-time scene mapping capabilities, offering significant advantages over previous methods in handling complex tasks in unknown environments .


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 studies exist in the field of embodied instruction following in unknown environments. Noteworthy researchers in this field include Yao Mu, Qinglong Zhang, Wenhai Wang, and Ping Luo , as well as Michael Murray, Maya Cakmak, and Van-Quang Nguyen . These researchers have contributed to advancements in vision-language pre-training, interactive instruction-following tasks, and improving performance on instruction-following tasks .

The key to the solution mentioned in the paper involves empowering embodied agents to explore unknown environments efficiently by generating feasible interaction based on semantic feature maps. The agent acquires task-relevant information for action generation with low exploration cost by building real-time scene maps and leveraging dynamic region attention to update online semantic feature maps. This approach enables the agent to interact with relevant objects, navigate optimal borders, and achieve human goals with minimized action cost .


How were the experiments in the paper designed?

The experiments in the paper were designed with different numbers of samples for model adaptation, focusing on method, shot numbers, and various metrics for evaluation . The experiments involved utilizing 8 NVIDIA 3090 GPUs for finetuning the high-level planner and the low-level controller for an hour during the training stage . The experiments aimed to demonstrate the effectiveness and efficiency of the framework in house-level unknown environments, providing visual information for the planner and the controller .


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

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context. However, the study extensively experiments in the ProcTHOR and AI2THOR simulators, utilizing scenes with objects from various categories . The code for the study is not explicitly stated to be open source in the provided context.


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 strong support for the scientific hypotheses that needed verification. The study conducted experiments with different numbers of samples for model adaptation and evaluated various metrics such as success rates (SR), goal condition success (GC), path length, and their path-length-weighted counterparts for assessment . The results demonstrated the effectiveness and efficiency of the framework in house-level unknown environments, showcasing the ability of the method to efficiently explore unknown environments by understanding visual clues for executable plan generation . Additionally, the study compared the proposed method with state-of-the-art approaches on the ALFRED benchmark, showcasing higher success rates and shorter path lengths, even in zero-shot settings without finetuning the pre-trained agent . These comparisons and evaluations provide robust evidence supporting the scientific hypotheses and the effectiveness of the proposed method in embodied instruction following in unknown environments.


What are the contributions of this paper?

The contributions of the paper "Embodied Instruction Following in Unknown Environments" include:

  • Proposing an EIF method for complex tasks in unknown environments, where the agent efficiently discovers relevant objects for task completion without prior knowledge of interactable objects .
  • Developing a framework with two main stages: utilizing GPT-4 for high-level planning and low-level actions generation based on prompts and scene information, followed by executing interactions with an oracle in the simulator to ground the generated plans and actions into the physical scene .
  • Conducting experiments in simulated environments like ProcTHOR and AI2THOR to evaluate success rates, goal condition success, path length, and their path-length-weighted counterparts for performance evaluation .
  • Introducing semantic feature maps to empower embodied agents in exploring unknown environments efficiently, enabling them to generate feasible plans with minimal exploration cost .
  • Providing a comparison with different EIF methods across various instructions in the ProcTHOR simulator, showcasing the effectiveness of the proposed method in achieving success rates, goal condition success, and path length metrics .

What work can be continued in depth?

To delve deeper into the research on embodied instruction following in unknown environments, several avenues for further exploration can be considered based on the existing work:

  1. Real Manipulation Implementation: One area for further research involves implementing real manipulation tasks to enhance the practical application of the developed frameworks. This includes designing mobile manipulation strategies for general tasks and integrating navigation policies with manipulation techniques for a more comprehensive approach .

  2. Closed-Loop System on Real Robots: Another promising direction is to move towards implementing the closed-loop system on real robots. This step would involve transitioning from simulation environments to real-world scenarios, enabling the validation and refinement of the developed models in practical settings .

  3. Mobile Manipulation Strategies: Exploring mobile manipulation strategies for general tasks can contribute to enhancing the adaptability and versatility of embodied agents in unknown environments. By focusing on developing strategies that enable agents to interact with objects and navigate effectively, the efficiency and effectiveness of the systems can be further improved .

By addressing these areas of research, advancements can be made in the field of embodied instruction following, leading to more robust and practical solutions for navigating and interacting in unknown environments.

Tables

5

Introduction
Background
Evolution of embodied AI and instruction following challenges
Importance of adaptability in real-world scenarios
Objective
To develop a novel EIF method for autonomous agents
Improve adaptability, task completion, and performance in unknown environments
Method
Hierarchical Task Planning
High-Level Planner
Natural language processing with multimodal large language models
Integration of scene context and semantic maps
Generation of step-by-step plans
Low-Level Controller
Execution of high-level plans with action sequences
Adaptation to changing environments
Online Semantic Feature Maps
Dynamic region attention for efficient exploration
Incorporation of visual features and object relationships
Real-time map updates
Performance Evaluation
Success Rate
Achieved 45.09% success rate in complex tasks (e.g., making breakfast, tidying rooms)
Comparison with existing EIF methods
Benchmarks
ProcTHOR and AI2THOR experiments: Superior performance in handling long instructions and diverse scenes
Experimental Results
Comparison of the proposed method with state-of-the-art techniques
Demonstrated advantages in adaptability and task completion
Future Work
Integration with real-world robots
Addressing manipulation challenges in the system
Conclusion
The paper's contribution to the field of embodied AI
Potential impact on autonomous systems for real-world applications
Basic info
papers
robotics
artificial intelligence
Advanced features
Insights
What is the primary focus of the paper's proposed embodied instruction following method?
How does the hierarchical framework in the method contribute to autonomous agents' task execution?
In which benchmarks are the method's performance and superiority demonstrated, and what are the key areas where it excels?
What is the success rate achieved by the method in complex tasks, and how does it compare to existing EIF methods?

Embodied Instruction Following in Unknown Environments

Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan·June 17, 2024

Summary

This paper presents a novel embodied instruction following (EIF) method that combines a hierarchical task planner and a low-level exploration controller to enable autonomous agents to execute human instructions in unknown environments. The approach utilizes multimodal large language models, semantic representation maps with dynamic region attention, and considers task completion processes and visual clues. Key features include: 1. Hierarchical framework: A high-level planner generates step-by-step plans based on natural language instructions, scene context, and semantic maps, while a low-level controller executes actions. 2. Online semantic feature maps: Dynamic maps that adapt to changing scenes, incorporating visual features and object relationships for efficient exploration. 3. Performance: The method achieves a 45.09% success rate in complex tasks like making breakfast and tidying rooms, outperforming existing EIF methods in terms of adaptability and task completion. 4. Comparison: Experiments in ProcTHOR and AI2THOR benchmarks demonstrate the method's superiority over other approaches, especially in handling long instructions and diverse scenes. The research highlights the importance of combining pre-trained models, scene understanding, and efficient exploration for creating more adaptable and capable autonomous systems in real-world scenarios. Future work will focus on integrating the system with real robots and addressing manipulation challenges.
Mind map
ProcTHOR and AI2THOR experiments: Superior performance in handling long instructions and diverse scenes
Comparison with existing EIF methods
Achieved 45.09% success rate in complex tasks (e.g., making breakfast, tidying rooms)
Adaptation to changing environments
Execution of high-level plans with action sequences
Generation of step-by-step plans
Integration of scene context and semantic maps
Natural language processing with multimodal large language models
Demonstrated advantages in adaptability and task completion
Comparison of the proposed method with state-of-the-art techniques
Benchmarks
Success Rate
Real-time map updates
Incorporation of visual features and object relationships
Dynamic region attention for efficient exploration
Low-Level Controller
High-Level Planner
Improve adaptability, task completion, and performance in unknown environments
To develop a novel EIF method for autonomous agents
Importance of adaptability in real-world scenarios
Evolution of embodied AI and instruction following challenges
Potential impact on autonomous systems for real-world applications
The paper's contribution to the field of embodied AI
Addressing manipulation challenges in the system
Integration with real-world robots
Experimental Results
Performance Evaluation
Online Semantic Feature Maps
Hierarchical Task Planning
Objective
Background
Conclusion
Future Work
Method
Introduction
Outline
Introduction
Background
Evolution of embodied AI and instruction following challenges
Importance of adaptability in real-world scenarios
Objective
To develop a novel EIF method for autonomous agents
Improve adaptability, task completion, and performance in unknown environments
Method
Hierarchical Task Planning
High-Level Planner
Natural language processing with multimodal large language models
Integration of scene context and semantic maps
Generation of step-by-step plans
Low-Level Controller
Execution of high-level plans with action sequences
Adaptation to changing environments
Online Semantic Feature Maps
Dynamic region attention for efficient exploration
Incorporation of visual features and object relationships
Real-time map updates
Performance Evaluation
Success Rate
Achieved 45.09% success rate in complex tasks (e.g., making breakfast, tidying rooms)
Comparison with existing EIF methods
Benchmarks
ProcTHOR and AI2THOR experiments: Superior performance in handling long instructions and diverse scenes
Experimental Results
Comparison of the proposed method with state-of-the-art techniques
Demonstrated advantages in adaptability and task completion
Future Work
Integration with real-world robots
Addressing manipulation challenges in the system
Conclusion
The paper's contribution to the field of embodied AI
Potential impact on autonomous systems for real-world applications
Key findings
10

Paper digest

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

The paper aims to address the challenge of embodied instruction following in unknown environments, where agents need to understand and execute complex human instructions in unfamiliar settings without prior knowledge of the objects present . This problem involves generating feasible plans for interacting with objects that may not be known in advance, requiring real-time scene mapping and efficient exploration to achieve human goals with minimal action cost . While the concept of embodied instruction following in unknown environments is not entirely new, the paper proposes a novel method that leverages semantic feature maps and multimodal large language models to improve performance in challenging task settings and enhance practicality in real-world deployment scenarios .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to Embodied Instruction Following (EIF) in unknown environments . The research focuses on developing a method for complex tasks in unknown environments where the agent needs to navigate and interact with objects based on human instructions . The hypothesis revolves around the effectiveness and efficiency of the proposed framework in house-level unknown environments, emphasizing the importance of generating feasible plans in real-time without prior knowledge of the scene .


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

The paper proposes a novel Embodied Instruction Following (EIF) method for complex tasks in unknown environments, addressing the limitations of existing approaches . The key innovation lies in the method's ability to navigate unknown environments to efficiently discover relevant objects for task completion, unlike conventional methods that assume prior knowledge of interactable objects . This approach enables the agent to generate feasible plans with minimal exploration cost in real-time scenarios where object properties change frequently due to human activities .

The paper introduces an end-to-end framework that leverages large language models (LLMs) for EIF tasks, aiming to complete diverse and complex instructions efficiently . Unlike modular methods that sequentially learn various components, the proposed method directly generates low-level actions from raw image input and natural language, supervised by expert trajectories . This approach enhances the agent's reasoning power and generalization ability, crucial for handling complex tasks in unknown environments .

Furthermore, the paper details the two main stages of the proposed framework. The first stage involves using GPT-4 to generate high-level planning and low-level actions based on prompts and scene information, filtering logical errors with TextWorld . The second stage focuses on executing interactions with an oracle in a simulator, grounding the generated plans and actions into the physical scene while collecting expert trajectories . This methodology ensures the quality of the training dataset and the feasibility of planning in unknown environments .

Additionally, the paper emphasizes the importance of semantic feature maps in empowering embodied agents to explore unknown environments efficiently . These maps enable agents to acquire task-relevant information for action generation with low exploration cost, enhancing the agent's ability to interpret visually-grounded navigation instructions in real environments . The integration of semantic feature maps plays a crucial role in reducing storage overhead and representing scene topology efficiently .

In summary, the paper's contributions include:

  • Proposing an EIF method for complex tasks in unknown environments, addressing the limitations of existing approaches .
  • Introducing an end-to-end framework leveraging LLMs for EIF tasks to handle diverse and complex instructions efficiently .
  • Detailing a two-stage process involving GPT-4, TextWorld, and a simulator to ensure the quality and feasibility of planning in unknown environments .
  • Highlighting the significance of semantic feature maps in empowering embodied agents to explore unknown environments effectively . The proposed Embodied Instruction Following (EIF) method in the paper offers several key characteristics and advantages compared to previous methods .
  1. Efficient Object Discovery: Unlike conventional methods that assume prior knowledge of interactable objects, the proposed method navigates unknown environments to efficiently discover relevant objects for task completion . This approach enables the agent to generate feasible plans with minimal exploration cost, enhancing its adaptability in dynamic environments where object properties change frequently due to human activities .

  2. End-to-End Framework: The paper introduces an end-to-end framework leveraging large language models (LLMs) for EIF tasks, enabling the agent to handle diverse and complex instructions efficiently . This framework directly generates low-level actions from raw image input and natural language, supervised by expert trajectories, enhancing the agent's reasoning power and generalization ability crucial for complex tasks in unknown environments .

  3. Semantic Feature Maps: The integration of semantic feature maps empowers embodied agents to explore unknown environments effectively by acquiring task-relevant information for action generation with low exploration cost . These maps reduce storage overhead and efficiently represent scene topology, enabling the agent to interpret visually-grounded navigation instructions in real environments .

  4. Real-Time Scene Mapping: In realistic deployment scenarios, the proposed method works in unknown environments without stored scene maps, accurately representing dynamic scenes where object properties change frequently due to human activities . By building real-time scene maps and generating feasible plans with minimal exploration cost, the method enhances the practicality of embodied agents in dynamic real-world scenarios .

In summary, the proposed EIF method stands out for its efficient object discovery, end-to-end framework leveraging LLMs, integration of semantic feature maps, and real-time scene mapping capabilities, offering significant advantages over previous methods in handling complex tasks in unknown environments .


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 studies exist in the field of embodied instruction following in unknown environments. Noteworthy researchers in this field include Yao Mu, Qinglong Zhang, Wenhai Wang, and Ping Luo , as well as Michael Murray, Maya Cakmak, and Van-Quang Nguyen . These researchers have contributed to advancements in vision-language pre-training, interactive instruction-following tasks, and improving performance on instruction-following tasks .

The key to the solution mentioned in the paper involves empowering embodied agents to explore unknown environments efficiently by generating feasible interaction based on semantic feature maps. The agent acquires task-relevant information for action generation with low exploration cost by building real-time scene maps and leveraging dynamic region attention to update online semantic feature maps. This approach enables the agent to interact with relevant objects, navigate optimal borders, and achieve human goals with minimized action cost .


How were the experiments in the paper designed?

The experiments in the paper were designed with different numbers of samples for model adaptation, focusing on method, shot numbers, and various metrics for evaluation . The experiments involved utilizing 8 NVIDIA 3090 GPUs for finetuning the high-level planner and the low-level controller for an hour during the training stage . The experiments aimed to demonstrate the effectiveness and efficiency of the framework in house-level unknown environments, providing visual information for the planner and the controller .


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

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context. However, the study extensively experiments in the ProcTHOR and AI2THOR simulators, utilizing scenes with objects from various categories . The code for the study is not explicitly stated to be open source in the provided context.


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 strong support for the scientific hypotheses that needed verification. The study conducted experiments with different numbers of samples for model adaptation and evaluated various metrics such as success rates (SR), goal condition success (GC), path length, and their path-length-weighted counterparts for assessment . The results demonstrated the effectiveness and efficiency of the framework in house-level unknown environments, showcasing the ability of the method to efficiently explore unknown environments by understanding visual clues for executable plan generation . Additionally, the study compared the proposed method with state-of-the-art approaches on the ALFRED benchmark, showcasing higher success rates and shorter path lengths, even in zero-shot settings without finetuning the pre-trained agent . These comparisons and evaluations provide robust evidence supporting the scientific hypotheses and the effectiveness of the proposed method in embodied instruction following in unknown environments.


What are the contributions of this paper?

The contributions of the paper "Embodied Instruction Following in Unknown Environments" include:

  • Proposing an EIF method for complex tasks in unknown environments, where the agent efficiently discovers relevant objects for task completion without prior knowledge of interactable objects .
  • Developing a framework with two main stages: utilizing GPT-4 for high-level planning and low-level actions generation based on prompts and scene information, followed by executing interactions with an oracle in the simulator to ground the generated plans and actions into the physical scene .
  • Conducting experiments in simulated environments like ProcTHOR and AI2THOR to evaluate success rates, goal condition success, path length, and their path-length-weighted counterparts for performance evaluation .
  • Introducing semantic feature maps to empower embodied agents in exploring unknown environments efficiently, enabling them to generate feasible plans with minimal exploration cost .
  • Providing a comparison with different EIF methods across various instructions in the ProcTHOR simulator, showcasing the effectiveness of the proposed method in achieving success rates, goal condition success, and path length metrics .

What work can be continued in depth?

To delve deeper into the research on embodied instruction following in unknown environments, several avenues for further exploration can be considered based on the existing work:

  1. Real Manipulation Implementation: One area for further research involves implementing real manipulation tasks to enhance the practical application of the developed frameworks. This includes designing mobile manipulation strategies for general tasks and integrating navigation policies with manipulation techniques for a more comprehensive approach .

  2. Closed-Loop System on Real Robots: Another promising direction is to move towards implementing the closed-loop system on real robots. This step would involve transitioning from simulation environments to real-world scenarios, enabling the validation and refinement of the developed models in practical settings .

  3. Mobile Manipulation Strategies: Exploring mobile manipulation strategies for general tasks can contribute to enhancing the adaptability and versatility of embodied agents in unknown environments. By focusing on developing strategies that enable agents to interact with objects and navigate effectively, the efficiency and effectiveness of the systems can be further improved .

By addressing these areas of research, advancements can be made in the field of embodied instruction following, leading to more robust and practical solutions for navigating and interacting in unknown environments.

Tables
5
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