Embodied Instruction Following in Unknown Environments
(2406.11818)Abstract
Enabling embodied agents to complete complex human instructions from natural language is crucial to autonomous systems in household services. Conventional methods can only accomplish human instructions in the known environment where all interactive objects are provided to the embodied agent, and directly deploying the existing approaches for the unknown environment usually generates infeasible plans that manipulate non-existing objects. On the contrary, we propose an embodied instruction following (EIF) method for complex tasks in the unknown environment, where the agent efficiently explores the unknown environment to generate feasible plans with existing objects to accomplish abstract instructions. Specifically, we build a hierarchical embodied instruction following framework including the high-level task planner and the low-level exploration controller with multimodal LLMs. We then construct a semantic representation map of the scene with dynamic region attention to demonstrate the known visual clues, where the goal of task planning and scene exploration is aligned for human instruction. For the task planner, we generate the feasible step-by-step plans for human goal accomplishment according to the task completion process and the known visual clues. For the exploration controller, the optimal navigation or object interaction policy is predicted based on the generated step-wise plans and the known visual clues. The experimental results demonstrate that our method can achieve 45.09% success rate in 204 complex human instructions such as making breakfast and tidying rooms in large house-level scenes.
Overview
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Zhenyu Wu et al. introduce a hierarchical embodied instruction following (EIF) framework that combines a high-level task planner and a low-level exploration controller, enhancing autonomous agents' ability to accomplish complex human instructions in unknown settings.
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The research proposes a novel online semantic feature map with dynamic region attention to efficiently map and update visual information for scene exploration and task execution, showcasing significant improvements in the ProcTHOR and AI2THOR simulation environments.
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Experimental results indicate a 45.09% success rate in handling complex instructions in large household environments, outperforming state-of-the-art methods like LLM-Planner and FILM, and setting the stage for future advancements in multimodal LLMs and autonomous system design.
Embodied Instruction Following in Unknown Environments: A Comprehensive Overview
The paper "Embodied Instruction Following in Unknown Environments" by Zhenyu Wu et al. addresses a crucial advancement in autonomous systems designed for household services, promoting the ability of embodied agents to accomplish complex human instructions in unexplored settings. This research is pivotal in enhancing the practicality of autonomous agents to efficiently navigate and interact within dynamic environments where pre-existing knowledge of the scene's objects and their locations is unavailable.
Hierarchical Framework for Embodied Instruction Following
The core contribution of the paper is the introduction of a hierarchical embodied instruction following (EIF) framework composed of two main components: a high-level task planner and a low-level exploration controller. These components are built upon multimodal LLMs, specifically a finetuned LLaVA, which leverages both natural language understanding and visual inputs to generate and execute complex task plans.
High-Level Task Planner
The high-level task planner is responsible for generating feasible step-by-step plans based on human instructions, visual clues, and the task's completion process. Utilizing a specialized LLaVA model, the planner synthesizes the next steps required to accomplish a given task from natural language instructions enriched by scene information. This step-wise planning ensures that the task progression aligns with dynamically changing environments, enhancing the agent's adaptability to new scenes.
Low-Level Exploration Controller
The low-level controller focuses on discovering task-related objects with minimal action cost and executing specific interaction actions. It incorporates optimal navigation and object interaction policies derived from the high-level plan and semantic visual clues. This hierarchical approach ensures that the low-level actions are guided by a broader task-oriented strategy, thus balancing the need for exploration and accomplishment of the given task within unknown environments.
Semantic Representation and Online Updating Mechanism
A significant novelty in this research is the construction and utilization of an online semantic feature map with dynamic region attention. This map is designed to project collected RGB-D features into a top-down scene representation, dynamically updating as the agent explores its environment. The dynamic region attention mechanism assigns importance weights to the visual features based on their relevance to the current task, effectively reducing redundancy and focusing on pertinent scene elements. This ensures efficient scene exploration and visual information alignment for both planning and action execution.
Experimental Validation and Numerical Results
The experimental validation of the proposed methodology is conducted in the ProcTHOR and AI2THOR simulation environments. The approach demonstrates a 45.09% success rate in executing 204 complex human instructions within large house-level scenes, a significant improvement compared to existing methods. This result is indicative of the system's robustness in handling instructions related to household activities such as making breakfast and tidying rooms, showcasing the agent's ability to navigate and interact effectively in unknown environments.
Comparison with Baselines
The paper presents a rigorous comparison with state-of-the-art methods such as LLM-Planner and FILM. The proposed method significantly outperforms these baselines, especially in large environments where existing methods falter due to their reliance on pre-known scene information. The use of online semantic feature maps and dynamic region attention proves to be critical in maintaining high efficiency and efficacy in task accomplishment.
Implications and Future Directions
Practically, the proposed EIF method holds considerable promise for real-world autonomous systems in household settings. The ability to dynamically explore and generate task-relevant plans without relying on pre-stored scene information greatly enhances the flexibility and scalability of such systems. Theoretically, this work pushes the boundaries of multimodal LLMs, integrating them seamlessly with real-time visual inputs to achieve complex decision-making and action execution.
Future developments in this domain may involve refining the navigation algorithms to further minimize path length and improve success rates in even larger, more cluttered environments. Additionally, extending this framework to incorporate adaptive manipulation strategies could further enhance the agent's interaction capabilities, making it more adept at handling a wider array of household tasks. Real-world implementation and testing would also provide valuable insights into the adaptability of the proposed framework and highlight areas for potential enhancement.
Conclusion
Zhenyu Wu et al.'s work on embodied instruction following in unknown environments sets a foundational precedent for the development of more autonomous, efficient, and adaptable household robots. The combination of high-level planning, low-level control, and dynamic scene mapping represents a substantial step forward in enabling embodied agents to successfully perform complex tasks in dynamically changing environments. This paper undoubtedly provides a critical reference point for future research in autonomous system design and embodied AI.
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