Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making (2410.07166v3)

Abstract: We aim to evaluate LLMs for embodied decision making. While a significant body of work has been leveraging LLMs for decision making in embodied environments, we still lack a systematic understanding of their performance because they are usually applied in different domains, for different purposes, and built based on different inputs and outputs. Furthermore, existing evaluations tend to rely solely on a final success rate, making it difficult to pinpoint what ability is missing in LLMs and where the problem lies, which in turn blocks embodied agents from leveraging LLMs effectively and selectively. To address these limitations, we propose a generalized interface (Embodied Agent Interface) that supports the formalization of various types of tasks and input-output specifications of LLM-based modules. Specifically, it allows us to unify 1) a broad set of embodied decision-making tasks involving both state and temporally extended goals, 2) four commonly-used LLM-based modules for decision making: goal interpretation, subgoal decomposition, action sequencing, and transition modeling, and 3) a collection of fine-grained metrics which break down evaluation into various types of errors, such as hallucination errors, affordance errors, various types of planning errors, etc. Overall, our benchmark offers a comprehensive assessment of LLMs' performance for different subtasks, pinpointing the strengths and weaknesses in LLM-powered embodied AI systems, and providing insights for effective and selective use of LLMs in embodied decision making.

• The paper presents the Embodied Agent Interface (EAI), a framework that employs Linear Temporal Logic for consistent measurement in LLM-based decision making.
• It categorizes the decision-making process into modules such as goal interpretation, subgoal decomposition, action sequencing, and transition modeling.
• Empirical results on BEHAVIOR and VirtualHome benchmarks highlight variabilities in model performance and the need for enhanced logical reasoning in complex tasks.

Benchmarking LLMs for Embodied Decision Making: Evaluating the Embodied Agent Interface

The use of LLMs in decision-making for embodied agents presents significant opportunities, yet these advancements bring alongside multifaceted challenges and limitations. The paper, "Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making," proposes a systematic framework—the Embodied Agent Interface (EAI)—aimed at addressing the gaps in evaluating LLMs within such environments. The objective is to provide a standardized, metric-driven approach to understanding LLM performance, primarily focusing on embodied decision-making tasks facilitated through interfaces that manage environmental interactions.

Key Contributions

The proposed EAI framework introduces a structured method that unifies the evaluation process under a cohesive lens, emphasizing three primary areas:

1. Standardized Goal Specifications: By employing Linear Temporal Logic (LTL) as a common language, the EAI presents a way to uniformly measure task specifications that extend over different environments and modules, enhancing interpretability.
2. Structured Interface and Modules: It categorizes the embodied decision-making process into four fundamental LLM-based modules:
   • Goal Interpretation
   • Subgoal Decomposition
   • Action Sequencing
   • Transition Modeling These categories function as independent metrics to evaluate specific facets of decision-making.
3. Comprehensive Evaluation Metrics: The EAI identifies detailed errors such as hallucinations, affordance errors, and sequence planning issues. This fine-grained diagnostic ability presents deeper insights into the functional efficiencies and limitations of LLMs dealing with embodied tasks.

Empirical Findings

The Interface is applied across two major benchmarks—BEHAVIOR and VirtualHome—where it assesses 15 different LLMs. The experiments shed light on the model’s robustness, trend emergence, and discrete error classification:

• Module Performance: The analysis reveals variability in LLM capabilities, where models like o1-preview consistently deliver superior performance across different evaluation modules, highlighting the importance of reasoning tokens and prediction accuracy.
• Task Complexity Dependency: There is a marked inverse correlation between trajectory evaluation performance and task complexity. This highlights that as tasks become more complex, the LLMs generally struggle to maintain accurate and feasible planning.
• Error Analysis: Errors in runtime, particularly missing steps and incorrect action sequencing, underscore challenges in cognitive reasoning. These errors manifest prominently in intricate, goal-dependent tasks, reflecting that substantial improvement is necessary in internal logic processing and world state understanding.

Implications for Future Development

The research directs multiple future endeavors, primarily in the areas of LLM integration and extension:

• Multimodal Integration: Bridging LLM tasks with Visual-LLMs is a promising route to improve understanding and grounding in real-world contexts, such as refining action sequences with visual feedback.
• Enhanced Logical Frameworks: Adopting more robust logical reasoning frameworks or hybrid models that can handle both abstract and low-level decision-making will be critical for performance improvement.
• Real-World Navigation and Interaction: Extending LTL use-cases in navigation tasks could significantly improve interaction handling skills, key for complex embodied tasks like home assistance or industrial automation.

Conclusion

The Embodied Agent Interface distinctly raises the bar on evaluating LLMs in embodied decision-making by broadening the scope of analysis through structured, logic-based interpretation frameworks. The insights revealed through this systematic approach are foundational in driving future research to overcome limitations in current models, carving the path toward more adept and intelligent embodied digital agents.