Generative AI in Embodied Systems: System-Level Analysis of Performance, Efficiency and Scalability (2504.18945v1)

Abstract: Embodied systems, where generative autonomous agents engage with the physical world through integrated perception, cognition, action, and advanced reasoning powered by LLMs, hold immense potential for addressing complex, long-horizon, multi-objective tasks in real-world environments. However, deploying these systems remains challenging due to prolonged runtime latency, limited scalability, and heightened sensitivity, leading to significant system inefficiencies. In this paper, we aim to understand the workload characteristics of embodied agent systems and explore optimization solutions. We systematically categorize these systems into four paradigms and conduct benchmarking studies to evaluate their task performance and system efficiency across various modules, agent scales, and embodied tasks. Our benchmarking studies uncover critical challenges, such as prolonged planning and communication latency, redundant agent interactions, complex low-level control mechanisms, memory inconsistencies, exploding prompt lengths, sensitivity to self-correction and execution, sharp declines in success rates, and reduced collaboration efficiency as agent numbers increase. Leveraging these profiling insights, we suggest system optimization strategies to improve the performance, efficiency, and scalability of embodied agents across different paradigms. This paper presents the first system-level analysis of embodied AI agents, and explores opportunities for advancing future embodied system design.

• The paper reveals that LLM-based planning and communication are major contributors to high latency and computational bottlenecks in embodied AI systems.
• It highlights memory inefficiencies due to retrieval latency and scalability challenges in multi-agent systems where communication bottlenecks reduce task success.
• The study emphasizes the need for efficient processing pipelines, optimization of intra/inter-module synergies, and lighter-weight models for future advancements.

Analysis of System-Level Characteristics in Generative AI for Embodied Systems

The paper "Generative AI in Embodied Systems: System-Level Analysis of Performance, Efficiency, and Scalability" provides an in-depth examination of the architectural and computational aspects of embodied AI systems powered by generative models, primarily focusing on their potential to address complex, real-world tasks. Embodied AI systems integrate perception, cognition, action, and reasoning, often facilitated by LLMs, to perform tasks with extended planning horizons and multiple objectives.

Key Insights and Analysis

This paper categorizes embodied systems into single and multi-agent configurations, each with modularized or end-to-end paradigms, and evaluates system performance across key building blocks: sensing, planning, communication, memory, reflection, and execution. Utilizing a workload suite comprising a variety of embodied AI tasks, the analysis reveals several critical findings regarding system inefficiencies and potential optimization strategies.

System Performance and Inefficiencies

1. Latency and Computational Bottlenecks: The paper reveals that LLM-based planning and communication are the major contributors to the prolonged runtime, often dominating computational resources. The complexities inherent in LLM prompts and the frequent inference runs exacerbate latency issues, which are particularly problematic for real-time applications.
2. Memory and Scalability Challenges: Embodied systems confront memory-related inefficiencies where large memory modules, although beneficial for task success rates, introduce increased retrieval latencies and inconsistencies. Furthermore, scaling multi-agent systems highlights communication bottlenecks and increasing prompt length, which diminishes task success as the number of agents increases.
3. Execution and Planning Balance: The differentiation between high-level planning and execution is essential. While LLMs are proficient in abstract reasoning and high-level planning, they are inefficient at low-level control, necessitating robust execution modules to handle detailed actions in varied environments.
4. System Pipeline Inefficiencies: The stereotypical sequential pipeline of perception, planning, communication, and execution is identified as a source of inefficiency, particularly due to repeated and often redundant processing steps that increase end-to-end task times significantly.

Theoretical and Practical Implications

The findings highlight the need for developing efficient processing pipelines that can handle large agent collaborations without compromising on response times or task success rates. Optimization strategies need to address both intra-module processing inefficiencies, such as those in memory retrieval and token management, and inter-module synergies, notably by refining communication and planning processes.

The introduction of hierarchical and hybrid control models could potentially mitigate the challenges faced in multi-agent systems. For example, reducing redundant communications or employing hierarchical decision-making frameworks may enhance collaboration efficiency in complex environments.

Future Prospects

Future advancements in embodied AI should target the development of lighter-weight models that maintain the reasoning capabilities of larger LLMs while reducing latency and computational overhead. Additionally, enhancing memory systems to provide quicker and more contextually relevant data retrieval could address consistency issues. Emerging areas such as neurosymbolic computing and adaptive learning could offer novel pathways for improving the adaptive capabilities of embodied systems.

Overall, this foundational paper contributes significantly to understanding and improving the sub-components of embodied AI systems, providing a roadmap for the future development of more adaptive, scalable, and efficient embodied agents for real-world applications.