MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation (2411.17636v1)

Abstract: LLMs have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation. While recent efforts in robotics have leveraged LLMs both for high-level and low-level planning, these approaches often face significant challenges, such as hallucinations in long-horizon tasks and limited adaptability due to the generation of plans in a single pass without real-time feedback. To address these limitations, we propose a novel multi-agent LLM framework, Multi-Agent LLM for Manipulation (MALMM) that distributes high-level planning and low-level control code generation across specialized LLM agents, supervised by an additional agent that dynamically manages transitions. By incorporating observations from the environment after each step, our framework effectively handles intermediate failures and enables adaptive re-planning. Unlike existing methods, our approach does not rely on pre-trained skill policies or in-context learning examples and generalizes to a variety of new tasks. We evaluate our approach on nine RLBench tasks, including long-horizon tasks, and demonstrate its ability to solve robotics manipulation in a zero-shot setting, thereby overcoming key limitations of existing LLM-based manipulation methods.

Multi-Agent LLMs for Zero-Shot Robotics Manipulation

The paper "MALLM-Man: Multi-Agent LLMs for Zero-Shot Robotics Manipulation" addresses the challenges in leveraging LLMs for robotic manipulation tasks. While LLMs have demonstrated promising results in planning across domains, their application to robotics faces significant hurdles. These include hallucinations during long-horizon tasks and a lack of real-time adaptability when generating plans in a single pass. This paper introduces a novel framework, MALLM-Man, which employs a multi-agent system to distribute high-level planning and low-level control, thereby addressing these limitations.

Framework and Methodology

MALLM-Man consists of a multi-agent system with distinct roles for each agent: high-level planning, low-level code generation, and supervision of transitional dynamics. The framework diverges from prior LLM-based robotic approaches by focusing on incremental processing with real-time environmental feedback. This design choice enables effective handling of intermediate failures and adaptive re-planning without pre-trained skill policies or examples for in-context learning. By integrating observations after each step of task execution, MALLM-Man seeks to mitigate hallucinations and improve task adaptability.

The primary contributions of the paper include:

• Introduction of the first multi-agent LLM framework capable of generalizing to unseen manipulation tasks without needing in-context learning examples.
• An approach that adapts dynamically to environmental changes, integrating feedback into the planning loop.
• A detailed evaluation demonstrating that MALLM-Man significantly outperforms existing LLM-based methods.

Evaluation and Results

The authors conducted thorough experiments on nine RLBench tasks, all within a zero-shot setting. The results demonstrate that MALLM-Man consistently surpasses other state-of-the-art approaches in solving complex robotics manipulation tasks. Specifically, notable success was observed in long-horizon tasks like block stacking, where handling intermediate feedback proved vital.

The performance of MALLM-Man indicates several advantages over single agent systems, including reduced hallucinations and improved efficiency in action execution. The framework allows more specialized reasoning for each task component, effectively distributing machine learning capabilities across multiple agents.

Implications and Future Work

The introduction of MALLM-Man highlights the potential of multi-agent systems in enhancing the capabilities of LLMs for robotics. By proving that an LLM-based framework can effectively integrate environmental feedback and successfully tackle zero-shot tasks, the researchers open avenues for future research. Potential developments could focus on extending these concepts to physical robots in real-world environments, refining the visual observation component further, and exploring the synergy of LLMs with other AI models to broaden applicability.

The work illustrates the potential of marrying advanced text-based models with robotics hardware, suggesting an impactful direction for AI research. However, understanding the limitations regarding computational costs and the necessity for manual prompt engineering underscores areas requiring further research and development.

In conclusion, the MALLM-Man framework advances the integration of LLMs into the domain of robotics manipulation, providing a structured approach that allows for improved adaptability and efficiency over past methodologies. The paper's results are promising and establish a foundation for further explorations into multi-agent systems and their applications in complex robotic tasks.