Two Heads Are Better Than One: Collaborative LLM Embodied Agents for Human-Robot Interaction
With the recent development of natural language generation models - termed as large language models (LLMs) - a potential use case has opened up to improve the way that humans interact with robot assistants. These LLMs should be able to leverage their large breadth of understanding to interpret natural language commands into effective, task appropriate and safe robot task executions. However, in reality, these models suffer from hallucinations, which may cause safety issues or deviations from the task. In other domains, these issues have been improved through the use of collaborative AI systems where multiple LLM agents can work together to collectively plan, code and self-check outputs. In this research, multiple collaborative AI systems were tested against a single independent AI agent to determine whether the success in other domains would translate into improved human-robot interaction performance. The results show that there is no defined trend between the number of agents and the success of the model. However, it is clear that some collaborative AI agent architectures can exhibit a greatly improved capacity to produce error-free code and to solve abstract problems.
Code (0)
등록된 구현이 없습니다.
Tasks
AI AgentText GenerationSimilar Papers 제목 키워드 기반
EMAC+: Embodied Multimodal Agent for Collaborative Planning with VLM+LLM
Although LLMs demonstrate proficiency in several text-based reasoning and planning tasks, their implementation in robotics control is constrained by significant deficiencies: (1) LLM agents are designed to work mainly wi…
Collaborative Tree Search for Enhancing Embodied Multi-Agent Collaboration
Embodied agents based on large language models (LLMs) face significant challenges in collaborative tasks, requiring effective communication and reasonable division of labor to ensure efficient and correct task comple…
Evaluating Generative Models as Interactive Emergent Representations of Human-Like Collaborative Behavior
Human-AI collaboration requires AI agents to understand human behavior for effective coordination. While advances in foundation models show promising capabilities in understanding and showing human-like behavior, their a…
Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments
Large language models (LLMs) possess extensive knowledge bases and strong reasoning capabilities, making them promising tools for complex, multi-agent planning in embodied environments. However, despite LLMs' advanced ab…
Multi-agent Reinforcement LearningMindForge: Empowering Embodied Agents with Theory of Mind for Lifelong Collaborative Learning
Contemporary embodied agents, such as Voyager in Minecraft, have demonstrated promising capabilities in open-ended individual learning. However, when powered with open large language models (LLMs), these agents often str…
MinecraftTransfer Learning