A closed-loop multi-agent LLM framework enables heterogeneous robots to collaboratively manipulate objects by decomposing tasks, grounding actions via visual tools, and recovering from execution failures hierarchically.
Ramchurn, and Mohammad D
4 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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abstract
Embodied robots which can interact with their environment and neighbours are increasingly being used as a test case to develop Artificial Intelligence. This creates a need for multimodal robot controllers that can operate across different types of information, including text. Large Language Models are able to process and generate textual as well as audiovisual data and, more recently, robot actions. Language Models are increasingly being applied to robotic systems; these Language-Based robots leverage the power of language models in a variety of ways. Additionally, the use of language opens up multiple forms of information exchange between members of a human-robot team. This survey motivates the use of language models in robotics, and then delineates works based on the part of the overall control flow in which language is incorporated. Language can be used by human to task a robot, by a robot to inform a human, between robots as a human-like communication medium, and internally for a robot's planning and control. Applications of language-based robots are explored, and numerous limitations and challenges are discussed to provide a summary of the development needed for the future of language-based robotics.
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EDITH combines egocentric vision and gaze from smart glasses with language in a hierarchical policy to let robots interpret brief nonverbal human intent and reduce user effort in interactive tasks.
A neuro-symbolic system pairing LLMs for symbolic reasoning with neural delta controllers for execution delivers over 70% step reduction and up to 8.83x speedup in language-guided planar object manipulation while remaining robust to LLM quality.
A survey that categorizes LLM uses in multi-robot systems across task allocation, motion planning, action generation, and human interaction, while noting challenges and future research opportunities.
citing papers explorer
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A Closed-Loop Multi-Agent Framework for Robust Multi-Robot Manipulation
A closed-loop multi-agent LLM framework enables heterogeneous robots to collaboratively manipulate objects by decomposing tasks, grounding actions via visual tools, and recovering from execution failures hierarchically.
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Hierarchical Policies from Verbal and Egocentric Human Signals for Natural Human-Robot Interaction
EDITH combines egocentric vision and gaze from smart glasses with language in a hierarchical policy to let robots interpret brief nonverbal human intent and reduce user effort in interactive tasks.
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Neuro-Symbolic Control with Large Language Models for Language-Guided Spatial Tasks
A neuro-symbolic system pairing LLMs for symbolic reasoning with neural delta controllers for execution delivers over 70% step reduction and up to 8.83x speedup in language-guided planar object manipulation while remaining robust to LLM quality.
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Large Language Models for Multi-Robot Systems: A Survey
A survey that categorizes LLM uses in multi-robot systems across task allocation, motion planning, action generation, and human interaction, while noting challenges and future research opportunities.