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LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks

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arxiv 2308.14972 v1 pith:64ZBCMIE submitted 2023-08-29 cs.RO cs.AI

Haokun Liu , Yaonan Zhu , Kenji Kato , Izumi Kondo , Tadayoshi Aoyama , Yasuhisa Hasegawa This is my paper · ORCID
classification cs.ROcs.AI
keywords collaborationcommandshuman-robotlanguagellm-basedmanipulationsystemaction
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents a novel approach to enhance autonomous robotic manipulation using the Large Language Model (LLM) for logical inference, converting high-level language commands into sequences of executable motion functions. The proposed system combines the advantage of LLM with YOLO-based environmental perception to enable robots to autonomously make reasonable decisions and task planning based on the given commands. Additionally, to address the potential inaccuracies or illogical actions arising from LLM, a combination of teleoperation and Dynamic Movement Primitives (DMP) is employed for action correction. This integration aims to improve the practicality and generalizability of the LLM-based human-robot collaboration system.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Attention at Rest Stays at Rest: Breaking Visual Inertia for Cognitive Hallucination Mitigation

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    Visual attention in MLLMs shows inertia that hinders cognitive inference on object relations, addressed by a training-free Inertia-aware Visual Excitation method that selects dynamically emerging tokens and applies an...

  2. A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A position paper arguing that LLM-based human-agent systems, not fully autonomous agents, should be the immediate goal for AI development.

  3. LA-RCS: LLM-Agent-Based Robot Control System

    cs.RO 2025-05 reject novelty 4.0 of 10

    LA-RCS reports that a dual-agent LLM system controls a small car robot to complete 18 of 20 self-designed commands with the GPT-4o variant, but the supporting evaluation is inconsistent and not reproducible.

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