mlsanityData to 14 Oct 2026

LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks

arXiv 2308.14972 ↗First posted 29 Aug 2023cs.RO cs.AIcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 2 of its 5 references that are in the map. First posted on arXiv 29 Aug 2023.

Builds on

2 of 5 references.

References · 2

  1. 2022ProgPrompt: Generating Situated Robot Task Plans using Large Language Modelscited by 611
  2. 2023Learning to Reason over Scene Graphs: A Case Study of Finetuning GPT-2 into a Robot Language Model for Grounded Task Planningcited by 16

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 9; 2026: 2.

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  1. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decodingcited by 688
  2. 2023Analyzing and Mitigating Object Hallucination in Large Vision-Language Modelscited by 316
  3. 2025ClearSight: Visual Signal Enhancement for Object Hallucination Mitigation in Multimodal Large language Modelscited by 40
  4. 2024AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Modelscited by 32
  5. 2025LLM-Based Human-Agent Collaboration and Interaction Systems: A Surveycited by 29
  6. 2024Large Language Models for Manufacturingcited by 24
  7. 2024SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Modelscited by 21
  8. 2025The Mirage of Performance Gains: Why Contrastive Decoding Fails to Mitigate Object Hallucinations in MLLMs?cited by 9
  9. 2025A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomycited by 9
  10. 2025SHIELD: Suppressing Hallucinations In LVLM Encoders via Bias and Vulnerability Defensecited by 7
  11. 2025HalluRNN: Mitigating Hallucinations via Recurrent Cross-Layer Reasoning in Large Vision-Language Modelscited by 4
  12. 2026CRoPS: A Training-Free Hallucination Mitigation Framework for Vision-Language Modelscited by 4
5 more
  1. 2024Integrating Large Language Models for UAV Control in Simulated Environments: A Modular Interaction Approachcited by 3
  2. 2026NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priorscited by 3
  3. 2025Hierarchical Language Models for Semantic Navigation and Manipulation in an Aerial-Ground Robotic Systemcited by 2
  4. 2025SAVER: Mitigating Hallucinations in Large Vision-Language Models via Style-Aware Visual Early Revisioncited by 2
  5. 2025LA-RCS: LLM-Agent-Based Robot Control Systemcited by 1