mlsanityData to 7 Oct 2026

HARMONIC: Cognitive and Control Collaboration in Human-Robotic Teams

arXiv 2409.18047 ↗First posted 26 Sep 2024cs.RO cs.AI cs.MAcited by 2

Not on the map. Why?

Abstract (arXiv)

This paper describes HARMONIC, a cognitive-robotic architecture that integrates the OntoAgent cognitive framework with general-purpose robot control systems applied to human-robot teaming (HRT). HARMONIC incorporates metacognition, meaningful natural language communication, and explainability capabilities required for developing mutual trust in HRT. Through simulation experiments involving a joint search task performed by a heterogeneous team of two HARMONIC-based robots and a human operator, we demonstrate heterogeneous robots that coordinate their actions, adapt to complex scenarios, and engage in natural human-robot communication. Evaluation results show that HARMONIC-based robots can reason about plans, goals, and team member attitudes while providing clear explanations for their decisions, which are essential requirements for realistic human-robot teaming.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 49 references that are in the map. First posted on arXiv 26 Sep 2024.

Builds on

13 of 49 references.

References · 13

  1. 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 2,993
  2. 2023Open X-Embodiment: Robotic Learning Datasets and RT-X Modelscited by 849
  3. 2023Generalized Planning in PDDL Domains with Pretrained Large Language Modelscited by 139
  4. 2017Behavior Trees in Robotics and AI: An Introductioncited by 105
  5. 2023Plug in the Safety Chip: Enforcing Constraints for LLM-driven Robot Agentscited by 67
  6. 2024Grounding LLMs For Robot Task Planning Using Closed-loop State Feedbackcited by 25
  7. 2013Practical Verification of Decision-Making in Agent-Based Autonomous Systemscited by 11
  8. 2021On the Implementation of Behavior Trees in Roboticscited by 9
  9. 2014Planning for Decentralized Control of Multiple Robots Under Uncertaintycited by 7
  10. 2022KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multi-Robot Systemscited by 6
  11. 2020Impact of Heterogeneity in Multi-Robot Systems on Collective Behaviors Studied Using a Search and Rescue Problemcited by 2
  12. 2022Heterogeneous Full-body Control of a Mobile Manipulator with Behavior Treescited by 2
1 more
  1. 2023IKT-BT: Indirect Knowledge Transfer Behavior Tree Framework for Multi-Robot Systems Through Communication Eavesdroppingcited by 2

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.