Embodied Intelligence: Physical Human–Robot Interaction

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "AI in Robotics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 3786

Special Issue Editor


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Guest Editor
1. Department of Computer Science, UNC at Chapel Hill, Chapel Hill, NC, USA
2. Department of Mechanical Engineering, UC Berkeley, Berkeley, CA, USA
Interests: embodied AI; robotics; vision

Special Issue Information

Dear Colleagues,

Recent advancements in robotics and human–robot interaction (HRI) have led to significant progress in the realm of embodied AI, in which robots demonstrate physical behaviors and engage in complex interactions with humans and the 3D world. This evolving field holds vast potential in industries such as healthcare, manufacturing, and assistive technologies. However, the physical properties of objects, such as friction, soft bodies, and fluids, can significantly affect the precision and adaptability of robotic grasping and manipulation, especially in unstructured environments. Additionally, many existing physics-based grasping techniques rely heavily on single-forward prediction, in which a predetermined grasping pose is executed without real-time exploration or feedback. To address these limitations, it is critical to explore how robots can move beyond predefined motion tasks and intelligently adapt to dynamic conditions using physical sensors such as tactile sensors.

This Special Issue welcomes the submission of papers that explore cutting-edge approaches to embodied AI within the scope of physical human–robot interaction, intelligent grasping, exploration and feedback mechanisms. Submissions that integrate robot learning, embodied planning, vision-language understanding, tactile sensing, adaptive control strategies, and iterative feedback loops to enhance the manipulation capabilities of robotics are highly encouraged. We also welcome theoretical studies that delve into the intersection of embodied AI, dynamics, cognitive science, and interactive systems, as well as real-world applications in healthcare robotics, industrial automation, and beyond.

Dr. Mingyu Ding
Guest Editor

Manuscript Submission Information

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Keywords

  • robotics
  • cognitive robotics
  • robot learning
  • embodied AI
  • tactile sensors and feedback
  • physics-based grasping
  • physical inference
  • human–robot interaction
  • physical interaction
  • vision-language understanding
  • soft object manipulation

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Published Papers (2 papers)

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Research

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28 pages, 4319 KiB  
Article
Agentic Workflows for Improving Large Language Model Reasoning in Robotic Object-Centered Planning
by Jesus Moncada-Ramirez, Jose-Luis Matez-Bandera, Javier Gonzalez-Jimenez and Jose-Raul Ruiz-Sarmiento
Robotics 2025, 14(3), 24; https://doi.org/10.3390/robotics14030024 - 24 Feb 2025
Viewed by 2048
Abstract
Large Language Models (LLMs) provide cognitive capabilities that enable robots to interpret and reason about their workspace, especially when paired with semantically rich representations like semantic maps. However, these models are prone to generating inaccurate or invented responses, known as hallucinations, that can [...] Read more.
Large Language Models (LLMs) provide cognitive capabilities that enable robots to interpret and reason about their workspace, especially when paired with semantically rich representations like semantic maps. However, these models are prone to generating inaccurate or invented responses, known as hallucinations, that can produce an erratic robotic operation. This can be addressed by employing agentic workflows, structured processes that guide and refine the model’s output to improve response quality. This work formally defines and qualitatively analyzes the impact of three agentic workflows (LLM Ensemble, Self-Reflection, and Multi-Agent Reflection) on enhancing the reasoning capabilities of an LLM guiding a robotic system to perform object-centered planning. In this context, the LLM is provided with a pre-built semantic map of the environment and a query, to which it must respond by determining the most relevant objects for the query. This response can be used in a multitude of downstream tasks. Extensive experiments were carried out employing state-of-the-art LLMs and semantic maps generated from the widely-used datasets ScanNet and SceneNN. The results show that agentic workflows significantly enhance object retrieval performance, especially in scenarios requiring complex reasoning, with improvements averaging up to 10% over the baseline. Full article
(This article belongs to the Special Issue Embodied Intelligence: Physical Human–Robot Interaction)
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Review

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38 pages, 5906 KiB  
Review
Perception and Computation for Speed and Separation Monitoring Architectures
by Odysseus Adamides, Karthik Subramanian, Sarthak Arora and Ferat Sahin
Robotics 2025, 14(4), 41; https://doi.org/10.3390/robotics14040041 - 31 Mar 2025
Viewed by 846
Abstract
Human–Robot Collaboration (HRC) has been a significant research topic within the Industry 4.0 movement over the past decade. The interest in HRC research has continued on with the dawn of Industry 5.0 focusing on worker experience. Within the study of HRC, the collaboration [...] Read more.
Human–Robot Collaboration (HRC) has been a significant research topic within the Industry 4.0 movement over the past decade. The interest in HRC research has continued on with the dawn of Industry 5.0 focusing on worker experience. Within the study of HRC, the collaboration approach of Speed and Separation Monitoring (SSM) has been implemented through various architectures. The different configuration strategies involve different perception-sensing modalities, mounting strategies, data filtration, computational platforms, and calibration methods. This paper explores the evolution of the perception architectures used to perform SSM, and highlights innovations in sensing and processing technologies that can open up the door to significant advancements in this sector of HRC research. Full article
(This article belongs to the Special Issue Embodied Intelligence: Physical Human–Robot Interaction)
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