Symmetry-Aware Embodied Intelligence: Foundations and Applications

A Special Issue of Symmetry (ISSN 2073-8994) belonging to the section "A: Computer Science".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1044

Editors


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Guest Editor
School of Mechanical Engineering, Donghua University, Shanghai 201620, China
Interests: human-robot collaboration; sustainable and intelligent manufacutring

E-Mail Website
Guest Editor
Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK
Interests: robotics; manufacturing automation; complex systems

Special Issue Information

Dear Colleagues, 

Embodied intelligence represents a transformative paradigm in artificial intelligence and robotics, focusing on the integration of physical entities with advanced cognitive capabilities to create robotic systems that can perceive, learn from, and interact with their environment in real-time. Embodied intelligence emphasises the connections between physical and cyber systems, enabling machines to adaptively respond to complex real-world challenges through environmental interactions.

Symmetry is a fundamental principle of efficiency, generalisation, and robustness in embodied systems. From the bilateral symmetry of humanoid robots enabling balanced locomotion to the mirror-symmetric action–reward structures in reinforcement learning, from time-reversal symmetry in dynamics modelling to the rotational invariance in visual perception, symmetry underpins the scalability and adaptability of intelligent agents.

This Special Issue explores the scientific foundation and practical applications of embodied intelligence, with a focus on how embodied intelligence evolves from simple automated tools to sophisticated collaborative systems capable of complex reasoning and adaptive behaviour.

Potential topics include but are not limited to the following:

  • Symmetry-driven mechanisms in multi-agent collaboration and collective intelligence;
  • Symmetric perception and behavioural mapping in human–robot interaction;
  • Symmetry-aware foundation models for embodied intelligence;
  • Embodied intelligence for smart manufacturing: scheduling, maintenance, and control;
  • Large language model-based embodied agents for complex task execution;
  • Architectural design and applications of embodied intelligence;
  • Integrated perception and planning for embodied navigation;
  • Embodied navigation and manipulation in complex, dynamic environments;
  • Integrated embodied perception and planning for proactive human–robot interaction;
  • Adaptive and generalisable control strategies for safe physical human–robot interaction;
  • Digital twin modelling for embodied intelligent robots;
  • Multimodal perception fusion for enhanced environmental understanding;
  • Sim-to-real transfer learning in robotic systems;
  • Generative AI for simulation in embodied intelligence;
  • Topological and geometric symmetries in robotic architecture design.

Dr. Jie Li
Dr. Yongjing Wang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • embodied intelligence
  • vision–language–action models
  • generative AI for robotics
  • digital twin in robotics
  • adaptive human–robot interaction
  • multi-agent embodied systems

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Published Papers (1 paper)

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Research

19 pages, 15393 KB  
Article
A Robotic Disassembly Planning Method for Retired Batteries Based on a Long Short-Term Memory Collaborative Framework
by Jie Li, Shuo Zhang, Jiahui Si and Jinsong Bao
Symmetry 2026, 18(6), 981; https://doi.org/10.3390/sym18060981 - 5 Jun 2026
Viewed by 452
Abstract
This paper addresses non-steady-state scenarios in the human–robot collaborative disassembly process of retired power batteries, including component aging, ambiguous instructions, and sensor drift. In such scenarios, the robot exhibits execution robustness problems. This paper proposes a Planning Domain Definition Language (PDDL) generation framework [...] Read more.
This paper addresses non-steady-state scenarios in the human–robot collaborative disassembly process of retired power batteries, including component aging, ambiguous instructions, and sensor drift. In such scenarios, the robot exhibits execution robustness problems. This paper proposes a Planning Domain Definition Language (PDDL) generation framework that integrates long-term and short-term memory. The framework combines large language models with knowledge graphs as a long-term memory module for symbolic task decomposition and domain semantic rule generalization, while using meta-heuristic optimization algorithms as a short-term memory module to adapt and optimize action parameters based on real-time sensor feedback. Through this closed-loop mechanism that combines long-term memory guidance with short-term memory adaptation, the system addresses the limitation of traditional PDDL, which, when facing open, time-varying, and heterogeneous industrial disassembly scenarios, has symbolic action models that have difficulty capturing the uncertainty and unpredictable disturbances in real physical systems, limiting its practicality in complex non-steady-state scenarios. Furthermore, the system establishes a feedback mechanism from short-term memory to long-term memory, enhancing disassembly capabilities in non-steady-state environments by transforming scenario information into supplementary understanding. The research validates this method on a real disassembly platform. Compared with baselines of traditional PDDL, a planning method using only large language models (LLMs), and heuristic algorithms, this method achieved an 88.0% task success rate (significantly superior to the 38.0% of traditional PDDL). Full article
(This article belongs to the Special Issue Symmetry-Aware Embodied Intelligence: Foundations and Applications)
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