Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Toward Embodied Intelligence: State-of-the-Art in Sensing, Decision-Making and Control Technologies...
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Toward Embodied Intelligence: State-of-the-Art in Sensing, Decision-Making and Control Technologies for Autonomous Robots (2nd Edition)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 4290

Special Issue Editors

Dr. Yuanlong Xie

E-Mail Website
Guest Editor
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: modern electric drive control; robotic drives; parameter identification and condition monitoring of motor drives
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuting Wang

E-Mail Website
Guest Editor
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: method and technology of the digital design associated with CAD/CAE/Optimisation; modern equipment; structural optimisation; intelligent test
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shane Xie

E-Mail Website
Guest Editor
School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK
Interests: robot control; motion control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advantages of noteworthy dexterity, maneuverability, and high efficiency in performing a growing variety of tasks, autonomous robots are becoming increasingly intelligent and complex with the ability to complete difficult operations with the comprehensive utilization of multimodal sensors, embodied intelligence frameworks, and large model-driven controllers. Autonomous robots can act in swarms with cognitive reasoning and offering total flexibility for industrial applications, which have expanded significantly from manufacturing and automation to healthcare, agriculture, and human–robot collaboration. This Special Issue aims to provide up-to-date research concepts, theoretical findings, and practical solutions for autonomous robots in relation to AI-augmented perception, embodied decision-making, and adaptive motion control, with a focus on sensorimotor coordination, human–robot value alignment, and safety-critical autonomy. We invite contributions that bridge theoretical advances and practical deployments, aiming to redefine the boundaries of autonomous robotics in the era of embodied intelligence.

Dr. Yuanlong Xie
Prof. Dr. Shuting Wang
Prof. Dr. Shane Xie
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • kinematic and dynamic modelling and parameter identification
  • learning-based perception, recognition, navigation, mapping, and localization
  • generative AI-driven perception and multimodal sensor fusion
  • understanding of intelligent decisions, cooperation, environments, and situations
  • AI-driven multi-agent decision-making (e.g., reinforcement learning, meta-learning)
  • large language model (LLM)-guided task planning and cognitive reasoning
  • foundation models for embodied intelligence and robot-environment interaction
  • mobile robot manipulation and in-wheel-driven techniques
  • cooperative control of multiple autonomous systems
  • coexisting–cooperative–cognitive technologies
  • autonomous levels of unmanned systems
  • robot optimal control, adaptive control, and system optimization
  • model-based and model-free reinforcement learning for adaptive control

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

27 pages, 6347 KB  
Article
Uncertainty-Calibrated Safety Gating for Vision–Language– Action Manipulation Under Domain Shift: Reliability Gains and Intervention–Efficiency Trade-Offs
by Atef M. Ghaleb, Ali S. Allahloh, Sobhi Mejjaouli, Mohammed A. H. Ali and Adel Al-Shayea
Sensors 2026, 26(10), 3140; https://doi.org/10.3390/s26103140 - 15 May 2026
Abstract
Vision–Language–Action (VLA) policies promise flexible long-horizon manipulation, but deployment under domain shift requires both reliable uncertainty estimates and a workable runtime-assurance policy. We study a model-agnostic uncertainty-calibrated safety-gating wrapper that estimates online failure risk and routes control among policy execution, pause-and-reobserve, and a [...] Read more.
Vision–Language–Action (VLA) policies promise flexible long-horizon manipulation, but deployment under domain shift requires both reliable uncertainty estimates and a workable runtime-assurance policy. We study a model-agnostic uncertainty-calibrated safety-gating wrapper that estimates online failure risk and routes control among policy execution, pause-and-reobserve, and a fallback planner. Using a cleaned and consistently aggregated benchmark pipeline, we evaluate two long-horizon manipulation tasks in NVIDIA Isaac Sim 5.0 under lighting, texture, occlusion, sensor, and combined shifts. Relative to an ungated VLA baseline, calibrated gating improves mean shifted success from 57.5% to 77.2% and reduces aggregate expected calibration error from 0.303 to 0.100. The largest success gains occur under occlusion and combined shift, including improvements from 48.3% to 85.2% on the drawer task and from 59.4% to 87.8% on clutter sort. The results also expose a systems trade-off: an aggressive uncalibrated threshold baseline attains stronger raw success and collision metrics, but requires nearly twice as many interventions per shifted episode (21.6 vs. 11.5). The main contribution is, therefore, an empirical characterization of the reliability–intervention trade-off created by calibrated supervision, not a claim that the calibrated supervisor is universally the best terminal controller. We frame calibrated gating as a better-calibrated, lower-intervention supervisor that materially improves robustness relative to an ungated VLA while revealing the open problem of mapping calibrated risk into efficient intervention policies. Additional threshold-sensitivity, signal-diagnostic, overhead, and residual-failure analyses show that the selected operating point is meaningful but not universal: the calibrated risk threshold captures most shifted failures in retrospective logs, yet residual contacts still arise during pause and fallback states. These findings provide controlled simulation evidence for trustworthy VLA supervision under distribution shift and clarify the reliability–intervention frontier that future embodied-control systems must navigate. Full article
(This article belongs to the Special Issue Toward Embodied Intelligence: State-of-the-Art in Sensing, Decision-Making and Control Technologies for Autonomous Robots (2nd Edition))
►▼ Show Figures

Figure 1

29 pages, 12586 KB  
Article
Hardware-Agnostic Imitation Learning Method for Autonomous Ultrasound Scanning Addressing Physical Deployment Discrepancies
by Zhuoyang Ma, Jing Xia, Hong Gao, Hongbo Zhu and Yongkang Tang
Sensors 2026, 26(9), 2804; https://doi.org/10.3390/s26092804 - 30 Apr 2026
Viewed by 304
Abstract
To achieve autonomous ultrasound scanning skill transfer across different physical equipment instances and address the limitations of traditional imitation learning methods—which struggle with cross-instance generalization due to their reliance on specific manipulator parameters—this study proposes a physical-parameter-decoupled imitation learning method based on waypoint [...] Read more.
To achieve autonomous ultrasound scanning skill transfer across different physical equipment instances and address the limitations of traditional imitation learning methods—which struggle with cross-instance generalization due to their reliance on specific manipulator parameters—this study proposes a physical-parameter-decoupled imitation learning method based on waypoint representation. This approach utilizes a greedy algorithm to automatically extract key nodes within the task space from expert demonstration trajectories, constructing a trajectory representation decoupled from low-level kinematic parameters and base calibration errors. Simultaneously, a velocity-aware adaptive error precision adjustment mechanism is introduced to dynamically modulate waypoint extraction thresholds, simulating the speed-accuracy strategies employed by sonographers across different scanning phases. Cross-validation across two mainstream generative architectures—Action Chunking Transformer (ACT) and Diffusion Policy—on an offline dataset confirms the plug-and-play capability of waypoint representation in suppressing long-horizon error accumulation, with both architectures achieving significant reductions in prediction errors. For physical deployment, a complete ACT-waypoint system featuring low-level triple safety redundancy was validated. In kidney long-axis standard plane scanning tasks, the system achieved a 92% success rate on the source domain manipulator and maintained an 84% success rate on the target deployment manipulator, despite incompatible low-level kinematic parameters and base coordinates. Force control accuracy remained stable around the target value of 12 N. The results demonstrate that the proposed method effectively overcomes base coordinate and D-H parameter discrepancies to achieve cross-instance zero-shot skill transfer, significantly enhancing the adaptability across physical instances and the scanning success rate of imitation learning models. Full article
(This article belongs to the Special Issue Toward Embodied Intelligence: State-of-the-Art in Sensing, Decision-Making and Control Technologies for Autonomous Robots (2nd Edition))
►▼ Show Figures

Figure 1

25 pages, 40263 KB  
Article
Autonomous Navigation of Mobile Robots: A Hierarchical Planning–Control Framework with Integrated DWA and MPC
by Zhongrui Wang, Shuting Wang, Yuanlong Xie, Tifan Xiong and Chao Wang
Sensors 2025, 25(7), 2014; https://doi.org/10.3390/s25072014 - 23 Mar 2025
Cited by 9 | Viewed by 3052
Abstract
In human–robot collaborative environments, the inherent complexity of shared operational spaces imposes dual requirements on process safety and task execution efficiency. To address the limitations of conventional approaches that decouple planning and control modules, we propose a hierarchical planning–control framework. The proposed framework [...] Read more.
In human–robot collaborative environments, the inherent complexity of shared operational spaces imposes dual requirements on process safety and task execution efficiency. To address the limitations of conventional approaches that decouple planning and control modules, we propose a hierarchical planning–control framework. The proposed framework explicitly incorporates path tracking constraints during path generation while simultaneously considering path characteristics in the control process. The framework comprises two principal components: (1) an enhanced Dynamic Window Approach (DWA) for the local path planning module, introducing adaptive sub-goal selection method and improved path evaluation functions; and (2) a modified Model Predictive Control (MPC) for the path tracking module, with a curvature-based reference state online changing strategy. Comprehensive simulation and real-world experiments demonstrate the framework’s operational advantages over conventional methods. Full article
(This article belongs to the Special Issue Toward Embodied Intelligence: State-of-the-Art in Sensing, Decision-Making and Control Technologies for Autonomous Robots (2nd Edition))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop