Research on Deep Learning and Human-Robot Collaboration
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (15 January 2026) | Viewed by 2768
Special Issue Editors
Interests: industrial AI; machine learning; deep learning; computer vision; human–robot collaboration
Special Issues, Collections and Topics in MDPI journals
Interests: UAV; AUV; underwater gliders; navigation and attitude; fuzzy logic system
Interests: active perception; scene understanding; field robotics
Special Issues, Collections and Topics in MDPI journals
Interests: robot manipulation; robot learning; task and motion planning
Special Issues, Collections and Topics in MDPI journals
Interests: human-computer interaction; virtual and augmented reality; tangible user interfaces; interaction techniques; interaction design
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on advancements in deep learning and its transformative role in enabling human–robot collaborations (HRCs). Emphasizing the integration of machine learning models with robotic systems, we explore how deep learning techniques can enhance robotic perception, decision-making, and interaction for seamless and intelligent collaboration with humans in diverse settings.
This Special Issue will cover a wide array of topics including, but not limited to, the following:
- Deep learning for robotic perception: Object recognition, scene understanding, and real-time pose an estimation for human-robot interaction.
- HRC for intelligent manufacturing: Collaborative robots (cobots) that work alongside humans in dynamic environments by using speech, gesture, and gaze recognition.
- Learning-driven control and adaptation: Reinforcement learning and domain adaptation techniques for optimizing robot behavior in unstructured or changing environments.
- Multi-modal interaction: Integration of vision, speech, and sensor data to enhance the intuitiveness and efficiency of HRC systems.
- Safety and trust in HRC: Using deep learning to model human intent, ensure safety, and foster trust in human–robot interactions.
This Special Issue aims to advance the field by highlighting how deep learning can bridge gaps between human cognition and robotic systems, fostering smoother collaboration. By showcasing innovative methods and real-world applications, it seeks to inspire new research directions and encourage interdisciplinary collaboration across robotics, AI, and human factor engineering.
Moreover, this Special issue will complement the existing literature by providing fresh perspectives on the integration of deep learning into human–robot collaboration. It expands on traditional approaches by incorporating emerging technologies such as transfer learning, temporal action recognition, and multi-modal fusion for HRC. Moreover, it addresses critical challenges like adaptability, safety, and real-time interactions in robotics. The contributions to this Special Issue are expected to push boundaries, offering insights into designing more robust, intelligent, and human-centered collaborative systems.
Dr. Haodong Chen
Dr. Enrico Petritoli
Dr. Liang Lu
Dr. Peng Zhou
Dr. Jorge C. S. Cardoso
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.
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Keywords
- gesture recognition
- gaze estimation
- speech recognition
- multi-modal interaction
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