About this Research Topic

Manuscript Submission Deadline 31 October 2022

Humans can learn new skills and recognize new things quickly from a small number of data points. This could be attributed to our ability to have multiple levels of abstractions when it comes to understanding the same concept. For instance, using affordance perception, humans can easily recognize if a cuboid can be sat on even if they have never seen it or used it before. Similarly, humans can precisely predict the trajectory of a moving ball by perceiving and predicting the physical laws. Can robots use similarly layered cognitive systems to learn efficiently? Recent progress has been made in this area, but there are still many unsolved problems for efficient robot cognition and learning.

The aim of this Research Topic is to collect comprehensive updates and high-quality practice regarding machine-learning-based robot cognition. Hierarchical cognition in robotics includes high-level affordances, intermediate-level human or robot language, and low-level prediction and recognition of physical equations (matching or learning an observed phenomenon with known physical laws). The goal is to integrate language, affordances, and physics-based inductive biases and representations into robot learning pipelines. Machine learning provides a common framework for jointly learning missing parameters from real-world data. An affordance allows collecting action possibilities, enabling fast discovery and learning the environment, often from one or a small number of observations. In addition, natural language provides a simple and promising approach for robotic communication and cognition tasks. Alternatively, robotic learning combined with physic-informed information allows robots to extrapolate from a small number of samples.

The Research Topic seeks contributions of Original Research, Systematic Review, Data Report, Methods, and Review. Areas of interest include, but are not limited to:
- Affordance learning and perception in robotics
- Natural language processing in robotics
- Understanding manipulation and tool-discovery affordance
- Learning natural language interfaces of human-robot interaction
- Self-supervised learning for robot affordance and language representation
- Robot language processing
- Physics-informed robot learning
- Machine-learning models combining affordance representation, language processing, and physics-informed priors

Dr. Chen, Dr. Karl, and Elie Aljalbout are affiliated with Volkswagen; Dr. Zeng is affiliated with Google; Dr. Mayol-Cuevas is affiliated with Amazon; Dr. Van Hoof is affiliated with Qualcomm and Elsevier. All other Topic Editors declare no competing interests with regard to the Research Topic subject.

Keywords: Robot learning, Affordance learning, Cognitive robotics, Language Acquisition, Physics-Informed learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Humans can learn new skills and recognize new things quickly from a small number of data points. This could be attributed to our ability to have multiple levels of abstractions when it comes to understanding the same concept. For instance, using affordance perception, humans can easily recognize if a cuboid can be sat on even if they have never seen it or used it before. Similarly, humans can precisely predict the trajectory of a moving ball by perceiving and predicting the physical laws. Can robots use similarly layered cognitive systems to learn efficiently? Recent progress has been made in this area, but there are still many unsolved problems for efficient robot cognition and learning.

The aim of this Research Topic is to collect comprehensive updates and high-quality practice regarding machine-learning-based robot cognition. Hierarchical cognition in robotics includes high-level affordances, intermediate-level human or robot language, and low-level prediction and recognition of physical equations (matching or learning an observed phenomenon with known physical laws). The goal is to integrate language, affordances, and physics-based inductive biases and representations into robot learning pipelines. Machine learning provides a common framework for jointly learning missing parameters from real-world data. An affordance allows collecting action possibilities, enabling fast discovery and learning the environment, often from one or a small number of observations. In addition, natural language provides a simple and promising approach for robotic communication and cognition tasks. Alternatively, robotic learning combined with physic-informed information allows robots to extrapolate from a small number of samples.

The Research Topic seeks contributions of Original Research, Systematic Review, Data Report, Methods, and Review. Areas of interest include, but are not limited to:
- Affordance learning and perception in robotics
- Natural language processing in robotics
- Understanding manipulation and tool-discovery affordance
- Learning natural language interfaces of human-robot interaction
- Self-supervised learning for robot affordance and language representation
- Robot language processing
- Physics-informed robot learning
- Machine-learning models combining affordance representation, language processing, and physics-informed priors

Dr. Chen, Dr. Karl, and Elie Aljalbout are affiliated with Volkswagen; Dr. Zeng is affiliated with Google; Dr. Mayol-Cuevas is affiliated with Amazon; Dr. Van Hoof is affiliated with Qualcomm and Elsevier. All other Topic Editors declare no competing interests with regard to the Research Topic subject.

Keywords: Robot learning, Affordance learning, Cognitive robotics, Language Acquisition, Physics-Informed learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Topic Editors

Loading..

Topic Coordinators

Loading..

articles

Sort by:

Loading..

authors

Loading..

views

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..

Share on

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.