Call for Papers: Special Session on KR and Robotics
In recent years, the fast-paced evolution of the Artificial Intelligence and Robotics fields has facilitated the development of scalable and cost-effective robotic solutions. Indeed, autonomous agents have been deployed in many scenarios, which include both industrial and manufacturing contexts, as well as applications in the tertiary sector. Most recently, there has also been increased interest in integrating robots within a broader Smart City environment as well as in Healthcare and Assistive scenarios. However, to operate reliably in the real world, robots need high-level cognitive skills: e.g., advanced motor skills, representations of the world around them, representations of the users they interact with, decisional autonomy, task planning, problem solving, interaction capabilities grounded on sensory modalities, and sophisticated sensemaking skills, to name just a few.
In addition, while the success of data-driven paradigms, namely of Machine Learning and Deep Learning methods, has magnified the robots' ability to recognize patterns from the perceptual information collected through their sensors, much more work is needed to go from pattern recognition to high-level cognition and sensemaking. To this purpose, robots also need access to knowledge representations that are more comprehensive and explainable than those embedded in data-driven methods. They need knowledge about their capabilities and features of the environment (e.g., human users, other robots, devices, etc.) in order to characterize and understand the relationships between their internal structures and the environment. Analogously, when interacting with humans, robots should be endowed with some kind of common sense or "human-level" knowledge in order to properly evaluate, for example, social norms or expected affordances of objects in the environment.
Furthermore, they also need robust mechanisms to reason on these knowledge representations. A key requirement, in this context, is ensuring that knowledge representations and knowledge-based reasoning techniques are suitable for robotic applications.
Information for Authors
The Special Session on KR & Robotics will allow contributions of both regular papers (9 pages) and short papers (4 pages), excluding references, prepared and submitted according to the authors guidelines detailed on the submission page.
The special session emphasizes KR & Robotics, and welcomes contributions that extend the state of the art at the intersection of KR & Robotics. Therefore, KR-only or Robotics-only submissions will not be accepted for evaluation in this special session.
Submissions will be rigorously peer reviewed by PC members who are active in KR & Robotics. They will be evaluated on the basis of the overall quality of their scientific contribution, including criteria such as originality, soundness, relevance, significance, quality of presentation, and awareness of the state of the art.
Special Session on KR & Robotics Chairs
- Submission of title and abstract: February 2, 2022
- Paper submission deadline:
February 9, 2022February 11, 2022 (strict deadline)
- Author response period: March 29-31, 2022
- Author notification: April 15, 2022
- Camera-ready papers: May 7, 2022
- Conference: July 31 - August 5, 2022
This special session welcomes contributions at the intersection of Knowledge Representation and Robotics. We solicit papers which extend knowledge representation and reasoning methods to address the challenges faced by robots operating in the real world. Themes of interest to this session include, but are not limited to:
- Reasoning with different sensory modalities;
- Sensor interpretation and continuous data streams in robotic scenarios;
- Reasoning with time and space;
- Grounding representations in the physical world;
- Time-valid representations and handling change;
- Dealing with uncertain, incomplete or contradictory information;
- Detecting and handling errors and anomalies;
- Reasoning with bounded computational resources;
- Modelling different types of robot intelligence (social, affective, visual, and others);
- Human-Robot Interaction;
- Cognitive Architectures for Robotics;
- Ontology Engineering for Robotics;
- Knowledge Graphs for Robotics;
- Representing implicit knowledge (commonsense, plausibility, typicality) for use in robotics applications;
- Applying general-purpose knowledge-bases to scenarios in robotics;
- Combining quantitative and qualitative knowledge representations;
- Integrating different computational methods– e.g., data-driven and knowledge-driven;
- Integrating symbolic and sub-symbolic approaches;
- Explainable and transparent robot behaviours;
- Reasoning for deliberation and decision-making;
- Reasoning for planning and task allocation;
- Combining reasoning with control theories;
- Causal reasoning in robotics applications;
- Orchestrating Multi-Robot Systems.