The AIRO research group is promoting a special issue on the topic "Planning and Learning for Autonomous Robotics" in the Journal of Robotics and Autonomous System.
The special issue aims to showcase recent advances in planning and learning methods for intelligent robots. Planning, learning and their synergistic combination are crucial across several areas of robotics research and pervasively exploited at various levels of robot architecture. The combination of these techniques is instrumental in shaping robot intelligence, enabling them to plan complex action sequences and acquire new tasks or action selection strategies.
As we expect robots to leave lab and take part in our everyday lives, a major advance in artificial intelligence techniques is expected to overcome intrinsic limitations of current autonomy and capabilities to interact with the environment, humans, and other agents. In this respect, planning and learning methodologies are expected to play a key role in supporting both task-oriented behaviors and continuous and incremental adaptation. The special issue seeks to consolidate recent strides in planning and learning for autonomous robotics, fostering the evolution of this critical research domain.
The scientific relevance of the special issue is related to how complementary areas of AI research, such as planning and learning, can be exploited and combined to support long-range autonomy, complex task learning, execution and human-robot interaction in real-world domains.
Topics:
Reinforcement Learning and Planning for Robot Autonomy
Heuristics for Robot Planning and Learning
Safe and Risk-aware Learning and Planning in Robotics
Planning and Learning for Explainable Robotics
Neuro-Symbolic Methods for Learning and Planning in Robotics
LLM Methods for Planning and Execution
Continual Learning and Execution for Autonomous Robots
Planning and execution under Uncertainty in Robotics
Task and Motion Planning for Autonomous Robots
Markov Models for Robot Planning and Control
Learning from Demonstrations
Knowledge Representation for Planning and Transfer Learning
Planning and Learning for Active Perception
Adaptive Multi-Agent Coordination
Inductive Learning for Robotics
Guest editors:
Assist. Prof. Alberto Castellini
Università di Verona, Verona, Italy
Email: alberto.castellini@univr.it
Areas of Expertise: Artificial Intelligence, Machine Learning and Data Analysis for intelligent systems
Prof. Salvatore Anzalone
Université Paris 8, Saint Denis, Paris, France
Email: sanzalone@univ-paris8.fr
Areas of Expertise: Social Robotics, Machine Learning, Computer Vision, Artificial Intelligence
Dr. Gloria Beraldo
Consiglio Nazionale delle Ricerche, CNR, Rome, Italy
Email: gloria.beraldo@istc.cnr.it
Areas of Expertise: human-robot interaction, shared control and shared autonomy, telepresence robots, neurorobotics, socially assistive robotics, and intelligent systems
Assoc. Prof. Alberto Finzi
Università di Napoli "Federico II", Naples, Italy
Email: alberto.finzi@unina.it
Areas of Expertise: cognitive robotics, autonomous robots, human-robot interaction, robot learning, robot planning and execution, executive and cognitive control, multiagent systems
Prof. Enrico Pagello
Università di Padova, Padua, Italy
Email: enrico.pagello@unipd.it
Areas of Expertise: the application of A.I. to Robotics, in particular for Robot Programming Languages, Task and Motion Planning, Multi-robot Systems, Cloud Robotics, and Industrial Manufacturing domains
Assoc. Prof. Fabio Patrizi
Sapienza, Università di Roma, Italy
Email: patrizi@diag.uniroma1.it
Areas of Expertise: theoretical, methodological, and practical aspects in different areas of Computer Science and Artificial Intelligence, such as Formal Methods, Knowledge Representation, Reasoning about Action, nonstandard forms of Planning, Service-oriented Computing, Business Processes
Manuscript submission information:
Important dates:
Deadline for the first submission: January 15th, 2025
First review round completed: June 15th, 2025
Deadline for revised manuscripts due: September 15th, 2025
Final notification of acceptance: December 15th, 2025
Notice: Papers can be submitted even before the deadline of January 15th, 2025. Papers submitted earlier will get reviews, final notification, and possible publication in advance.
Contributed papers must be submitted via the Robotics and Autonomous Systems online submission system: https://www.editorialmanager.com/robot/default.aspx. Please select the article type “VSI: Planning & Learning” when submitting the manuscript online.
Please refer to the Guide for Authors (https://www.sciencedirect.com/journal/robotics-and-autonomous-systems/publish/guide-for-authors) to prepare your manuscript.
For any further information, the authors may contact the Guest Editors.
Keywords:
(Task Planning) OR (Motion Planning) OR (Robot Learning) OR (Planning and Execution) OR (Autonomous Robots)