Affiliated with the Intelligent Autonomous Systems (IAS) Society

Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems.

Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.Benefits to authors
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Special Issue on Planning and Learning for Autonomous Robotics
截稿日期: 2025-01-15

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 (Executive Guest Editor)
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