American Journal of Computer Science and Technology

Special Issue

Robot Learning to Adapt and Improve in the Real World

  • Submission Deadline: Apr. 15, 2024
  • Status: Open for Submission
  • Lead Guest Editor: Harry Zhang
About This Special Issue
In an era where adaptability and continual learning are paramount for autonomous systems, the field of robotics stands at a critical juncture. The complexity and unpredictability of the real world present a formidable challenge: a robot trained in a controlled environment, even with sophisticated simulation or rich offline datasets, may falter when faced with the myriad, dynamic conditions outside the lab. The transition from static, predictable settings to the nuanced variability of the physical world necessitates robots that are not merely reactive but are endowed with the capacity for proactive adaptation and self-improvement.
This special issue, titled "Robot Learning to Adapt and Improve in the Real World," aims to cast a spotlight on the latest research that empowers robots with such capabilities. We seek to explore cutting-edge methodologies that facilitate continuous adaptation to new tasks and environments, enhance generalization to previously unseen scenarios, and refine the proficiency of existing robotic skills.
We welcome submissions that delve into the theoretical underpinnings, algorithmic advancements, and empirical evaluations of robot learning systems.
We invite submissions of original research articles, reviews, and potentially high-impact short communications that address these topics. Contributions should not only present novel approaches but also substantiate their applicability in real-world scenarios through rigorous experimentation or substantial empirical evidence.

Topics of interest include, but are not limited to:

  1. Online and incremental learning algorithms for robots
  2. Transfer and multi-task learning in robotics
  3. Reinforcement learning and deep learning in physical robot systems
  4. Human-robot interaction and collaboration for adaptive learning
  5. Simulation-to-reality transfer (Sim2Real) techniques
  6. Methods for robustness and generalization in robotic systems
  7. Self-supervised and unsupervised learning paradigms in robotics
  8. Lifelong learning frameworks for autonomous robotic agents
Lead Guest Editor
  • Harry Zhang

    Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, United States

Guest Editors
  • Huang Huang

    Berkeley AI Research Lab, University of California, Berkeley, United States

  • Heng Yu

    Robotics Institute, Carnegie Mellon University, Pittsburgh, United States

  • David Jin

    Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, United States

  • Lawrence Chen

    Department of Industrial Engineering, University of California, Berkeley, United States

  • Wenxuan Zhou

    Robotics Institute, Carnegie Mellon University, Pittsburgh, United States

  • Brian Okorn

    Boston Dynamics, Cambridge, United States

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